Effect of gravity shear ratio on governing failure mode of reinforced concrete slab-column connections

Effect of gravity shear ratio on governing failure mode of reinforced concrete slab-column connections | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effect of gravity shear ratio on governing failure mode of reinforced concrete slab-column connections Navid Jafarian, Davood Mostofinejad, Ali Raji This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2291906/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Reinforced concrete (RC) flat slabs are widely being applied to almost every building structure due to their distinct advantages. Nevertheless, there has not yet been a definitive method to precisely foresee the governing slab failure modes. This research was targeted at predicting the main failure mode of RC slab-column connections subjected to unbalanced moment and various vertical shear forces, for the first time. Thus, the failure modes of the connections were deviated by comparing the unbalanced moment capacity at punching shear failure controlled by the codes and unbalanced moment strength at the flexural mechanism checked by the yield line theory (YLT). The procedure was validated by the results of experimental tests carried out at authentic research in the literature. Afterward, 200 case studies were done on the connections under moment transfer at 20%, 40%, and 60% of gravity shear ratios (GSRs), regarding the alteration of flexural reinforcement ratio from zero to 3.0%. Openings and shear strengthening were looked into in the case studies as two highly effective parameters for the governing failure mode. The intersection of unbalanced moment capacities owing to punching shear and flexural collapses with respect to the longitudinal reinforcement ratio indicated the coordinate of boundary point (BP) between possible failure modes. It was proved that the GSR rise and the existence of opening, lead to a decline in the coordinate of BP notwithstanding, the effect of shear strengthening was in reverse. Slab-column connection Unbalanced moment resistance Gravity shear ratio Failure mode Code provisions Yield line theory Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction Over the past several decades, there has been substantial industrial progress on the RC flat slabs in the building industry. RC flat roofs as two-way structural systems are being applied to almost every superstructure, targeting bearing light floor loads and moving them directly to the columns without using any beams or girders. The general behavior of slabs, by and large, relies heavily on the percentage of flexural reinforcement. Slabs can be structurally classified as light, medium and high flexural reinforcement ratios, basically leading to three different sorts of failure [1-5]. Three failure modes of the slabs are brittle punching shear, combined flexural-shear, and flexure-induced punching introduced as follows [6]: a . Brittle punching shear is generally known as the failure mode of the slabs with a high flexural reinforcement ratio. The obvious feature of this type of failure mode is that the ultimate rupture arises with no clear sign before the longitudinal reinforcement yielding. Meanwhile, both the tensile crack propagation and the plastic deformation are at the absolute minimum amount. b . The slabs with a medium flexural reinforcement ratio illustrate a combined flexural-shear mode. This failure behavior acts in such a manner that the part of longitudinal reinforcement yields. Besides, the yield lines move along the diagonal directions according to the level of flexural reinforcement ratio; just as the amount of longitudinal reinforcement ratio decreases, so the yield-line pattern spreads rapidly to the full mechanism. c . Flexure-induced punching mode appears in the slabs with a low level of flexural reinforcement ratio, broadly leading to a ductile behavior through the plastic plateau region. In this type of failure behavior, the widespread yielding of longitudinal reinforcement causes the slabs to fail in the full mechanism of the yield-line pattern. The important feature of this type of failure mode is the occurrence of large plastic deflection in advance of the brittle punching failure while tensile cracks suddenly come into view along the depth of the slabs. The slabs have both advantages and disadvantages, but the benefits outweigh the drawbacks nonetheless. It is widely believed in the virtues of employing RC flat slab systems as follows: Employing less formwork; Saving in overall building height; Design flexibility of space layout; Reducing construction cost and time; Ease of reinforcing placement, mechanical and electrical services installation. Predicting the value of the ultimate load-carrying strength of RC flat slabs puts obstacles in the way of researchers trying to develop theoretical methods. This issue has sparked a storm of controversy surrounding the equations of shear and flexural cracking loads of the slabs. The estimation of moments and stresses manifested in the slabs was come up with a justifiable theory in 1921 [8]. The basis of this theory was provided by the finite difference method (FDM); this method was applied to the slabs under different load cases considering the profound stiffness effect of columns [7]. In 1925, the slab experimental tests were conducted in the form of footings [10] at the University of Illinois at Urbana-Champaign of the US, aiming to estimate the punching shear resistance. Nevertheless, the cross-section of tested footings was broad in comparison to the supports of mushroom slabs at that time. Consequently, further experimental tests were needed to precisely calculate the punching shear strength. Hence, some slabs were experimentally tested to plug the huge gaps in punching shear formulas [4]. Then, the significant influence of some important parameters such as support conditions, compressive strength of concrete, shear, and flexural reinforcement was proved on the punching shear capacity of the slabs [7]. In 1961, a large number of tests were experimentally performed on the slab-column connections and the results were the basis of recommendations in ACI 318 to determine the unbalanced moment strength due to the punching shear mechanism [11]. Experimental [6,12-16] and numerical [17-22] findings have made a major technological breakthrough in the subject of engineering applications. Over the last few decades, RC slab-column connections are becoming the focus of many pieces of research, regarded as a top priority. By contrast, there has always been a great deal of predicting the connection’s main failure modes. Hence, this research shed the light on projecting the failure modes of the connections exerted to the unbalanced moment with different vertical shear loads, by comparison of the maximum and minimum unbalanced moments, respectively, transferred by shear and flexure. The highest value of unbalanced moment capacity owing to the punching shear mechanism was derived by code provisions, e.g., ACI 318-19 [23], EC 2 [24], and MC2010 [25]. By contrast, the lowest value of unbalanced moment strength on account of flexural failure was extracted by the yield line theory (YLT). Respecting the tensile reinforcement ratio, the intersection of the results of codes and YLT was known as the boundary point of failure modes at which the flexural-shear collapse happened. The scheme of this research can be described in the following steps. First, the common failure modes of RC flat slabs were fully explained, including punching shear and flexural behaviors. Second, the governing formulae of the codes and YLT were represented, respectively, to acquire the unbalanced moment strength on account of punching shear and flexural failures. Then, the accuracy and safety of code provisions and predicted failure modes were compared with the results of the experimental database in the literature. Finally, 200 case studies were taken on the connections under moment transfer at the GSRs of 20%, 40%, and 60%, with respect to the flexural reinforcement ratio (0.0%-3.0%). Opening and shear strengthening were taken as distinguishing characteristics of the governing failure mode. The geometry of all specimens was the same. The first connection was considered as the control specimen without any modifications while the second one included four openings positioned close and parallel to the column. Two other specimens were strengthened with vertical reinforcing elements to enhance the equivalent shear stress regarding punching capacity by 70%. 2. Failure Mechanisms Of Rc Flat Slabs 2.1. Punching shear failure Punching shear failure is a kind of dismal collapse observed mainly in RC two-way slabs as a result of the concentration of shear and bending stresses. This failure mode is a destructive mechanism occurred in the connection between the slab and support point as a column. In fact, this fracture is a process by which a part of the slab punches into the column and develops into the shortened cone shape. This brittle failure is associated with a razor-sharp reduction in the shear load-carrying capacity [ 26 , 27 ]. On the whole, punching shear collapse is of critical importance to the design procedure of the slab structures. Therefore, it is absolutely vital to know the effective characteristics of the punching load-carrying capacity. Several explanations have been offered for the issue of punching shear calculation of RC flat slabs by observing the final results of widespread experimental studies. Most studies confirmed that the punching shear strength of the slabs is mainly influenced by the strength of concrete, tensile longitudinal reinforcement ratio, geometry and dimensions of the column, size effect, opening, shear reinforcement, and compression steel. These parameters are a set of fixed limits controlling the punching shear behavior. Flexural reinforcement ratio, opening, shear reinforcement, and compressive steel that were the focus of this study, are characterized based on the available research literature, standards, and codes as follows: 2.1.1. Flexural reinforcement ratio Numerous studies have shown the effect of the flexural reinforcement ratio on the punching shear strength of the slabs. It was observed that the punching shear capacity of the slabs increased by approximately 95% as the tensile longitudinal reinforcement ratio raised from 0.8–2.1% [ 32 ]. This enhancement was about 63% when the aforementioned ratio varied from 0.6–2.4% [ 29 ]. According to extensive research, it was observed that the punching shear resistance is largely a function of the fourth root of the flexural reinforcement ratio [ 33 ]. Nevertheless, some studies confirmed that the shear load-carrying capacity of the slabs is directly proportional to the cube root of the flexural reinforcement ratio [ 34 , 35 ]; this theoretical concept was incorporated in EC 2 [ 24 ]. In addition, the punching shear strength presented in MC2010 [ 25 ] is related to the flexural reinforcement ratio by the design value of the resistant moment. Despite EC 2 [ 24 ] and MC2010 [ 25 ], ACI 318 [ 23 ] has never considered the importance of the flexural reinforcement ratio in the punching shear capacity. 2.1.2. Opening Opening as a serious defect leads to a significant decrease in the punching shear strength of RC flat slabs. As the openings are located close to the columns, punching shear designing of the slabs faces two tremendous challenges; the reduction of a part of concrete assigned to carry the punching shear stresses, and the interruption of flexural reinforcing bars [ 19 ]. Due to the harmful destructive effects of openings on the punching shear capacity [ 40 – 43 ], it is absolutely vital to accurately compute the punching shear resistance of the slabs with openings. Pioneering research has been done a much to advance the knowledge of openings in the slabs. For this reason, code provisions, without exception, are being updated in accordance with the research findings corresponding to the slabs with the opening. The damaging effect of opening on the control perimeter of the slabs is illustrated in Fig. 1. The length of the control perimeter is taken ineffective, enclosed by unbent lines sticking out from the center of the column and reaching the outer circumference of the opening if the space between the outside boundary of the column and opening is less than a specific value. The corresponding value is \(4h\) , \(6d\) , and \(5d\) , respectively, in accordance with ACI 318 − 19 [ 23 ], EC 2 [ 24 ], and MC2010 [ 35 ]. This value was taken as \(10h\) in the previous versions of ACI provisions. 2.1.3. Shear reinforcement To improve the punching shear behavior of slabs, down-stand beams, column capitals, and drop-panel columns were used before the appearance of shear reinforcement. It is certainly true that employing shear reinforcing elements is definitely an extremely efficient way to increase the punching shear load capacity of the slabs [ 12 – 14 , 16 , 44 – 46 ] and enhance the corresponding lateral drift capacity and ductility [ 45 ]. On the other hand, it decreases the slab thickness and improves the punching shear characteristics. Because of this, researchers have too deeply probed into the influence of various kinds of shear reinforcing elements [ 12 – 14 , 16 , 20 , 44 – 47 ] on the punching shear strength. As the slab is strengthened by vertical reinforcing elements, it is important to monitor the concrete shear stress in an outermost control perimeter located at a distance from where the shear reinforcement is interrupted, and at a distance from the column. The outer and inner critical sections of the slab are illustrated in Fig. 1. 2.1.4. Compression steel Compressive steel is a catch-all term occasionally known as the effective punching shear parameter of the slabs, but it is becoming the focus of much research. Employing compression steel has a wide range of benefits as increasing bending load capacity and improving ductility [ 48 ]. Nonetheless, this effective parameter is completely ignored by code guidelines except in MC2010 [ 25 ]. The influence of compression steel on the shear load-carrying capacity is closely associated with the design average flexural strength per unit length, as for MC 2010 [ 25 ]. The nominal moment of resistance of a rectangular cross-section with the compression steel is estimated by working out the governing equilibrium equation of the stress-strain diagram, indicated in Fig. 2; this formulation is heavily dependent upon the yielding of steel reinforcing bars, resulting in four various expressions mentioned in Table 1 [ 48 ]. Table 1 Modes of equations for moment of resistance with effect of compression steel [ 48 ]. Modes Steel strain Steel stress Reinforcement ratio Steel yielding 1 \({\varepsilon _s}>{\varepsilon _y}\) and \({\varepsilon ^{\prime}_s}>{\varepsilon ^{\prime}_y}\) \({f_s}={f_y}\) and \({f^{\prime}_s}={f^{\prime}_y}\) \(\rho {\bar {\rho }_{\hbox{min} }}\) Tension and compression 2 \({\varepsilon _s}>{\varepsilon _y}\) and \({\varepsilon ^{\prime}_s}<{\varepsilon ^{\prime}_y}\) \({f_s}={f_y}\) and \({f^{\prime}_s}~<{f^{\prime}_y}\) \(\rho <{\bar {\rho }_b}\) and \(\rho <{\bar {\rho }_{\hbox{min} }}\) Just tension 3 \({\varepsilon _s}{\varepsilon ^{\prime}_y}\) \({f_s}~{\bar {\rho }_b}\) and \(\rho >{\bar {\rho }_{\hbox{min} }}\) Just compression 4 \({\varepsilon _s}<{\varepsilon _y}\) and \({\varepsilon ^{\prime}_s}~<~{\varepsilon ^{\prime}_y}\) \({f_s}~<{f_y}\) and \({f^{\prime}_s}~{\bar {\rho }_b}\) and \(\rho <{\bar {\rho }_{\hbox{min} }}\) None 2.2. Flexural failure mechanism Generally, the bending performance of slabs is turned into a ductile behavior, experiencing large deflections under the high external loads. Additionally, numerous flexural cracks are created and distributed on the tensile side of the slabs as the warning signs before the collapse state [ 49 ]. Researchers have widely developed theories to figure out the flexural strength of the slabs. In 1953, yield line theory (YLT) as an upper bound method was introduced by Hognestad [ 50 ], employed in the limit analysis of the slabs. In this method, a yield line pattern is hypothesized as the collapse mechanism to estimate the flexural load-carrying capacity. The yield line pattern as the plastic hinge lines is predicted by the boundary conditions of the slabs. The internal moment of resistance occurs in the plastic hinge lines at the ultimate state of the slab section. The ultimate load of flexure is calculated by employing two different reliable methods; the principle of virtual work, and the equations of equilibrium. Besides, all the possible types of yield line patterns must be analyzed to check that the flexural resistance is not overestimated, as has the minimum value of all the possible scenarios [ 49 ]. 3. Equations Of Code Guidelines And Yield Line Theory 3.1. Formulas of Unbalanced moment resistance at punching shear failure in codes In the code provisions, formulae of unbalanced moment transferred by the eccentricity of shear due to the slabs, are based on extensive experimental tests. Furthermore, updating the corresponding formulas is needed for providing a confident prediction of the moment resistance more than ever. In this section, the important formulae of the code provisions are fully briefed to compute the unbalanced moment capacity owing to punching shear collapse. 3.1.1. ACI 318 − 19 [ 23 ] ACI 318 − 19 [ 23 ] employs an eccentric shear model to calculate the unbalanced moment resistance causing failure by punching shear. In this model, it is assumed that a linear shear stress distribution acts along the control perimeter of the slab-column connection for a given shear force and a fraction of an unbalanced moment. ACI 318 − 19 [ 23 ] also states that the unbalanced moment resistance on account of the punching shear mechanism should be smaller than the flexural moment resisted by the hogging and sagging reinforcement over the effective width, \(c+3h\) . Thus, the maximum unbalanced moment at punching shear failure is governed by [ 23 ]: $${M_n}=\hbox{min} \left\{ {\left( {{V_n} - {V_u}} \right)\frac{{{J_c}}}{{{\gamma _v}{b_0}d{c_c}}},\frac{{({m_{Rd}}+{{m^{\prime}}_{Rd}})(c+3h)}}{{1 - {\gamma _v}}}} \right\}$$ 1 where, \({V_n}\) is obtained by Eq. ( 2 ) in the absence of shear reinforcement, \({J_c}\) and \({\gamma _v}\) are respectively proportional to \(c\left[ {{b_1}d\left( {{b_1}+3{b_2}} \right)+{d^3}} \right]/3\) and \(1 - {\left( {1+2\sqrt {{b_1}/{b_2}} /3} \right)^{ - 1}}\) [ 23 ]. $${V_n}={V_c}=\hbox{min} \left( {\frac{1}{3}{\lambda _s}\lambda \sqrt {{{f^{\prime}}_c}} {b_0}d,\left( {1+\frac{2}{\beta }} \right){\lambda _s}\lambda \frac{{\sqrt {{{f^{\prime}}_c}} {b_0}d}}{6},\left( {\frac{{{\alpha _s}d}}{{{b_0}}}+2} \right){\lambda _s}\lambda \frac{{\sqrt {{{f^{\prime}}_c}} {b_0}d}}{{12}}} \right)$$ 2 in which, \({\lambda _s}\) is equated to \(\sqrt {2/(1+0.004d)}\) , \(\lambda\) has the value of 1.0 for a normal-weight concrete, and \({\alpha _s}\) holds the value of 40 for interior columns [ 23 ]. 3.1.2. EC 2 [ 24 ] Similar to ACI 318 − 19 [ 23 ], EC 2 [ 24 ] assesses the maximum unbalanced moment resistance due to punching shear failure by exploiting the eccentric shear transfer mechanism according to Eq. ( 3 ). But, it presumes that the shear stress distribution is uniform instead of linear. $${M_{Rd}}=\left( {{V_{Rd}} - {V_{Ed}}} \right)\frac{{{W_1}}}{{{u_1}\kappa }}$$ 3 where, \({V_{Rd}}\) is associated with Eq. ( 4 ) as the nominal punching shear capacity is only attributed to concrete, \({W_1}\) is in relation to \(c{}_{1}^{2}/2+{c_1}{c_2}+4{c_2}d+16{d^2}+2\pi d{c_1}\) , and is considered 0.6 for the square loaded area [ 24 ]. $${V_{Rd}}={V_{Rd,c}}={C_{Rd,c}}k{(100{\rho _l}{f_{ck}})^{1/3}}{u_1}d \geqslant {V_{\hbox{min} }}$$ 4 in which, \({C_{Rd,c}}\) and \({V_{\hbox{min} }}\) are obtained by \(0.18/{\gamma _c}\) and \(0.035{k^{3/2}}{f_{ck}}^{{1/2}}\) , respectively; and \({\rho _l}\) are respectively taken as \(1+\sqrt {200/d}\) and \(\sqrt {{\rho _{ly}}{\rho _{lz}}}\) limited to 2.0 and 0.02, respectively; is, moreover, defined by \(({d_y}+{d_z})/2\) [ 24 ]. 3.1.3. MC2010 [ 25 ] Other than ACI 318 − 19 [ 23 ] and EC 2 [ 24 ], MC2010 [ 25 ] utilizes a failure criterion based on the critical shear crack theory (CSCT) as defined in Eq. ( 5 ), to put a figure on the unbalanced moment strength due to punching shear collapse. $${V_{Rd}}=\hbox{min} \left( {\frac{1}{{1.5+0.9{k_{dg}}\psi d}},0.6} \right)\frac{{\sqrt {{f_{ck}}} }}{{{\gamma _c}}}{b_0}{d_v} \geqslant {V_{Ed}}$$ 5 in which, \({k_{dg}}\) is proportioned to \(32/\left( {16+{d_g}} \right)\) with the lower limit of 0.75, \({b_0}\) is determined by \({k_e}{b_1}\) , and \(\psi\) in the second level of approximation as the redistribution of bending moment is significant, may be evaluated by a parabola equation as following [ 25 ]: $$\psi =1.5\frac{{{r_s}}}{d}\frac{{{f_{yd}}}}{{{E_s}}}{\left( {\frac{{{m_{sd}}}}{{{m_{Rd}}}}} \right)^{1.5}}$$ 6 Besides, \({k_e}\) and \({m_{sd}}\) are respectively computed by \(1/\left( {1+{e_u}/{b_u}} \right)\) and \({V_{Ed}}\left( {1/8+0.5{e_u}/{b_s}} \right)\) , where \({e_u}={M_{Ed}}/{V_{Ed}}\) ; \({m_{Rd}}\) is analyzed by the notes mentioned in Section 2.1.4 ; \({f_{yd}}\) is also equal to \({f_{yk}}/{\gamma _c}\) [ 25 ]. 3.2. Formulas of unbalanced bending moment in yield line theory A yield line pattern was proposed in Fig. 3 by Dilger and Cao [ 51 ] for the slab-column connections subjected to substantial lateral load and significant gravity force. Based on the principle of virtual work, the unbalanced moment resistance owing to flexural collapse is associated with Eq. (7a) for the slab without opening. It is governed by Eq. (7b) for the slab with four square openings placed adjacent and parallel to the column. It is also assumed that \({\ell _{Op}}\) as the opening size is not bigger than the column size. The openings’ influence over the unbalanced moment strength due to flexural mechanism was considered in a way that the area of openings is removed from the yield line pattern of the slab without any openings. 4. Method Analysis The proficiency in code guidelines was probed by comparing the unbalanced moment capacity owing to punching shear collapse with the experimental test results of 25 RC slab-column connections collected as a literature database. The main criterion for selecting the database was a high degree of reliability of the experimental results based on the literature sources of genuine journals. The properties of the experimental database were depicted in Table 2 . Table 2 Specifications of literature experimental specimens. Reference Specimen (mm) (mm) \({r_s}\) (mm) \({f^{\prime}_c}\) (MPa) \(\rho\) (%) \(\rho ^{\prime}\) (%) \({f_y}\) (MPa) \({f^{\prime}_y}\) (MPa) \({V_u}\) (kN) Robertson et al. [ 52 ] 1C 100 250 1320 35.4 0.70 0.42 441 441 39.90 Robertson and Johnson [ 53 ] ND1C 100 254 1320 29.6 0.53 0.54 441 441 60.80 ND4LL 100 254 1320 32.3 0.53 0.54 441 441 93.40 ND5XL 100 254 1320 24.1 0.53 0.54 441 441 104.80 ND6HR 100 254 1320 26.3 0.93 0.93 441 441 67.20 ND7LR 100 254 1320 18.8 0.39 0.37 441 441 68.50 ND8BU 100 254 1320 39.2 0.93 0.81 441 441 65.30 Bu & Polak [ 45 ] SW5 * 89 200 825 46.0 1.20 0.74 476 476 160.00 SW6 * 89 200 825 52.0 1.20 0.74 476 476 160.00 Park et al. [ 54 ] RC-A 114 300 1350 22.5 1.06 0.79 430 430 134.04 RC-B 114 300 1350 38.7 1.06 0.79 430 430 158.60 Almeida et al. [ 55 ] C-30 118 250 913 66.50 0.96 0.67 535 526 131.30 C-40 119 250 913 53.10 0.96 0.67 535 526 167.40 C-50 118 250 913 52.40 0.96 0.67 535 526 203.40 Drakatos et al. [ 56 ] PD1 204 390 1504 37.90 0.79 0.35 559 559 253.00 PD2 198 390 1504 36.90 0.81 0.34 558 558 288.00 PD3 198 390 1504 34.90 0.81 0.34 558 558 288.00 PD4 201 390 1504 39.00 0.80 0.35 507 507 376.00 PD5 198 390 1504 37.50 0.81 0.35 507 507 195.00 PD6 199 390 1504 38.30 0.81 0.30 507 507 192.00 PD8 198 390 1504 32.70 0.81 0.29 575 575 152.00 PD10 197 390 1504 32.30 1.60 0.72 593 593 301.00 PD11 196 390 1504 33.10 1.60 0.71 593 593 299.00 PD12 195 390 1504 35.50 1.61 0.72 546 546 205.00 PD13 196 390 1504 36.50 1.61 0.72 546 546 201.00 * SW5 and SW6 have two square openings with a dimension of 150 mm, located parallel and adjacent to the column face For the literature database, Table 3 illustrates the values of unbalanced moment resistance on account of punching shear failure predicted by the code guidelines and those presented by experimental tests, as does their proportion. Average (AVG) and coefficient of variant (CV), were employed as statistical parameters to investigate the empirical methods regarding safety and precision, respectively, aimed at comparing the results of codes and experimental studies. The average value of the ratio between \({M_{punch}}\) and \({M_{\exp }}\) may show the degree of safety. Code underestimates the unbalanced moment resistance at punching shear collapse if the ratio is less than 1.0. It yields an optimized level of safety for the outputs as the ratio is in the period of 1.0 and 1.1. Besides, it presents satisfactory safety responses as the ratio is between 1.1 and 1.3. The conservative estimation is also obtained for the ratio with the bigger value of 1.3. To probe the precision of unbalanced moment strength due to punching shear failure calculated by the codes, CV was calculated for the proportion of \({M_{punch}}\) to \({M_{\exp }}\) . This parameter is obeyed by the ratio of the standard deviation to the average, leading to determining the dispersion of frequency distribution. As the value of CV is lower, the measure of dispersion decreases, and therefore, the response value tends to be more accurate. Table 3 expresses AVG and CV of the unbalanced moment resistance owing to the punching shear mechanism according to ACI 318 − 19 [ 23 ], EC 2 [ 24 ], and MC2010 [ 25 ] without and with the effect of compression steel. The AVG of \({M_{punch}}/{M_{\exp }}\) for those guidelines was respectively 0.98, 1.19, 0.83, and 0.84; moreover, the CV of those was 0.14, 0.21, 0.10, and 0.10 in the order given. As a consequence, the results of ACI 318 − 19 [ 23 ] and MC2010 [ 25 ] were underestimated, but those of EC 2 [ 24 ] had a satisfactory level of safety. MC2010 [ 25 ] estimated the unbalanced moment transferred by shear stress at the same level of precision and was more accurate than ACI 318 − 19 [ 23 ] and EC 2 [ 24 ]. However, ACI 318 − 19 [ 23 ] yields more exact answers than EC 2 [ 24 ]. Table 3 Comparison of literature experimental results with predictions of code guidelines. Reference Specimen \({M_{punch}}\) (kN.m) \({M_{\exp }}\) (kN.m) \(\frac{{{M_{punch}}}}{{{M_{\exp }}}}\) ACI [ 23 ] EC [ 24 ] MC [ 25 ] MC [ 25 ]-CS ACI [ 23 ] EC [ 24 ] MC [ 25 ] MC [ 25 ]-CS Robertson et al. [ 52 ] 1C 70.76 74.30 62.74 62.93 51.2 1.38 1.45 1.23 1.23 Robertson and Johnson [ 53 ] ND1C 58.97 54.70 41.49 41.70 42.3 1.39 1.29 0.98 0.99 ND4LL 52.61 44.62 28.71 29.04 44.4 1.18 1.00 0.65 0.65 ND5XL 38.19 32.84 18.23 18.27 32.5 1.18 1.01 0.56 0.56 ND6HR 52.61 64.69 54.87 55.09 58.5 0.90 1.11 0.94 0.94 ND7LR 40.97 34.36 21.85 21.93 30.0 1.37 1.15 0.73 0.73 ND8BU 69.27 78.24 66.77 66.71 58.7 1.18 1.33 1.14 1.14 Bu & Polak [ 45 ] SW5 * 17.90 23.78 19.62 21.29 77.9 0.23 0.31 0.25 0.27 SW6 * 0.00 0.00 1.82 2.60 52.3 0.00 0.00 0.03 0.05 Park et al. [ 54 ] RC-A 57.82 77.82 56.94 57.32 64.5 0.90 1.21 0.88 0.89 RC-B 81.86 94.12 67.94 68.00 70.5 1.16 1.34 0.96 0.96 Almeida et al. [ 55 ] C-30 107.21 120.77 95.03 98.24 121.6 0.88 0.99 0.78 0.81 C-40 81.79 95.78 74.10 75.99 102.8 0.80 0.93 0.72 0.74 C-50 67.85 77.37 58.02 60.14 74.8 0.91 1.03 0.78 0.80 Drakatos et al. [ 56 ] PD1 377.95 472.46 297.01 298.71 525.0 0.72 0.90 0.57 0.57 PD2 330.03 412.55 254.10 257.15 196.0 1.68 2.10 1.30 1.31 PD3 316.97 401.36 246.99 249.48 200.0 1.58 2.01 1.23 1.25 PD4 310.70 379.52 211.01 215.19 527.0 0.59 0.72 0.40 0.41 PD5 380.74 478.99 307.75 312.60 462.0 0.82 1.04 0.67 0.68 PD6 391.34 491.93 316.01 320.29 372.0 1.05 1.32 0.85 0.86 PD8 370.70 480.90 323.25 324.69 384.0 0.97 1.25 0.84 0.85 PD10 289.33 517.91 351.80 353.80 290.0 1.00 1.79 1.21 1.22 PD11 289.33 517.91 351.80 353.80 286.0 1.01 1.81 1.23 1.24 PD12 351.35 591.57 418.59 418.65 469.0 0.75 1.26 0.89 0.89 PD13 363.55 609.09 431.37 431.40 410.0 0.89 1.49 1.05 1.05 AVG 0.98 1.19 0.83 0.84 CV 0.14 0.21 0.10 0.10 Table 4 indicates the ratio between maximum unbalanced moment resistance on account of punching shear failure gained from the codes and minimum unbalanced moment resistance at flexural collapse obtained by the YLT. The corresponding proportion was utilized to deviate the main failure mode of slab-column connections without and with the effect of compression steel. If \({M_{punch,\hbox{max} }}/{M_{flex}}\) is lower or greater than 1.0, the failure mode is probably flexural or punching shear, respectively. Furthermore, the failure mode may be governed by shear-flexural as \({M_{punch,\hbox{max} }}/{M_{flex}}\) is equal to 1.0. As shown in Table 4 , the failure modes predicted by the proposed method, were 92% compatible with those reported by the experimental database. As a result, this approach is capable to project the governing failure mode of the connections for research purposes and structural designing. Table 4 Experimental and predicted failure modes. Reference Specimen \({M_{flex}}\) (kN.m) \({M_{punch,\hbox{max} }}\) (kN.m) \(\frac{{{M_{punch,\hbox{max} }}}}{{{M_{flex}}}}\) Failure mode YLT YLT-CS YLT YLT-CS P EXP Robertson et al. [ 52 ] 1C 110.81 111.39 74.30 0.67 0.67 Shear Shear Robertson and Johnson [ 53 ] ND1C 86.05 86.63 58.97 0.69 0.68 Shear Shear ND4LL 82.29 83.17 52.61 0.64 0.63 Shear Shear ND5XL 79.41 79.53 38.19 0.48 0.48 Shear Shear ND6HR 148.16 149.09 64.69 0.44 0.43 Shear Shear ND7LR 59.77 59.98 40.97 0.69 0.68 Shear Shear ND8BU 153.63 153.41 78.24 0.51 0.51 Shear Shear Bu & Polak [ 45 ] SW5 * 120.5 128.32 23.78 0.20 0.19 Shear Shear SW6 * 84.31 91.30 2.60 0.03 0.03 Shear Shear Park et al. [ 54 ] RC-A 239.11 240.92 77.82 0.33 0.32 Shear Shear RC-B 250.15 250.40 94.12 0.38 0.38 Shear Shear Almeida et al. [ 55 ] C-30 266.27 279.89 120.77 0.45 0.43 Shear Shear C-40 263.11 271.70 95.78 0.36 0.35 Shear Shear C-50 253.63 263.37 77.37 0.31 0.29 Shear Shear Drakatos et al. [ 56 ] PD1 1056.42 1065.08 472.46 0.45 0.44 Shear Shear-Flex PD2 1005.94 1021.76 412.55 0.41 0.40 Shear Shear-Flex PD3 1001.2 1014.28 401.36 0.40 0.40 Shear Shear PD4 920.44 940.11 379.52 0.41 0.40 Shear Shear PD5 934.96 958.02 478.99 0.51 0.50 Shear Shear PD6 946.82 967.03 491.93 0.52 0.51 Shear Shear PD8 1050.93 1058.82 480.90 0.46 0.45 Shear Shear PD10 1908.02 1925.50 517.91 0.27 0.27 Shear Shear PD11 1908.02 1925.50 517.91 0.27 0.27 Shear Shear PD12 1804.14 1804.68 591.57 0.33 0.33 Shear Shear PD13 1832.62 1832.90 609.09 0.33 0.33 Shear Shear 5. Case Studies On Rc Flat-slab Systems In this section, the governing failure mode of 200 case studies was gotten for the slab-column connections concerning the change of longitudinal reinforcing steel from 0.0 to 3.0%. It exploited the proposed prediction-failure method mentioned in Section 4 . As displayed in Fig. 4, one slab served as the control specimen known as CS, but the other contained four square openings placed adjacent and parallel to the column face, named SO. Two other specimens were strengthened in a way that the equivalent stress relating to shear load-carrying capacity escalates 70% in comparison to those without any shear reinforcing element. Both had the same geometry as CS and SO, called respectively S70 and SO70. All of the slabs were square with the dimensions of 2000 mm while the length of clear span measured center-to-center of the supports was 1800 mm. The slabs’ thickness was 150 mm whereas their effective depth was taken as 120 mm. The size of the openings was equal to 110 mm according to the explanatory note recommended by ACI 318 − 19 [ 23 ]; at the intersection of two column strips, the maximum value of opening size is limited to one eight the width of the column strip in either span. The lateral loading and vertical shear force were monotonically subjected to the top of the square column whereupon the slab-column connection collapsed under the unbalanced moment transferred by the eccentricity of shear. The column had a size of 300 \(\times\) 300 mm. 28-day characteristic compressive cylinder strength of concrete, yield strength, and modulus of elasticity of flexural reinforcement were 30 MPa, 420 MPa, and 200,000 MPa, in the order given. The vertical shear load was considered variable in all the case studies since it highly affects the main failure mode of the connections. The gravity shear force was 0.2, 0.4, and 0.6 of the nominal punching shear strength computed by ACI 318 − 19 [ 23 ]. Thus, the ratio of the gravity shear load to the nominal punching shear capacity, described as the gravity shear ratio, had the percent values of 20, 40, and 60. Figures 5(a), (b), (c), and (d) depict the unbalanced moment strength at punching shear collapse of CS, SO, S70, and SO70, respectively, achieved by ACI 318 − 19 [ 23 ] while the flexural reinforcement ratio differs. The vertical axis covers the unbalanced moment transferred by shear stresses with the unchanged value at different percent amounts of GSRs as 20, 40, and 60. The horizontal axis specifies the ratio of longitudinal reinforcing steel between 0.0% and 3.0%. The unbalanced moment capacity due to the punching shear mechanism remained consistent for 0.0%-3.0% of the ratio of flexural reinforcement. The constant values of CS, SO, S70, and SO70 at the GSR of 20%, were respectively 105.16 kN, 37.15 kN, 197.18 kN, and 75.17 kN. At the GSRs of 40%, these values were 78.87 kN, 20.01 kN, 170.89 kN, and 58.02 kN in the order given. The corresponding amounts changed to 52.58 kN, 2.86 kN, 144.60 kN, and 40.87 kN at the GSR of 60% in the same order. The damaging effect of opening on the unbalanced moment resistance owing to punching shear failure of CS with the GSRs of 20%, 40%, and 60%, were respectively 64.7%, 74.6%, and 94.6%. The corresponding values were 61.9%, 66.1%, and 71.7% for S70 in the same order. Besides, shear strengthening improved the unbalanced moment strength at the punching shear mechanism of CS as much as 87.5%, 116.7%, and 175.0%, respectively, at the GSRs of 20%, 40%, and 60%. For SO, these amounts switched to 102.3%, 190.0%, and 1330.0% in a similar order. Overall, it can be concluded that the unbalanced moment capacity due to punching shear collapse remained static regarding the change of tensile steel ratio, as is obvious in Eq. ( 1 ) of Section 3.1.1 . Figures 6(a), (b), (c), and (d) provide information about the unbalanced moment resistance owing to the punching shear mechanism of CS, SO, S70, and SO70, respectively, governed by EC 2 [ 24 ] that takes place over a wide range of longitudinal reinforcement ratio. The vertical axis indicates the unbalanced moment strength at punching shear failure between two different plateaus at various GSRs (20%, 40%, and 60%). Besides, the horizontal axis designates the major alteration of the ratio of flexural reinforcing steel from 0.0–3.0%. In the small initial interval of the ratio of longitudinal reinforcing steel, the unbalanced moment capacity due to punching shear failure stayed the same because of the inequality hinted in Eq. ( 4 ) of Section 3.1.2 ; the nominal punching shear capacity of the slab must be greater than the minimum of design shear resistance of the member without shear reinforcement. It is noteworthy to point out that the unbalanced moment resistance owing to punching shear collapse of the case studies is taken as zero if the nominal punching shear strength is less than the vertical shear force. It arose from the weakness in the shear load-carrying capacity. Other than S70, CS and SO70 experienced a period of stability in zero value at the GSR of 60%, as did SO at both GSRs of 40% and 60%. The trend was followed by a noticeable increase from the end of the first plateau to reach a particular level with the maximum value of unbalanced moment resistance on account of punching shear failure. The maximum values attributed to CS, SO, S70, and SO70 at the GSR of 20%, were respectively 174.01 kN, 50.44 kN, 319.15 kN, and 99.37 kN. At the GSR of 40%, these values were 140.69 kN, 30.99 kN, 285.83 kN, and 79.92 kN in the order presented. Besides, these amounts changed to 107.36 kN, 11.54 kN, 252.50 kN, and 60.46 kN at the GSR of 60% in the same order. The unbalanced moment delivered by shear of SO decreased 71.0%, 78.0%, and 89.3% in comparison with the control specimen, respectively, at the GSRs of 20%, 40%, and 60. The relating differences for S70 and SO70 had the following percent rise and decline values in the order proposed; 83.41, 103.16, and 135.19; 42.90, 43.20, and 43.68. From 2.0–3.0% of the flexural steel ratio, the figure was flat. This plateau is raised from the explanatory note introduced in Section 3.1.2 ; the upper limit of the ratio of tensile reinforcement is 2.0%. Generally, it can be deduced that there has been a rapid escalation in the value of unbalanced moment resistance at punching shear failure, although some indications are leading to a change in the trend. Figures 7(a), (b), (c), and (d) clearly display the major alteration of unbalanced moment strength on account of the punching shear mechanism of CS, SO, S70, and SO70, respectively, acquired by MC2010 [ 25 ] as the ratio of tensile reinforcement extremely alters. On the vertical axis, the unbalanced moment resistance due to punching shear collapse was described from a flat level in zero value to the relating maximum value, wherein on the horizontal axis, the longitudinal steel ratio differed in the range of 0.0–3.0%. The GSR varied from 20–40% and 60%. As the tensile reinforcement ratio was 3.0%, the lowest values of unbalanced moment capacity owing to the punching shear mechanism of the slabs were at the GSRs of 20%. These values were obeyed respectively by 164.70 kN, 77.35 kN, 279.99 kN, and 131.50 kN for CS, SO, S70, and SO70. Besides, the highest counterparts were at the GSR of 60% with the values of 103.00 kN, 32.69 kN, 175.11 kN, and 55.57 kN in a similar order. The difference of unbalanced moment resistance at punching shear collapse in the GSRs of 20% and 60%, assigned to the slabs without and with opening, were respectively 37.5% and 57.7%. Except for the first plateau, it can be inferred that the unbalanced moment transferred by the shear eccentricity of the slabs was on the increase in such a wise that the trend of the GSR of 20% is consistently higher than that of 40% and 60%. In Figs. 8(a), (b), (c), and (d), the influence of compression steel was expressed over the unbalanced moment capacity on account of punching shear collapse of CS, SO, S70, and SO70, respectively, calculated by MC2010 [ 25 ] in the differential ratios of longitudinal reinforcement. The vertical axis presents the unbalanced moment strength owing to punching shear failure from the zero value, and forecasts trends up the regrading maximum value at the GSRs of 20%, 40%, and 60%. The horizontal axis proposes the ratio of tensile reinforcing steel from 0.0–3.0% in addition. At the end of the period of flexural reinforcement ratio, the norm values of unbalanced moment resistance at the punching shear mechanism of CS, SO, S70, and SO70 were at the GSR of 40%, with the amount of 139.26 kN, 56.80 kN, 236.74 kN, and 96.55 kN. Additionally, the effects of opening and shear strengthening on the norm of unbalanced moment strength due to the punching shear mechanism were 59.2% and 70.0% in the order presented. All in all, the unbalanced moment transferred by the eccentricity of shear showed an upward trend, but there is an indication in the form of a steady level that this figure may be altering. By comparison of Fig. 7 with Fig. 8, the trend of unbalanced moment resistance on account of punching shear collapse of the slabs with the effect of compression steel overtook those without the corresponding influence, but both followed a similar pattern. Figures 9(a) and (b) reveal the unbalanced moment strength at the flexural collapse of the slabs without and with opening, respectively, derived by the YLT [ 51 ] for the tensile steel reinforcement. While the vertical axis demonstrates the unbalanced bending moment from zero to the ultimate value at the GSRs of 20%, 40%, and 60%, the horizontal axis shows the ratio of tensile reinforcement ratio from 0.0–3.0%. The values of unbalanced moment transferred by flexural stresses of the slabs without opening at the GSRs of 20%, 40%, and 60%, were respectively 666.30 kN, 655.25 kN, and 644.21 kN. For the slabs with opening, these values were 576.35 kN, 565.30 kN, and 554.24 kN in the same order. The gap between the unbalanced moment resistance owing to the flexural mechanism of the slabs without and with opening, were respectively 13.5%, 13.7%, and 14.0% at the GSRs of 20%, 40%, and 60%. Overall, the graph shows how the unbalanced moment transferred by flexure surged dramatically through the escalation of the ratio of flexural reinforcement. The compression steel’s influence on the unbalanced moment capacity on account of the flexural failure of the slabs without and with an opening is manifested in Figs. 10(a) and (b), respectively, according to the rise in tensile reinforcement ratio. The horizontal and vertical axes, respectively, characterize the ratio of longitudinal reinforcement in the range of 0.0–3.0%, and the unbalanced moment resistance due to flexural collapse from the zero value to its maximum value. The values of unbalanced moment capacity owing to the flexural mechanism of the slabs without and with opening were respectively 720.65 kN and 623.48 kN at the GSR of 20%. For the GSRs of 40% and 60%, the corresponding values were in the following order; 709.60 kN and 612.44 kN, and 698.56 kN and 601.39 kN. The gap values between the unbalanced moment transferred by flexural eccentricity at the GSRs of 20% and 40%, were 1.5% and 1.8% for the slabs without and with opening, respectively. The difference values were 3.1% and 3.5% regarding the GSRs of 20% and 60%, as these figures were 1.6% and 1.8% concerning the GSRs of 20% and 60% in the order mentioned. The unbalanced moment resistance at flexural failure, by and large, rose significantly throughout the growth in the flexural steel ratio. By comparison of Fig. 10 with Fig. 9, the unbalanced moment strength on account of the flexural mechanism of the slab with the influence of compressive steel outstripped that without the relating effect, whereas trends of both were similar. To predict the governing failure mode of the slabs, the maximum unbalanced moment strength on account of the punching shear mechanism governed by the code provisions [ 23 – 25 ] compared with that on account of flexural failure obeyed by the YLT [ 51 ], as mentioned in detail in Section 4 . Figures 11(a), (b), (c), and (d) present information on the maximum unbalanced moment delivered by shear of CS, SO, S70, and SO70, respectively, as well as the minimum unbalanced moment transferred by flexure, regarding the ratio of longitudinal reinforcing steel. The vertical axis represents the maximum and minimum unbalanced moment resistances at punching shear and flexural failures at the GSRs of 20%, 40%, and 60%, in the range of 0.0 to its highest value. Moreover, the horizontal axis describes the change in the ratio of tensile reinforcement between two percent values of 0.0 and 3.0. The intersection of maximum and minimum unbalanced moments transferred by shear and flexural eccentricities, respectively, according to the flexural reinforcement ratio, specifies the coordinate of an identifier point. This point is assigned to the boundary between punching shear and flexural failures. On the other hand, the flexural-shear failure mode of the slabs happens at this point. Additionally, the governing failure mode of the slabs is flexural if the longitudinal reinforcement ratio is less than the x-coordinate of BP, although punching shear failure occurs for the greater ratios of flexural reinforcing steel. The highest and lowest longitudinal reinforcement ratio of boundary points were respectively 0.88% and 0.14%, manifested in S70 and SO at the GSRs of 20% and 60%. It can be ascertained that a larger range of tensile reinforcement ratios allocates to the punching shear collapse. The coordinates of boundary points of CS, SO, S70, and SO70, at the GSRs of 20%, 40%, and 60%, were mentioned in Table 5 . Thus, the minimum unbalanced moment strengths at flexural failure compared with the unbalanced moment transferred by shear computed by ACI 318 − 19 [ 23 ], EC 2 [ 24 ], and MC2010 [ 25 ] without and with the effect of compression steel, as well as their maximum values. The openings nosedived the x and y coordinates of boundary points of CS in the amounts of 52.5% and 64.7%, respectively, at the GSR of 20%. For the GSRs of 40% and 60%, these values changed to 54.3% and 74.6%, and 51.7% and 94.6%, in the order mentioned. In addition, the shear strengthening leaped x and y coordinates of the boundary points at the GSRs of 20%, 40%, and 60%, respectively to 120.0% and 123.6%, 111.4% and 136.0%, and 113.8% and 175.0%. Moreover, Table 5 compares the boundary points attained by the codes [ 23 – 25 ] regarding the highest values. According to data shown in Table 5 , the boundary points achieved by MC2010 [ 25 ] without and with the effect of compression steel, had lower values in comparison with other counterparts, if any at all. The concerning values of EC 2 [ 24 ] were lower and greater than those of ACI 318 − 19 [ 23 ] and MC2010 [ 25 ], respectively. Besides, the boundary points calculated by ACI 318 − 19 [ 23 ] had the maximum amounts, compared with those of other code provisions. In conclusion, the governing failure modes of the slabs were governed by the results of ACI 318 − 19 [ 23 ]. Table 5 Intersection of maximum and minimum unbalanced moment resistances and its counterpart in tensile reinforcement ratio, respectively, due to code guidelines and yield line theory, at different GSRs. Specimen GSR (%) \({\rho _{bp}}\) (%) \({M_{bp}}\) (kN.m) ACI [ 23 ] EC [ 24 ] MC [ 25 ] MC [ 25 ]-CS Max ACI [ 23 ] EC [ 24 ] MC [ 25 ] MC [ 25 ]-CS Max 20 0.40 0.30 0.03 0.00 0.40 105.16 76.79 0.00 0.00 105.16 CS 40 0.35 0.13 0.06 0.06 0.35 78.87 17.00 0.00 0.00 78.87 60 0.29 0.00 0.11 0.11 0.29 52.58 0.00 0.00 0.00 52.58 20 0.19 0.07 0.03 0.00 0.19 37.15 7.44 0.00 0.00 37.15 SO 40 0.16 0.08 0.08 0.08 0.16 20.01 0.00 0.00 0.00 20.01 60 0.14 0.00 0.00 0.00 0.14 2.86 0.00 0.00 0.00 2.86 20 0.88 0.88 0.00 0.00 0.88 235.11 235.11 0.00 0.00 235.11 S70 40 0.74 0.74 0.06 0.06 0.74 186.12 186.12 0.00 0.00 186.12 60 0.62 0.58 0.11 0.11 0.62 144.60 133.78 0.00 0.00 144.60 20 0.34 0.17 0.03 0.00 0.34 75.17 32.87 0.00 0.00 75.17 SO70 40 0.32 0.11 0.08 0.08 0.32 58.02 7.27 0.00 0.00 58.02 60 0.29 0.00 0.00 0.00 0.29 40.87 0.00 0.00 0.00 40.87 6. Conclusion This study was aimed at predicting the governing failure mode of RC slab-column connections under unbalanced moments developed by different gravity shear loads, which is foremost amongst the major works of literature. On this wise, the maximum unbalanced moment capacities on account of the punching shear mechanism computed by code provisions were compared to the unbalanced moment strengths due to flexural failure by the YLT. This comparison was a proper procedure to obtain the boundary point. The efficiency and validity of this method were proved by comparing the approach’s outputs to the experimental results of RC slab-column connections collected in a literature database. Subsequently, 200 case studies were conducted on the slab-column connections respecting the change of tensile reinforcement ratio. The main conclusions of the results of this survey can be summarized as follows: According to the statistical analysis on \({M_{punch}}/{M_{\exp }}\) , EC2 gave satisfactory responses of safety. However, ACI 318 − 19 and MC2010 yield underestimated answers. Moreover, the results of MC2010 were 28. % and 52. % more accurate than those of ACI 318 − 19 and EC2, respectively. The outputs of ACI 318 − 19 were also 33.3% more precise than those of EC 2. Shear strengthening increased the range of flexural reinforcement ratio and corresponding unbalanced moment resistance at which the governing failure mode is not flexural, whereas the opening had the opposite effect. For instance, x and y coordinates of boundary points of failure modes attributed to CS, SO, and S70 at the GSR of 20%, were as follows in the order given; 0.4% and 105.16 kN; 0.19% and 37.15 kN; 0.88% and 235.11 kN. The boundary points coordinate of flexural-shear failure mode achieved by ACI 318 − 19 (i.e., 0.35% and 78.87 kN) were greater than those of EC 2 (i.e., 0.13% and 17.00 kN) and MC2010 (i.e., 0.06% and 0.00), leading to being the main boundary points. As the GSR plummeted, the boundary points of governing failure modes soared, and the x and y coordinates of SO70 respectively declined by 10.3% and 29.6% as the GSR changed from 60–40%. The effect of GSR on the unbalance moment resistance at the boundary of failure modes shot up when the case studies contained openings. The x and y coordinate of the boundary point of failure mode of CS, SO, S70, and SO70 rose respectively to 12.5% and 25.0%, 16.0% and 46.1%, 15.9% and 20.8%, and 5.9% and 22.8%, when the GSR replaced from 40–20%. Nomenclature References Stein, T., Ghali, A., and Dilger, W., “Distinction between punching and flexural failure modes of flat plates”, ACI Structural Journal , Vol. 104, 3, pp. 357-365, 2007. 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Anil, Ö., Kina, T., and Salmani, V., “Effect of opening size and location on punching shear behaviour of two-way RC slabs”, Magazine of Concrete Research , Vol. 66, 18, pp. 955-966, 2014. Mota, M.C., and Kamara, M., “Floor Openings in Two-Way Slabs”, Concrete International , Vol. 28, 7, pp. 33-36, 2006. Genikomsou, A.S., and Polak, M.A., “Effect of Openings on Punching Shear Strength of Reinforced Concrete Slabs—Finite Element Investigation”, ACI Structural Journal , Vol. 114, 5, pp. 1249-1261, 2017. El-Salakawy, E.F., Polak, M.A., and Soudki, K.A., “New strengthening technique for concrete slab-column connections”, ACI Structural Journal , Vol. 100, 3, pp. 297-304, 2003. Bu, W., and Polak, M.A., “Effect of openings and shear bolt pattern in seismic retrofit of reinforced concrete slab–column connections”, Engineering Structures , Vol. 33, 12, pp. 3329-3340, 2011. Li, R., Cho, Y.S., and Zhang, S., “Punching shear behavior of concrete flat plate slab reinforced with carbon fiber reinforced polymer rods”, Composites Part B: Engineering , Vol. 38, 5, pp. 712-719, 2007. Lowe, D., Roy, K., Das, R., Clifton, C.G., and Lim, J.B.P., “Full scale experiments on splitting behaviour of concrete slabs in steel-concrete composite beams with shear stud connection”, Structures , Vol. 23, pp. 126-138, 2020. Mostofinejad, D., Reinforced concrete structures , 3 rd Ed. (51 st reprint), Arkan Danesh Publications, Isfahan, Iran, Vol. 1, 2021; [in Persian]. Mostofinejad, D., Reinforced concrete structures , 2 nd Ed. (37 th reprint), Arkan Danesh Publications, Isfahan, Iran, Vol. 2, 2020; [in Persian]. Hognestad, E., “Yield-line theory for the ultimate flexural strength of reinforced concrete slabs”, ACI Journal Proceedings , Vol. 49, 3, pp. 637-657, 1953. Dilger, W.H., and Cao, H., “Behavior of slab-column connections under reversed cyclic loading”, Proceedings of the 5th International Colloquium on Concrete , pp. 596-606, Cairo, Egypt, 1994. Robertson, I., Kawai, T., Lee, J., and Enomoto, B., “Cyclic testing of slab-column connections with shear reinforcement”, ACI Structural Journal , Vol. 99, 5, pp. 605-613, 2002. Robertson, I., and Johnson, G., “Cyclic lateral loading of nonductile slab-column connections”, ACI Structural Journal , Vol. 103, 3, pp. 356-364, 2006. Park, H.-G., Kim, Y.-N., Song, J.-G., and Kang, S.-M., “Lattice shear reinforcement for enhancement of slab-column connections”, Journal of Structural Engineering , Vol. 138, 3, pp. 425-437, 2012. Almeida, A.F.O., Inácio, M.M.G., Lúcio, V.J.G., and Ramos, A.P., “Punching behaviour of RC flat slabs under reversed horizontal cyclic loading”, Engineering Structures , Vol. 117, pp. 204-219, 2016. Drakatos, I.-S., Muttoni, A., and Beyer, K., “Internal slab-column connections under monotonic and cyclic imposed rotations”, Engineering Structures , Vol. 123, pp. 501-516, 2016. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2291906","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":153942340,"identity":"5a372ba9-d80a-4956-955c-06276d71a823","order_by":0,"name":"Navid Jafarian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIie3RP0vDQBjH8R8ErssDriet+hauBGydfCtPCWSqIGTpIMXJLIdvwFfh0o5eKdhF9xukmKWTw02hQxHTRIqDSRwd7rvdnw938AA+3/+MMQEICEyxkN+bqoW8lERwSegPBHtSRNU1avvVAMicuVn3jh50/r7FcPSEIHO4XteSi1tE0jwnJN9e530NOdIQoYRKaoky4K4TTLBXM0klwXmxz00k2ppPpjM73hzvStLJ20gsF3dMyo5Ft3qFWl5ZIh4u7pn6Nh6EPSVDvaREchNZ6cianC9PbbTJPibTkzRNH53b1RMEdDgU1QQD7MfbVMf8ID6fz+f7pS+ccEvpTiO8xwAAAABJRU5ErkJggg==","orcid":"","institution":"Isfahan University of Technology (IUT)","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Navid","middleName":"","lastName":"Jafarian","suffix":""},{"id":153942341,"identity":"49108df3-6984-4ab6-8121-18d6dfc523c0","order_by":1,"name":"Davood Mostofinejad","email":"","orcid":"","institution":"Isfahan University of Technology (IUT)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Davood","middleName":"","lastName":"Mostofinejad","suffix":""},{"id":153942342,"identity":"82217e0d-7aa0-46ab-81cb-6b09397c1a13","order_by":2,"name":"Ali Raji","email":"","orcid":"","institution":"Isfahan University of Technology (IUT)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Raji","suffix":""}],"badges":[],"createdAt":"2022-11-19 17:29:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2291906/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2291906/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":29448595,"identity":"0cb78f8a-5f03-48ff-9987-d6902ba8125b","added_by":"auto","created_at":"2022-11-23 19:25:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":120304,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of opening and shear reinforcement on the control perimeter of RC flat slab; a) ACI 318-19 [\u003ca href=\"https://www.researchsquare.com/article/rs-2291906/admin/draft#_ENREF_23\" title=\"ACI 318-19, 2019 #76\"\u003e23\u003c/a\u003e]; b) EC 2 [\u003ca href=\"https://www.researchsquare.com/article/rs-2291906/admin/draft#_ENREF_24\" title=\"Euro Code 2, 2004 #261\"\u003e24\u003c/a\u003e]; c) MC2010 [\u003ca href=\"https://www.researchsquare.com/article/rs-2291906/admin/draft#_ENREF_25\" title=\"FIB, 2010 #262\"\u003e25\u003c/a\u003e].\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2291906/v1/16f0a1c9357901b1b9a2337a.png"},{"id":29448308,"identity":"fcff905b-e638-400e-86b7-3705745b7096","added_by":"auto","created_at":"2022-11-23 19:17:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":68001,"visible":true,"origin":"","legend":"\u003cp\u003eStrain-stress distribution in a rectangular cross-section at failure point [\u003ca href=\"https://www.researchsquare.com/article/rs-2291906/admin/draft#_ENREF_48\" title=\"Mostofinejad, 2021; [in Persian]. #411\"\u003e48\u003c/a\u003e].\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2291906/v1/06621cd1a57bd26f56cde64d.png"},{"id":29448309,"identity":"5fae21a7-5acb-497e-9a96-74ef743c177e","added_by":"auto","created_at":"2022-11-23 19:17:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":150915,"visible":true,"origin":"","legend":"\u003cp\u003eYield line pattern for a slab-column connection subjected to unbalanced moment at flexural failure in combination with significant gravity shear load [\u003ca href=\"https://www.researchsquare.com/article/rs-2291906/admin/draft#_ENREF_51\" title=\"Dilger, 1994 #310\"\u003e51\u003c/a\u003e]; a) plan; b) cross-section.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2291906/v1/4b334e0eee118415493dd106.png"},{"id":29448924,"identity":"a44b86af-128e-4a81-bca0-703e7e0345dc","added_by":"auto","created_at":"2022-11-23 19:33:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":53028,"visible":true,"origin":"","legend":"\u003cp\u003eGeometry of case studies; a) without opening; b) with opening.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-2291906/v1/9f88cac3e5e97f397628ba83.png"},{"id":29448592,"identity":"ced92fa0-2f5e-42fa-aeb4-a5e20f3917a2","added_by":"auto","created_at":"2022-11-23 19:25:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":127708,"visible":true,"origin":"","legend":"\u003cp\u003eUnbalanced moment resistance at punching shear failure with different GSRs versus reinforcement ratio for case studies according to ACI 318-19 [\u003ca href=\"https://www.researchsquare.com/article/rs-2291906/admin/draft#_ENREF_23\" title=\"ACI 318-19, 2019 #76\"\u003e23\u003c/a\u003e]; a) CS; b) SO; c) S70; d) SO70.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-2291906/v1/7d62b2d88e770cca281a0067.png"},{"id":29448310,"identity":"aa5b3cfa-4f32-41a5-881f-809110004e7f","added_by":"auto","created_at":"2022-11-23 19:17:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":136991,"visible":true,"origin":"","legend":"\u003cp\u003eUnbalanced moment resistance at punching shear failure with different GSRs versus reinforcement ratio for case studies according to EC 2 [\u003ca href=\"https://www.researchsquare.com/article/rs-2291906/admin/draft#_ENREF_24\" title=\"Euro Code 2, 2004 #261\"\u003e24\u003c/a\u003e]; a) CS; b) SO; c) S70; d) SO70.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-2291906/v1/8401c5ff75effc385871739f.png"},{"id":29448597,"identity":"6350f938-ca48-4a29-9b0c-863d35b0b0d7","added_by":"auto","created_at":"2022-11-23 19:25:55","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":147428,"visible":true,"origin":"","legend":"\u003cp\u003eUnbalanced moment resistance at punching shear failure with different GSRs \u0026nbsp;\u0026nbsp;versus reinforcement ratio for case studies according to MC 2010 [\u003ca href=\"https://www.researchsquare.com/article/rs-2291906/admin/draft#_ENREF_25\" title=\"FIB, 2010 #262\"\u003e25\u003c/a\u003e]; \u0026nbsp;\u0026nbsp;a) CS; b) SO; c) S70; d) SO70.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-2291906/v1/8625198d6da242488450bd72.png"},{"id":29448589,"identity":"bd93830d-b178-48fd-85e7-6c1b245bf939","added_by":"auto","created_at":"2022-11-23 19:25:54","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":148882,"visible":true,"origin":"","legend":"\u003cp\u003eUnbalanced moment resistance at punching shear failure with different GSRs versus reinforcement ratio for case studies according to MC 2010 [\u003ca href=\"https://www.researchsquare.com/article/rs-2291906/admin/draft#_ENREF_25\" title=\"FIB, 2010 #262\"\u003e25\u003c/a\u003e] with effect of compression steel; a) CS; b) SO; c) S70; d) SO70.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-2291906/v1/634ab77fa489ce1caf046e49.png"},{"id":29448925,"identity":"2dd60a7e-57f8-4e23-981e-16873eed1caf","added_by":"auto","created_at":"2022-11-23 19:33:54","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":85096,"visible":true,"origin":"","legend":"\u003cp\u003eUnbalanced moment resistance at flexural failure with different GSRs versus reinforcement ratio for case studies according to the yield line theory; a) without opening; b) with opening.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-2291906/v1/eae42ee3a1d67a7235cadad2.png"},{"id":29448591,"identity":"79041870-9135-4455-9d56-0ad50184fc40","added_by":"auto","created_at":"2022-11-23 19:25:54","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":95011,"visible":true,"origin":"","legend":"\u003cp\u003eUnbalanced moment resistance at flexural failure with different GSRs versus reinforcement ratio for case studies according to the yield line theory with effect of compression steel; a) without opening; b) with opening.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-2291906/v1/6b92c16f6822a4e093cd9dd6.png"},{"id":29448313,"identity":"a4b586e7-5cd7-4c4f-9f34-3c9248d9017a","added_by":"auto","created_at":"2022-11-23 19:17:54","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":260174,"visible":true,"origin":"","legend":"\u003cp\u003eComparison between maximum and minimum unbalanced moment resistances with different GSRs, respectively, at punching shear and flexural failures versus tensile reinforcement ratio for case studies; a) CS; b) SO; c) S70; d) SO70.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-2291906/v1/0bfdf8aefac905cbdbb18540.png"},{"id":29457497,"identity":"ca12f838-07f8-43d6-b1e6-a22235db6668","added_by":"auto","created_at":"2022-11-23 20:59:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2123182,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2291906/v1/a52e56c8-f55c-4623-a912-e8a413e21488.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effect of gravity shear ratio on governing failure mode of reinforced concrete slab-column connections","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOver the past several decades, there has been substantial industrial progress on the RC flat slabs in the building industry. RC flat roofs as two-way structural systems are being applied to almost every superstructure, targeting bearing light floor loads and moving them directly to the columns without using any beams or girders. The general behavior of slabs, by and large, relies heavily on the percentage of flexural reinforcement. Slabs can be structurally classified as light, medium and high flexural reinforcement ratios, basically leading to three different sorts of failure [1-5]. Three failure modes of the slabs are brittle punching shear, combined flexural-shear, and flexure-induced punching introduced as follows [6]: \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e a\u003c/strong\u003e. Brittle punching shear is generally known as the failure mode of the slabs with a high flexural reinforcement ratio. The obvious feature of this type of failure mode is that the ultimate rupture arises with no clear sign before the longitudinal reinforcement yielding. Meanwhile, both the tensile crack propagation and the plastic deformation are at the absolute minimum amount.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e. The slabs with a medium flexural reinforcement ratio illustrate a combined flexural-shear mode. This failure behavior acts in such a manner that the part of longitudinal reinforcement yields. Besides, the yield lines move along the diagonal directions according to the level of flexural reinforcement ratio; just as the amount of longitudinal reinforcement ratio decreases, so the yield-line pattern spreads rapidly to the full mechanism.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec\u003c/strong\u003e. Flexure-induced punching mode appears in the slabs with a low level of flexural reinforcement ratio, broadly leading to a ductile behavior through the plastic plateau region. In this type of failure behavior, the widespread yielding of longitudinal reinforcement causes the slabs to fail in the full mechanism of the yield-line pattern. The important feature of this type of failure mode is the occurrence of large plastic deflection in advance of the brittle punching failure while tensile cracks suddenly come into view along the depth of the slabs.\u003c/p\u003e\n\u003cp\u003eThe slabs have both advantages and disadvantages, but the benefits outweigh the drawbacks nonetheless. It is widely believed in the virtues of employing RC flat slab systems as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEmploying less formwork;\u003c/li\u003e\n\u003cli\u003eSaving in overall building height;\u003c/li\u003e\n\u003cli\u003eDesign flexibility of space layout;\u003c/li\u003e\n\u003cli\u003eReducing construction cost and time;\u003c/li\u003e\n\u003cli\u003eEase of reinforcing placement, mechanical and electrical services installation. \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ePredicting the value of the ultimate load-carrying strength of RC flat slabs puts obstacles in the way of researchers trying to develop theoretical methods. This issue has sparked a storm of controversy surrounding the equations of shear and flexural cracking loads of the slabs. The estimation of moments and stresses manifested in the slabs was come up with a justifiable theory in 1921 [8]. The basis of this theory was provided by the finite difference method (FDM); this method was applied to the slabs under different load cases considering the profound stiffness effect of columns [7].\u003c/p\u003e\n\u003cp\u003eIn 1925, the slab experimental tests were conducted in the form of footings [10] at the University of Illinois at Urbana-Champaign of the US, aiming to estimate the punching shear resistance. Nevertheless, the cross-section of tested footings was broad in comparison to the supports of mushroom slabs at that time. Consequently, further experimental tests were needed to precisely calculate the punching shear strength. Hence, some slabs were experimentally tested to plug the huge gaps in punching shear formulas [4]. Then, the significant influence of some important parameters such as support conditions, compressive strength of concrete, shear, and flexural reinforcement was proved on the punching shear capacity of the slabs [7]. In 1961, a large number of tests were experimentally performed on the slab-column connections and the results were the basis of recommendations in ACI 318 to determine the unbalanced moment strength due to the punching shear mechanism [11].\u003c/p\u003e\n\u003cp\u003eExperimental [6,12-16] and numerical [17-22] findings have made a major technological breakthrough in the subject of engineering applications. Over the last few decades, RC slab-column connections are becoming the focus of many pieces of research, regarded as a top priority. By contrast, there has always been a great deal of predicting the connection\u0026rsquo;s main failure modes. Hence, this research shed the light on projecting the failure modes of the connections exerted to the unbalanced moment with different vertical shear loads, by comparison of the maximum and minimum unbalanced moments, respectively, transferred by shear and flexure. The highest value of unbalanced moment capacity owing to the punching shear mechanism was derived by code provisions, e.g., ACI 318-19 [23], EC 2 [24], and MC2010 [25]. By contrast, the lowest value of unbalanced moment strength on account of flexural failure was extracted by the yield line theory (YLT). Respecting the tensile reinforcement ratio, the intersection of the results of codes and YLT was known as the boundary point of failure modes at which the flexural-shear collapse happened.\u003c/p\u003e\n\u003cp\u003eThe scheme of this research can be described in the following steps. First, the common failure modes of RC flat slabs were fully explained, including punching shear and flexural behaviors. Second, the governing formulae of the codes and YLT were represented, respectively, to acquire the unbalanced moment strength on account of punching shear and flexural failures. Then, the accuracy and safety of code provisions and predicted failure modes were compared with the results of the experimental database in the literature. Finally, 200 case studies were taken on the connections under moment transfer at the GSRs of 20%, 40%, and 60%, with respect to the flexural reinforcement ratio (0.0%-3.0%). Opening and shear strengthening were taken as distinguishing characteristics of the governing failure mode. The geometry of all specimens was the same. The first connection was considered as the control specimen without any modifications while the second one included four openings positioned close and parallel to the column. Two other specimens were strengthened with vertical reinforcing elements to enhance the equivalent shear stress regarding punching capacity by 70%.\u003c/p\u003e"},{"header":"2. Failure Mechanisms Of Rc Flat Slabs","content":"\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003e2.1. Punching shear failure\u003c/h2\u003e\n \u003cp\u003ePunching shear failure is a kind of dismal collapse observed mainly in RC two-way slabs as a result of the concentration of shear and bending stresses. This failure mode is a destructive mechanism occurred in the connection between the slab and support point as a column. In fact, this fracture is a process by which a part of the slab punches into the column and develops into the shortened cone shape. This brittle failure is associated with a razor-sharp reduction in the shear load-carrying capacity [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. On the whole, punching shear collapse is of critical importance to the design procedure of the slab structures. Therefore, it is absolutely vital to know the effective characteristics of the punching load-carrying capacity.\u003c/p\u003e\n \u003cp\u003eSeveral explanations have been offered for the issue of punching shear calculation of RC flat slabs by observing the final results of widespread experimental studies. Most studies confirmed that the punching shear strength of the slabs is mainly influenced by the strength of concrete, tensile longitudinal reinforcement ratio, geometry and dimensions of the column, size effect, opening, shear reinforcement, and compression steel. These parameters are a set of fixed limits controlling the punching shear behavior. Flexural reinforcement ratio, opening, shear reinforcement, and compressive steel that were the focus of this study, are characterized based on the available research literature, standards, and codes as follows:\u003c/p\u003e\n \u003cdiv class=\"Section3\" id=\"Sec4\"\u003e\n \u003ch2\u003e2.1.1. Flexural reinforcement ratio\u003c/h2\u003e\n \u003cp\u003eNumerous studies have shown the effect of the flexural reinforcement ratio on the punching shear strength of the slabs. It was observed that the punching shear capacity of the slabs increased by approximately 95% as the tensile longitudinal reinforcement ratio raised from 0.8\u0026ndash;2.1% [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]. This enhancement was about 63% when the aforementioned ratio varied from 0.6\u0026ndash;2.4% [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. According to extensive research, it was observed that the punching shear resistance is largely a function of the fourth root of the flexural reinforcement ratio [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. Nevertheless, some studies confirmed that the shear load-carrying capacity of the slabs is directly proportional to the cube root of the flexural reinforcement ratio [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]; this theoretical concept was incorporated in EC 2 [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. In addition, the punching shear strength presented in MC2010 [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e] is related to the flexural reinforcement ratio by the design value of the resistant moment. Despite EC 2 [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e] and MC2010 [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e], ACI 318 [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e] has never considered the importance of the flexural reinforcement ratio in the punching shear capacity.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec5\"\u003e\n \u003ch2\u003e2.1.2. Opening\u003c/h2\u003e\n \u003cp\u003eOpening as a serious defect leads to a significant decrease in the punching shear strength of RC flat slabs. As the openings are located close to the columns, punching shear designing of the slabs faces two tremendous challenges; the reduction of a part of concrete assigned to carry the punching shear stresses, and the interruption of flexural reinforcing bars [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. Due to the harmful destructive effects of openings on the punching shear capacity [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e], it is absolutely vital to accurately compute the punching shear resistance of the slabs with openings. Pioneering research has been done a much to advance the knowledge of openings in the slabs. For this reason, code provisions, without exception, are being updated in accordance with the research findings corresponding to the slabs with the opening. The damaging effect of opening on the control perimeter of the slabs is illustrated in Fig. 1. The length of the control perimeter is taken ineffective, enclosed by unbent lines sticking out from the center of the column and reaching the outer circumference of the opening if the space between the outside boundary of the column and opening is less than a specific value. The corresponding value is \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(4h\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(6d\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(5d\\)\u003c/span\u003e\u003c/span\u003e, respectively, in accordance with ACI 318\u0026thinsp;\u0026minus;\u0026thinsp;19 [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e], EC 2 [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e], and MC2010 [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]. This value was taken as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(10h\\)\u003c/span\u003e\u003c/span\u003e in the previous versions of ACI provisions.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec6\"\u003e\n \u003ch2\u003e2.1.3. Shear reinforcement\u003c/h2\u003e\n \u003cp\u003eTo improve the punching shear behavior of slabs, down-stand beams, column capitals, and drop-panel columns were used before the appearance of shear reinforcement. It is certainly true that employing shear reinforcing elements is definitely an extremely efficient way to increase the punching shear load capacity of the slabs [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e] and enhance the corresponding lateral drift capacity and ductility [\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e]. On the other hand, it decreases the slab thickness and improves the punching shear characteristics. Because of this, researchers have too deeply probed into the influence of various kinds of shear reinforcing elements [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e] on the punching shear strength. As the slab is strengthened by vertical reinforcing elements, it is important to monitor the concrete shear stress in an outermost control perimeter located at a distance from where the shear reinforcement is interrupted, and at a distance from the column. The outer and inner critical sections of the slab are illustrated in Fig.\u0026nbsp;1.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec7\"\u003e\n \u003ch2\u003e2.1.4. Compression steel\u003c/h2\u003e\n \u003cp\u003eCompressive steel is a catch-all term occasionally known as the effective punching shear parameter of the slabs, but it is becoming the focus of much research. Employing compression steel has a wide range of benefits as increasing bending load capacity and improving ductility [\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e]. Nonetheless, this effective parameter is completely ignored by code guidelines except in MC2010 [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. The influence of compression steel on the shear load-carrying capacity is closely associated with the design average flexural strength per unit length, as for MC 2010 [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. The nominal moment of resistance of a rectangular cross-section with the compression steel is estimated by working out the governing equilibrium equation of the stress-strain diagram, indicated in Fig. 2; this formulation is heavily dependent upon the yielding of steel reinforcing bars, resulting in four various expressions mentioned in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\" style=\"text-align: center;\"\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cbr\u003e Modes of equations for moment of resistance with effect of compression steel [\u003ca href=\"#_ENREF_48\" title=\"Mostofinejad, 2021; [in Persian]. #411\"\u003e48\u003c/a\u003e].\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tabc\"\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSteel strain\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSteel stress\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReinforcement ratio\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSteel yielding\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varepsilon _s}\u0026gt;{\\varepsilon _y}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varepsilon ^{\\prime}_s}\u0026gt;{\\varepsilon ^{\\prime}_y}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({f_s}={f_y}\\)\u003c/span\u003e\u003c/span\u003e and\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({f^{\\prime}_s}={f^{\\prime}_y}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\rho \u0026lt;{\\bar {\\rho }_b}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\rho \u0026gt;{\\bar {\\rho }_{\\hbox{min} }}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTension and compression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varepsilon _s}\u0026gt;{\\varepsilon _y}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varepsilon ^{\\prime}_s}\u0026lt;{\\varepsilon ^{\\prime}_y}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({f_s}={f_y}\\)\u003c/span\u003e\u003c/span\u003e and\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({f^{\\prime}_s}~\u0026lt;{f^{\\prime}_y}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\rho \u0026lt;{\\bar {\\rho }_b}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\rho \u0026lt;{\\bar {\\rho }_{\\hbox{min} }}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJust tension\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varepsilon _s}\u0026lt;{\\varepsilon _y}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varepsilon ^{\\prime}_s}\u0026gt;{\\varepsilon ^{\\prime}_y}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({f_s}~\u0026lt;{f_y}\\)\u003c/span\u003e\u003c/span\u003e and\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({f^{\\prime}_s}~={f^{\\prime}_y}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\rho \u0026gt;{\\bar {\\rho }_b}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\rho \u0026gt;{\\bar {\\rho }_{\\hbox{min} }}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJust compression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varepsilon _s}\u0026lt;{\\varepsilon _y}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varepsilon ^{\\prime}_s}~\u0026lt;~{\\varepsilon ^{\\prime}_y}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({f_s}~\u0026lt;{f_y}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({f^{\\prime}_s}~\u0026lt;{f^{\\prime}_y}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\rho \u0026gt;{\\bar {\\rho }_b}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\rho \u0026lt;{\\bar {\\rho }_{\\hbox{min} }}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec8\"\u003e\n \u003ch2\u003e2.2. Flexural failure mechanism\u003c/h2\u003e\n \u003cp\u003eGenerally, the bending performance of slabs is turned into a ductile behavior, experiencing large deflections under the high external loads. Additionally, numerous flexural cracks are created and distributed on the tensile side of the slabs as the warning signs before the collapse state [\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e]. Researchers have widely developed theories to figure out the flexural strength of the slabs. In 1953, yield line theory (YLT) as an upper bound method was introduced by Hognestad [\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e], employed in the limit analysis of the slabs. In this method, a yield line pattern is hypothesized as the collapse mechanism to estimate the flexural load-carrying capacity. The yield line pattern as the plastic hinge lines is predicted by the boundary conditions of the slabs. The internal moment of resistance occurs in the plastic hinge lines at the ultimate state of the slab section. The ultimate load of flexure is calculated by employing two different reliable methods; the principle of virtual work, and the equations of equilibrium. Besides, all the possible types of yield line patterns must be analyzed to check that the flexural resistance is not overestimated, as has the minimum value of all the possible scenarios [\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Equations Of Code Guidelines And Yield Line Theory","content":"\u003cdiv class=\"Section2\" id=\"Sec10\"\u003e\n \u003ch2\u003e3.1. Formulas of Unbalanced moment resistance at punching shear failure in codes\u003c/h2\u003e\n \u003cp\u003eIn the code provisions, formulae of unbalanced moment transferred by the eccentricity of shear due to the slabs, are based on extensive experimental tests. Furthermore, updating the corresponding formulas is needed for providing a confident prediction of the moment resistance more than ever. In this section, the important formulae of the code provisions are fully briefed to compute the unbalanced moment capacity owing to punching shear collapse.\u003c/p\u003e\n \u003cdiv class=\"Section3\" id=\"Sec11\"\u003e\n \u003ch2\u003e3.1.1. ACI 318\u0026thinsp;\u0026minus;\u0026thinsp;19 [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/h2\u003e\n \u003cp\u003eACI 318\u0026thinsp;\u0026minus;\u0026thinsp;19 [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e] employs an eccentric shear model to calculate the unbalanced moment resistance causing failure by punching shear. In this model, it is assumed that a linear shear stress distribution acts along the control perimeter of the slab-column connection for a given shear force and a fraction of an unbalanced moment. ACI 318\u0026thinsp;\u0026minus;\u0026thinsp;19 [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e] also states that the unbalanced moment resistance on account of the punching shear mechanism should be smaller than the flexural moment resisted by the hogging and sagging reinforcement over the effective width, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(c+3h\\)\u003c/span\u003e\u003c/span\u003e. Thus, the maximum unbalanced moment at punching shear failure is governed by [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]:\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ1\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$${M_n}=\\hbox{min} \\left\\{ {\\left( {{V_n} - {V_u}} \\right)\\frac{{{J_c}}}{{{\\gamma _v}{b_0}d{c_c}}},\\frac{{({m_{Rd}}+{{m^{\\prime}}_{Rd}})(c+3h)}}{{1 - {\\gamma _v}}}} \\right\\}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({V_n}\\)\u003c/span\u003e\u003c/span\u003e is obtained by Eq. (\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) in the absence of shear reinforcement, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({J_c}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\gamma _v}\\)\u003c/span\u003e\u003c/span\u003e are respectively proportional to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(c\\left[ {{b_1}d\\left( {{b_1}+3{b_2}} \\right)+{d^3}} \\right]/3\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(1 - {\\left( {1+2\\sqrt {{b_1}/{b_2}} /3} \\right)^{ - 1}}\\)\u003c/span\u003e\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ2\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$${V_n}={V_c}=\\hbox{min} \\left( {\\frac{1}{3}{\\lambda _s}\\lambda \\sqrt {{{f^{\\prime}}_c}} {b_0}d,\\left( {1+\\frac{2}{\\beta }} \\right){\\lambda _s}\\lambda \\frac{{\\sqrt {{{f^{\\prime}}_c}} {b_0}d}}{6},\\left( {\\frac{{{\\alpha _s}d}}{{{b_0}}}+2} \\right){\\lambda _s}\\lambda \\frac{{\\sqrt {{{f^{\\prime}}_c}} {b_0}d}}{{12}}} \\right)$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ein which, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\lambda _s}\\)\u003c/span\u003e\u003c/span\u003e is equated to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sqrt {2/(1+0.004d)}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\lambda\\)\u003c/span\u003e\u003c/span\u003e has the value of 1.0 for a normal-weight concrete, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\alpha _s}\\)\u003c/span\u003e\u003c/span\u003e holds the value of 40 for interior columns [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec12\"\u003e\n \u003ch2\u003e3.1.2. EC 2 [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/h2\u003e\n \u003cp\u003eSimilar to ACI 318\u0026thinsp;\u0026minus;\u0026thinsp;19 [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e], EC 2 [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e] assesses the maximum unbalanced moment resistance due to punching shear failure by exploiting the eccentric shear transfer mechanism according to Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). But, it presumes that the shear stress distribution is uniform instead of linear.\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ3\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e$${M_{Rd}}=\\left( {{V_{Rd}} - {V_{Ed}}} \\right)\\frac{{{W_1}}}{{{u_1}\\kappa }}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({V_{Rd}}\\)\u003c/span\u003e\u003c/span\u003e is associated with Eq. (\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) as the nominal punching shear capacity is only attributed to concrete, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({W_1}\\)\u003c/span\u003e\u003c/span\u003e is in relation to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(c{}_{1}^{2}/2+{c_1}{c_2}+4{c_2}d+16{d^2}+2\\pi d{c_1}\\)\u003c/span\u003e\u003c/span\u003e, and \u0026nbsp;is considered 0.6 for the square loaded area [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ4\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e$${V_{Rd}}={V_{Rd,c}}={C_{Rd,c}}k{(100{\\rho _l}{f_{ck}})^{1/3}}{u_1}d \\geqslant {V_{\\hbox{min} }}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ein which, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C_{Rd,c}}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({V_{\\hbox{min} }}\\)\u003c/span\u003e\u003c/span\u003e are obtained by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(0.18/{\\gamma _c}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(0.035{k^{3/2}}{f_{ck}}^{{1/2}}\\)\u003c/span\u003e\u003c/span\u003e, respectively; \u0026nbsp;and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho _l}\\)\u003c/span\u003e\u003c/span\u003e are respectively taken as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(1+\\sqrt {200/d}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sqrt {{\\rho _{ly}}{\\rho _{lz}}}\\)\u003c/span\u003e\u003c/span\u003e limited to 2.0 and 0.02, respectively; \u0026nbsp;is, moreover, defined by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(({d_y}+{d_z})/2\\)\u003c/span\u003e\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec13\"\u003e\n \u003ch2\u003e3.1.3. MC2010 [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/h2\u003e\n \u003cp\u003eOther than ACI 318\u0026thinsp;\u0026minus;\u0026thinsp;19 [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e] and EC 2 [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e], MC2010 [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e] utilizes a failure criterion based on the critical shear crack theory (CSCT) as defined in Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e), to put a figure on the unbalanced moment strength due to punching shear collapse.\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ5\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e$${V_{Rd}}=\\hbox{min} \\left( {\\frac{1}{{1.5+0.9{k_{dg}}\\psi d}},0.6} \\right)\\frac{{\\sqrt {{f_{ck}}} }}{{{\\gamma _c}}}{b_0}{d_v} \\geqslant {V_{Ed}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ein which, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k_{dg}}\\)\u003c/span\u003e\u003c/span\u003e is proportioned to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(32/\\left( {16+{d_g}} \\right)\\)\u003c/span\u003e\u003c/span\u003e with the lower limit of 0.75, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({b_0}\\)\u003c/span\u003e\u003c/span\u003e is determined by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k_e}{b_1}\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\psi\\)\u003c/span\u003e\u003c/span\u003e in the second level of approximation as the redistribution of bending moment is significant, may be evaluated by a parabola equation as following [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]:\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ6\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e$$\\psi =1.5\\frac{{{r_s}}}{d}\\frac{{{f_{yd}}}}{{{E_s}}}{\\left( {\\frac{{{m_{sd}}}}{{{m_{Rd}}}}} \\right)^{1.5}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eBesides, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k_e}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({m_{sd}}\\)\u003c/span\u003e\u003c/span\u003e are respectively computed by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(1/\\left( {1+{e_u}/{b_u}} \\right)\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({V_{Ed}}\\left( {1/8+0.5{e_u}/{b_s}} \\right)\\)\u003c/span\u003e\u003c/span\u003e, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({e_u}={M_{Ed}}/{V_{Ed}}\\)\u003c/span\u003e\u003c/span\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({m_{Rd}}\\)\u003c/span\u003e\u003c/span\u003e is analyzed by the notes mentioned in Section \u003cspan class=\"InternalRef\"\u003e2.1.4\u003c/span\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({f_{yd}}\\)\u003c/span\u003e\u003c/span\u003e is also equal to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({f_{yk}}/{\\gamma _c}\\)\u003c/span\u003e\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec14\"\u003e\n \u003ch2\u003e3.2. Formulas of unbalanced bending moment in yield line theory\u003c/h2\u003e\n \u003cp\u003eA yield line pattern was proposed in Fig.\u0026nbsp;3 by Dilger and Cao [\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e] for the slab-column connections subjected to substantial lateral load and significant gravity force. Based on the principle of virtual work, the unbalanced moment resistance owing to flexural collapse is associated with Eq.\u0026nbsp;(7a) for the slab without opening. It is governed by Eq.\u0026nbsp;(7b) for the slab with four square openings placed adjacent and parallel to the column.\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\" width=\"860\" height=\"100\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eIt is also assumed that \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\ell _{Op}}\\)\u003c/span\u003e\u003c/span\u003e as the opening size is not bigger than the column size. The openings\u0026rsquo; influence over the unbalanced moment strength due to flexural mechanism was considered in a way that the area of openings is removed from the yield line pattern of the slab without any openings.\u003c/p\u003e\n\n\u003c/div\u003e"},{"header":"4. Method Analysis","content":"\u003cp\u003eThe proficiency in code guidelines was probed by comparing the unbalanced moment capacity owing to punching shear collapse with the experimental test results of 25 RC slab-column connections collected as a literature database. The main criterion for selecting the database was a high degree of reliability of the experimental results based on the literature sources of genuine journals. The properties of the experimental database were depicted in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSpecifications of literature experimental specimens.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecimen\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(mm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(mm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({r_s}\\)\u003c/span\u003e\u003c/span\u003e (mm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({f^{\\prime}_c}\\)\u003c/span\u003e\u003c/span\u003e (MPa)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\rho\\)\u003c/span\u003e\u003c/span\u003e (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\rho ^{\\prime}\\)\u003c/span\u003e\u003c/span\u003e (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({f_y}\\)\u003c/span\u003e\u003c/span\u003e (MPa)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({f^{\\prime}_y}\\)\u003c/span\u003e\u003c/span\u003e (MPa)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({V_u}\\)\u003c/span\u003e\u003c/span\u003e (kN)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRobertson et al. [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eRobertson and Johnson [\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND1C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND4LL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND5XL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND6HR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND7LR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND8BU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eBu \u0026amp; Polak [\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSW5\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSW6\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePark et al. [\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRC-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRC-B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e158.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eAlmeida et al. [\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC-40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e167.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e203.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"11\"\u003e\n \u003cp\u003eDrakatos et al. [\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e253.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n 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\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e288.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e376.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e195.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e192.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e301.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e299.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e205.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"11\"\u003e\n \u003cp\u003e\u003csup\u003e*\u003c/sup\u003e SW5 and SW6 have two square openings with a dimension of 150 mm, located parallel and adjacent to the column face\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eFor the literature database, Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the values of unbalanced moment resistance on account of punching shear failure predicted by the code guidelines and those presented by experimental tests, as does their proportion. Average (AVG) and coefficient of variant (CV), were employed as statistical parameters to investigate the empirical methods regarding safety and precision, respectively, aimed at comparing the results of codes and experimental studies. The average value of the ratio between \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({M_{punch}}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({M_{\\exp }}\\)\u003c/span\u003e\u003c/span\u003e may show the degree of safety. Code underestimates the unbalanced moment resistance at punching shear collapse if the ratio is less than 1.0. It yields an optimized level of safety for the outputs as the ratio is in the period of 1.0 and 1.1. Besides, it presents satisfactory safety responses as the ratio is between 1.1 and 1.3. The conservative estimation is also obtained for the ratio with the bigger value of 1.3.\u003c/p\u003e\n\u003cp\u003eTo probe the precision of unbalanced moment strength due to punching shear failure calculated by the codes, CV was calculated for the proportion of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({M_{punch}}\\)\u003c/span\u003e\u003c/span\u003e to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({M_{\\exp }}\\)\u003c/span\u003e\u003c/span\u003e. This parameter is obeyed by the ratio of the standard deviation to the average, leading to determining the dispersion of frequency distribution. As the value of CV is lower, the measure of dispersion decreases, and therefore, the response value tends to be more accurate.\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e expresses AVG and CV of the unbalanced moment resistance owing to the punching shear mechanism according to ACI 318\u0026thinsp;\u0026minus;\u0026thinsp;19 [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e], EC 2 [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e], and MC2010 [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e] without and with the effect of compression steel. The AVG of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({M_{punch}}/{M_{\\exp }}\\)\u003c/span\u003e\u003c/span\u003e for those guidelines was respectively 0.98, 1.19, 0.83, and 0.84; moreover, the CV of those was 0.14, 0.21, 0.10, and 0.10 in the order given. As a consequence, the results of ACI 318\u0026thinsp;\u0026minus;\u0026thinsp;19 [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e] and MC2010 [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e] were underestimated, but those of EC 2 [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e] had a satisfactory level of safety. MC2010 [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e] estimated the unbalanced moment transferred by shear stress at the same level of precision and was more accurate than ACI 318\u0026thinsp;\u0026minus;\u0026thinsp;19 [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e] and EC 2 [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, ACI 318\u0026thinsp;\u0026minus;\u0026thinsp;19 [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e] yields more exact answers than EC 2 [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e].\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of literature experimental results with predictions of code guidelines.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSpecimen\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({M_{punch}}\\)\u003c/span\u003e\u003c/span\u003e (kN.m)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({M_{\\exp }}\\)\u003c/span\u003e\u003c/span\u003e (kN.m)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{{{M_{punch}}}}{{{M_{\\exp }}}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eACI [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEC [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMC [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMC [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]-CS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eACI [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEC [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMC [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMC [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]-CS\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRobertson et al. [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eRobertson and Johnson [\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND1C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND4LL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND5XL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND6HR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND7LR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND8BU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eBu \u0026amp; Polak [\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSW5\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSW6\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePark et al. [\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRC-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRC-B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eAlmeida et al. [\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e121.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC-40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"11\"\u003e\n \u003cp\u003eDrakatos et al. [\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e377.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e472.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e297.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e298.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e525.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e330.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e412.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e254.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e257.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e196.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e316.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e401.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e246.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e249.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e310.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e379.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e211.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e215.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e527.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e380.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e478.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e307.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e312.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e462.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e391.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e491.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e316.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e320.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e372.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e370.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e480.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e323.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e324.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e384.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e289.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e517.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e351.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e353.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e290.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e289.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e517.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e351.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e353.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e286.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e351.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e591.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e418.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e418.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e469.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e363.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e609.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e431.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e431.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e410.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAVG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e indicates the ratio between maximum unbalanced moment resistance on account of punching shear failure gained from the codes and minimum unbalanced moment resistance at flexural collapse obtained by the YLT. The corresponding proportion was utilized to deviate the main failure mode of slab-column connections without and with the effect of compression steel. If \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({M_{punch,\\hbox{max} }}/{M_{flex}}\\)\u003c/span\u003e\u003c/span\u003e is lower or greater than 1.0, the failure mode is probably flexural or punching shear, respectively. Furthermore, the failure mode may be governed by shear-flexural as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({M_{punch,\\hbox{max} }}/{M_{flex}}\\)\u003c/span\u003e\u003c/span\u003e is equal to 1.0. As shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, the failure modes predicted by the proposed method, were 92% compatible with those reported by the experimental database. As a result, this approach is capable to project the governing failure mode of the connections for research purposes and structural designing.\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eExperimental and predicted failure modes.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSpecimen\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({M_{flex}}\\)\u003c/span\u003e\u003c/span\u003e (kN.m)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({M_{punch,\\hbox{max} }}\\)\u003c/span\u003e\u003c/span\u003e (kN.m)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{{{M_{punch,\\hbox{max} }}}}{{{M_{flex}}}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFailure mode\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYLT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYLT-CS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYLT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYLT-CS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEXP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRobertson et al. [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eRobertson and Johnson [\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND1C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND4LL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND5XL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND6HR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e148.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e149.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND7LR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND8BU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e153.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e153.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eBu \u0026amp; Polak [\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSW5\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSW6\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePark et al. [\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRC-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e239.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e240.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRC-B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eAlmeida et al. [\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e266.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e279.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC-40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e263.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e271.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e253.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e263.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"11\"\u003e\n \u003cp\u003eDrakatos et al. [\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1056.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1065.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e472.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear-Flex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1005.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1021.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e412.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear-Flex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1001.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1014.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e401.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e920.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e940.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e379.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e934.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e958.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e478.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e946.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e967.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e491.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1050.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1058.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e480.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1908.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1925.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e517.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1908.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1925.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e517.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1804.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1804.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e591.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1832.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1832.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e609.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"5. Case Studies On Rc Flat-slab Systems","content":"\u003cp\u003eIn this section, the governing failure mode of 200 case studies was gotten for the slab-column connections concerning the change of longitudinal reinforcing steel from 0.0 to 3.0%. It exploited the proposed prediction-failure method mentioned in Section \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. As displayed in Fig.\u0026nbsp;4, one slab served as the control specimen known as CS, but the other contained four square openings placed adjacent and parallel to the column face, named SO. Two other specimens were strengthened in a way that the equivalent stress relating to shear load-carrying capacity escalates 70% in comparison to those without any shear reinforcing element. Both had the same geometry as CS and SO, called respectively S70 and SO70. All of the slabs were square with the dimensions of 2000 mm while the length of clear span measured center-to-center of the supports was 1800 mm. The slabs\u0026rsquo; thickness was 150 mm whereas their effective depth was taken as 120 mm. The size of the openings was equal to 110 mm according to the explanatory note recommended by ACI 318\u0026thinsp;\u0026minus;\u0026thinsp;19 [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]; at the intersection of two column strips, the maximum value of opening size is limited to one eight the width of the column strip in either span.\u003c/p\u003e\n\u003cp\u003eThe lateral loading and vertical shear force were monotonically subjected to the top of the square column whereupon the slab-column connection collapsed under the unbalanced moment transferred by the eccentricity of shear. The column had a size of 300\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times\\)\u003c/span\u003e\u003c/span\u003e300 mm. 28-day characteristic compressive cylinder strength of concrete, yield strength, and modulus of elasticity of flexural reinforcement were 30 MPa, 420 MPa, and 200,000 MPa, in the order given. The vertical shear load was considered variable in all the case studies since it highly affects the main failure mode of the connections. The gravity shear force was 0.2, 0.4, and 0.6 of the nominal punching shear strength computed by ACI 318\u0026thinsp;\u0026minus;\u0026thinsp;19 [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. Thus, the ratio of the gravity shear load to the nominal punching shear capacity, described as the gravity shear ratio, had the percent values of 20, 40, and 60.\u003c/p\u003e\n\u003cp\u003eFigures\u0026nbsp;5(a), (b), (c), and (d) depict the unbalanced moment strength at punching shear collapse of CS, SO, S70, and SO70, respectively, achieved by ACI 318\u0026thinsp;\u0026minus;\u0026thinsp;19 [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e] while the flexural reinforcement ratio differs. The vertical axis covers the unbalanced moment transferred by shear stresses with the unchanged value at different percent amounts of GSRs as 20, 40, and 60. The horizontal axis specifies the ratio of longitudinal reinforcing steel between 0.0% and 3.0%. The unbalanced moment capacity due to the punching shear mechanism remained consistent for 0.0%-3.0% of the ratio of flexural reinforcement. The constant values of CS, SO, S70, and SO70 at the GSR of 20%, were respectively 105.16 kN, 37.15 kN, 197.18 kN, and 75.17 kN. At the GSRs of 40%, these values were 78.87 kN, 20.01 kN, 170.89 kN, and 58.02 kN in the order given. The corresponding amounts changed to 52.58 kN, 2.86 kN, 144.60 kN, and 40.87 kN at the GSR of 60% in the same order.\u003c/p\u003e\n\u003cp\u003eThe damaging effect of opening on the unbalanced moment resistance owing to punching shear failure of CS with the GSRs of 20%, 40%, and 60%, were respectively 64.7%, 74.6%, and 94.6%. The corresponding values were 61.9%, 66.1%, and 71.7% for S70 in the same order. Besides, shear strengthening improved the unbalanced moment strength at the punching shear mechanism of CS as much as 87.5%, 116.7%, and 175.0%, respectively, at the GSRs of 20%, 40%, and 60%. For SO, these amounts switched to 102.3%, 190.0%, and 1330.0% in a similar order. Overall, it can be concluded that the unbalanced moment capacity due to punching shear collapse remained static regarding the change of tensile steel ratio, as is obvious in Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) of Section \u003cspan class=\"InternalRef\"\u003e3.1.1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eFigures 6(a), (b), (c), and (d) provide information about the unbalanced moment resistance owing to the punching shear mechanism of CS, SO, S70, and SO70, respectively, governed by EC 2 [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e] that takes place over a wide range of longitudinal reinforcement ratio. The vertical axis indicates the unbalanced moment strength at punching shear failure between two different plateaus at various GSRs (20%, 40%, and 60%). Besides, the horizontal axis designates the major alteration of the ratio of flexural reinforcing steel from 0.0\u0026ndash;3.0%.\u003c/p\u003e\n\u003cp\u003eIn the small initial interval of the ratio of longitudinal reinforcing steel, the unbalanced moment capacity due to punching shear failure stayed the same because of the inequality hinted in Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) of Section \u003cspan class=\"InternalRef\"\u003e3.1.2\u003c/span\u003e; the nominal punching shear capacity of the slab must be greater than the minimum of design shear resistance of the member without shear reinforcement. It is noteworthy to point out that the unbalanced moment resistance owing to punching shear collapse of the case studies is taken as zero if the nominal punching shear strength is less than the vertical shear force. It arose from the weakness in the shear load-carrying capacity. Other than S70, CS and SO70 experienced a period of stability in zero value at the GSR of 60%, as did SO at both GSRs of 40% and 60%.\u003c/p\u003e\n\u003cp\u003eThe trend was followed by a noticeable increase from the end of the first plateau to reach a particular level with the maximum value of unbalanced moment resistance on account of punching shear failure. The maximum values attributed to CS, SO, S70, and SO70 at the GSR of 20%, were respectively 174.01 kN, 50.44 kN, 319.15 kN, and 99.37 kN. At the GSR of 40%, these values were 140.69 kN, 30.99 kN, 285.83 kN, and 79.92 kN in the order presented. Besides, these amounts changed to 107.36 kN, 11.54 kN, 252.50 kN, and 60.46 kN at the GSR of 60% in the same order. The unbalanced moment delivered by shear of SO decreased 71.0%, 78.0%, and 89.3% in comparison with the control specimen, respectively, at the GSRs of 20%, 40%, and 60. The relating differences for S70 and SO70 had the following percent rise and decline values in the order proposed; 83.41, 103.16, and 135.19; 42.90, 43.20, and 43.68.\u003c/p\u003e\n\u003cp\u003eFrom 2.0\u0026ndash;3.0% of the flexural steel ratio, the figure was flat. This plateau is raised from the explanatory note introduced in Section \u003cspan class=\"InternalRef\"\u003e3.1.2\u003c/span\u003e; the upper limit of the ratio of tensile reinforcement is 2.0%. Generally, it can be deduced that there has been a rapid escalation in the value of unbalanced moment resistance at punching shear failure, although some indications are leading to a change in the trend.\u003c/p\u003e\n\u003cp\u003eFigures\u0026nbsp;7(a), (b), (c), and (d) clearly display the major alteration of unbalanced moment strength on account of the punching shear mechanism of CS, SO, S70, and SO70, respectively, acquired by MC2010 [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e] as the ratio of tensile reinforcement extremely alters. On the vertical axis, the unbalanced moment resistance due to punching shear collapse was described from a flat level in zero value to the relating maximum value, wherein on the horizontal axis, the longitudinal steel ratio differed in the range of 0.0\u0026ndash;3.0%. The GSR varied from 20\u0026ndash;40% and 60%. As the tensile reinforcement ratio was 3.0%, the lowest values of unbalanced moment capacity owing to the punching shear mechanism of the slabs were at the GSRs of 20%. These values were obeyed respectively by 164.70 kN, 77.35 kN, 279.99 kN, and 131.50 kN for CS, SO, S70, and SO70. Besides, the highest counterparts were at the GSR of 60% with the values of 103.00 kN, 32.69 kN, 175.11 kN, and 55.57 kN in a similar order. The difference of unbalanced moment resistance at punching shear collapse in the GSRs of 20% and 60%, assigned to the slabs without and with opening, were respectively 37.5% and 57.7%. Except for the first plateau, it can be inferred that the unbalanced moment transferred by the shear eccentricity of the slabs was on the increase in such a wise that the trend of the GSR of 20% is consistently higher than that of 40% and 60%.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable border=\"1\" id=\"Tabi\"\u003e\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn Figs.\u0026nbsp;8(a), (b), (c), and (d), the influence of compression steel was expressed over the unbalanced moment capacity on account of punching shear collapse of CS, SO, S70, and SO70, respectively, calculated by MC2010 [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e] in the differential ratios of longitudinal reinforcement. The vertical axis presents the unbalanced moment strength owing to punching shear failure from the zero value, and forecasts trends up the regrading maximum value at the GSRs of 20%, 40%, and 60%. The horizontal axis proposes the ratio of tensile reinforcing steel from 0.0\u0026ndash;3.0% in addition. At the end of the period of flexural reinforcement ratio, the norm values of unbalanced moment resistance at the punching shear mechanism of CS, SO, S70, and SO70 were at the GSR of 40%, with the amount of 139.26 kN, 56.80 kN, 236.74 kN, and 96.55 kN. Additionally, the effects of opening and shear strengthening on the norm of unbalanced moment strength due to the punching shear mechanism were 59.2% and 70.0% in the order presented. All in all, the unbalanced moment transferred by the eccentricity of shear showed an upward trend, but there is an indication in the form of a steady level that this figure may be altering. By comparison of Fig. 7 with Fig. 8, the trend of unbalanced moment resistance on account of punching shear collapse of the slabs with the effect of compression steel overtook those without the corresponding influence, but both followed a similar pattern.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable border=\"1\" id=\"Tabj\"\u003e\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFigures\u0026nbsp;9(a) and (b) reveal the unbalanced moment strength at the flexural collapse of the slabs without and with opening, respectively, derived by the YLT [\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e] for the tensile steel reinforcement. While the vertical axis demonstrates the unbalanced bending moment from zero to the ultimate value at the GSRs of 20%, 40%, and 60%, the horizontal axis shows the ratio of tensile reinforcement ratio from 0.0\u0026ndash;3.0%. The values of unbalanced moment transferred by flexural stresses of the slabs without opening at the GSRs of 20%, 40%, and 60%, were respectively 666.30 kN, 655.25 kN, and 644.21 kN. For the slabs with opening, these values were 576.35 kN, 565.30 kN, and 554.24 kN in the same order. The gap between the unbalanced moment resistance owing to the flexural mechanism of the slabs without and with opening, were respectively 13.5%, 13.7%, and 14.0% at the GSRs of 20%, 40%, and 60%. Overall, the graph shows how the unbalanced moment transferred by flexure surged dramatically through the escalation of the ratio of flexural reinforcement.\u003c/p\u003e\n\u003cp\u003eThe compression steel\u0026rsquo;s influence on the unbalanced moment capacity on account of the flexural failure of the slabs without and with an opening is manifested in Figs. 10(a) and (b), respectively, according to the rise in tensile reinforcement ratio. The horizontal and vertical axes, respectively, characterize the ratio of longitudinal reinforcement in the range of 0.0\u0026ndash;3.0%, and the unbalanced moment resistance due to flexural collapse from the zero value to its maximum value. The values of unbalanced moment capacity owing to the flexural mechanism of the slabs without and with opening were respectively 720.65 kN and 623.48 kN at the GSR of 20%. For the GSRs of 40% and 60%, the corresponding values were in the following order; 709.60 kN and 612.44 kN, and 698.56 kN and 601.39 kN. The gap values between the unbalanced moment transferred by flexural eccentricity at the GSRs of 20% and 40%, were 1.5% and 1.8% for the slabs without and with opening, respectively. The difference values were 3.1% and 3.5% regarding the GSRs of 20% and 60%, as these figures were 1.6% and 1.8% concerning the GSRs of 20% and 60% in the order mentioned. The unbalanced moment resistance at flexural failure, by and large, rose significantly throughout the growth in the flexural steel ratio. By comparison of Fig. 10 with Fig. 9, the unbalanced moment strength on account of the flexural mechanism of the slab with the influence of compressive steel outstripped that without the relating effect, whereas trends of both were similar.\u003c/p\u003e\n\u003cp\u003eTo predict the governing failure mode of the slabs, the maximum unbalanced moment strength on account of the punching shear mechanism governed by the code provisions [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e] compared with that on account of flexural failure obeyed by the YLT [\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e], as mentioned in detail in Section \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Figures\u0026nbsp;11(a), (b), (c), and (d) present information on the maximum unbalanced moment delivered by shear of CS, SO, S70, and SO70, respectively, as well as the minimum unbalanced moment transferred by flexure, regarding the ratio of longitudinal reinforcing steel. The vertical axis represents the maximum and minimum unbalanced moment resistances at punching shear and flexural failures at the GSRs of 20%, 40%, and 60%, in the range of 0.0 to its highest value. Moreover, the horizontal axis describes the change in the ratio of tensile reinforcement between two percent values of 0.0 and 3.0.\u003c/p\u003e\n\u003cp\u003eThe intersection of maximum and minimum unbalanced moments transferred by shear and flexural eccentricities, respectively, according to the flexural reinforcement ratio, specifies the coordinate of an identifier point. This point is assigned to the boundary between punching shear and flexural failures. On the other hand, the flexural-shear failure mode of the slabs happens at this point. Additionally, the governing failure mode of the slabs is flexural if the longitudinal reinforcement ratio is less than the x-coordinate of BP, although punching shear failure occurs for the greater ratios of flexural reinforcing steel. The highest and lowest longitudinal reinforcement ratio of boundary points were respectively 0.88% and 0.14%, manifested in S70 and SO at the GSRs of 20% and 60%. It can be ascertained that a larger range of tensile reinforcement ratios allocates to the punching shear collapse.\u003c/p\u003e\n\u003cp\u003eThe coordinates of boundary points of CS, SO, S70, and SO70, at the GSRs of 20%, 40%, and 60%, were mentioned in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. Thus, the minimum unbalanced moment strengths at flexural failure compared with the unbalanced moment transferred by shear computed by ACI 318\u0026thinsp;\u0026minus;\u0026thinsp;19 [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e], EC 2 [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e], and MC2010 [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e] without and with the effect of compression steel, as well as their maximum values. The openings nosedived the x and y coordinates of boundary points of CS in the amounts of 52.5% and 64.7%, respectively, at the GSR of 20%. For the GSRs of 40% and 60%, these values changed to 54.3% and 74.6%, and 51.7% and 94.6%, in the order mentioned. In addition, the shear strengthening leaped x and y coordinates of the boundary points at the GSRs of 20%, 40%, and 60%, respectively to 120.0% and 123.6%, 111.4% and 136.0%, and 113.8% and 175.0%. Moreover, Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e compares the boundary points attained by the codes [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e] regarding the highest values.\u003c/p\u003e\n\u003cp\u003eAccording to data shown in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, the boundary points achieved by MC2010 [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e] without and with the effect of compression steel, had lower values in comparison with other counterparts, if any at all. The concerning values of EC 2 [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e] were lower and greater than those of ACI 318\u0026thinsp;\u0026minus;\u0026thinsp;19 [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e] and MC2010 [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e], respectively. Besides, the boundary points calculated by ACI 318\u0026thinsp;\u0026minus;\u0026thinsp;19 [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e] had the maximum amounts, compared with those of other code provisions. In conclusion, the governing failure modes of the slabs were governed by the results of ACI 318\u0026thinsp;\u0026minus;\u0026thinsp;19 [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\n\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n\u003cdiv align=\"left\" class=\"colspec\" style=\"text-align: center;\"\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e Intersection of maximum and minimum unbalanced moment resistances and its counterpart in tensile reinforcement ratio, respectively, due to code guidelines and yield line theory, at different GSRs.\u003c/div\u003e\n\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tabm\" style=\"border-collapse: collapse; margin: 0px auto;\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSpecimen\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGSR (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho _{bp}}\\)\u003c/span\u003e\u003c/span\u003e (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({M_{bp}}\\)\u003c/span\u003e\u003c/span\u003e (kN.m)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eACI [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEC [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMC [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMC [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]-CS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eACI [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEC [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMC [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMC [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]-CS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e235.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e235.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e235.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e186.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e186.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e186.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSO70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study was aimed at predicting the governing failure mode of RC slab-column connections under unbalanced moments developed by different gravity shear loads, which is foremost amongst the major works of literature. On this wise, the maximum unbalanced moment capacities on account of the punching shear mechanism computed by code provisions were compared to the unbalanced moment strengths due to flexural failure by the YLT. This comparison was a proper procedure to obtain the boundary point. The efficiency and validity of this method were proved by comparing the approach\u0026rsquo;s outputs to the experimental results of RC slab-column connections collected in a literature database. Subsequently, 200 case studies were conducted on the slab-column connections respecting the change of tensile reinforcement ratio. The main conclusions of the results of this survey can be summarized as follows:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAccording to the statistical analysis on \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({M_{punch}}/{M_{\\exp }}\\)\u003c/span\u003e\u003c/span\u003e, EC2 gave satisfactory responses of safety. However, ACI 318\u0026thinsp;\u0026minus;\u0026thinsp;19 and MC2010 yield underestimated answers. Moreover, the results of MC2010 were 28. % and 52. % more accurate than those of ACI 318\u0026thinsp;\u0026minus;\u0026thinsp;19 and EC2, respectively. The outputs of ACI 318\u0026thinsp;\u0026minus;\u0026thinsp;19 were also 33.3% more precise than those of EC 2.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eShear strengthening increased the range of flexural reinforcement ratio and corresponding unbalanced moment resistance at which the governing failure mode is not flexural, whereas the opening had the opposite effect. For instance, x and y coordinates of boundary points of failure modes attributed to CS, SO, and S70 at the GSR of 20%, were as follows in the order given; 0.4% and 105.16 kN; 0.19% and 37.15 kN; 0.88% and 235.11 kN.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe boundary points coordinate of flexural-shear failure mode achieved by ACI 318\u0026thinsp;\u0026minus;\u0026thinsp;19 (i.e., 0.35% and 78.87 kN) were greater than those of EC 2 (i.e., 0.13% and 17.00 kN) and MC2010 (i.e., 0.06% and 0.00), leading to being the main boundary points.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAs the GSR plummeted, the boundary points of governing failure modes soared, and the x and y coordinates of SO70 respectively declined by 10.3% and 29.6% as the GSR changed from 60\u0026ndash;40%.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe effect of GSR on the unbalance moment resistance at the boundary of failure modes shot up when the case studies contained openings. The x and y coordinate of the boundary point of failure mode of CS, SO, S70, and SO70 rose respectively to 12.5% and 25.0%, 16.0% and 46.1%, 15.9% and 20.8%, and 5.9% and 22.8%, when the GSR replaced from 40\u0026ndash;20%.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Nomenclature","content":"\u003cp\u003e\u003cimg 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\" width=\"992\" height=\"4658\"\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eStein, T., Ghali, A., and Dilger, W., \u0026ldquo;Distinction between punching and flexural failure modes of flat plates\u0026rdquo;, \u003cem\u003eACI Structural Journal\u003c/em\u003e, Vol. 104, 3, pp. 357-365, 2007.\u003c/li\u003e\n\u003cli\u003eEbead, U., and Marzouk, H., \u0026ldquo;Fiber-reinforced polymer strengthening of two-way slabs\u0026rdquo;, \u003cem\u003eACI Structural Journal\u003c/em\u003e, Vol. 101, 5, pp. 650-659, 2004.\u003c/li\u003e\n\u003cli\u003eMichel, L., Ferrier, E., Agbossou, A., and Patrice, H., \u0026ldquo;Flexural stiffness modeling of RC slab strengthened by externally bonded FRP\u0026rdquo;, \u003cem\u003eComposites Part B-engineering\u003c/em\u003e, Vol. 40, pp. 758-765, 2009.\u003c/li\u003e\n\u003cli\u003eElstner, R.C., and Hognestad, E., \u0026ldquo;Shearing strength of reinforced concrete slabs\u0026rdquo;, 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Nevertheless, there has not yet been a definitive method to precisely foresee the governing slab failure modes. This research was targeted at predicting the main failure mode of RC slab-column connections subjected to unbalanced moment and various vertical shear forces, for the first time. Thus, the failure modes of the connections were deviated by comparing the unbalanced moment capacity at punching shear failure controlled by the codes and unbalanced moment strength at the flexural mechanism checked by the yield line theory (YLT). The procedure was validated by the results of experimental tests carried out at authentic research in the literature. Afterward, 200 case studies were done on the connections under moment transfer at 20%, 40%, and 60% of gravity shear ratios (GSRs), regarding the alteration of flexural reinforcement ratio from zero to 3.0%. Openings and shear strengthening were looked into in the case studies as two highly effective parameters for the governing failure mode. The intersection of unbalanced moment capacities owing to punching shear and flexural collapses with respect to the longitudinal reinforcement ratio indicated the coordinate of boundary point (BP) between possible failure modes. It was proved that the GSR rise and the existence of opening, lead to a decline in the coordinate of BP notwithstanding, the effect of shear strengthening was in reverse.\u003c/p\u003e","manuscriptTitle":"Effect of gravity shear ratio on governing failure mode of reinforced concrete slab-column connections","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-11-23 19:17:48","doi":"10.21203/rs.3.rs-2291906/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f267d17a-5c20-4557-97bc-1ef79ea4e55f","owner":[],"postedDate":"November 23rd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-11-23T20:59:10+00:00","versionOfRecord":[],"versionCreatedAt":"2022-11-23 19:17:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2291906","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2291906","identity":"rs-2291906","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}