Modification of PBSD Procedure for Moderate Shear Walls Subjected to Near-Field Excitation

Modification of PBSD Procedure for Moderate Shear Walls Subjected to Near-Field Excitation | 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 Modification of PBSD Procedure for Moderate Shear Walls Subjected to Near-Field Excitation Peyman Ghatee, Mohd Saleh Jaafar, Azmi Ibrahim, Nazanin Fallahi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-259219/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 In the near-field areas, VCE effectively exists which is so sensitive depending on the distance from a source and the earthquake magnitude. This has remarkable effects when shear wall is to be in moderate size which causes it to face wide diversity modes of failure. So, a modified procedure is conceived to ensure the response of the shear wall subjected to biaxial excitation. Thereby, there is an evaluation process that is based on the intersection of capacity and demand curves commonly called the performance point. The modification factors are developed based on the validation of performance points from which are derived demand and capacity curves. Biotechnology and Bioengineering Moderate shear wall near-field excitation performance points modification factors PBSD method Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Introduction The main scope of the performance-based seismic design (PBSD) method is conceptually formed based on the development of structural and component performances with the utilization of the nonlinear static analysis instead of nonlinear dynamic analysis which its goal is to capture a reliable result by reduction of the computational effort. Meanwhile, a rational procedure which can provide a reliable result in an area that a structure is subjected to synchronous vertical (VCE) and horizontal (HCE) earthquake excitations (biaxial excitation) has not been clarified yet ((H. J. JIANG et al., 2011, He and Agrawal, 2008, Ellys Lim and Chouw, 2018, Laila Elhifnawy et al., 2017, Collier and Elanashai, 2001, Naeim, 1998, S., 1997, Huy T. Tran et al., 2016). Researchers indicated that in the near-field area due to intensities of VCE and HCE and change of their characteristics, the interaction between them and distance from a source, performances of components and structures may be considerably affected (Nikolaos Simos et al., 2018, Sinan Akkar et al., 2018, Jalal Kiani et al., 2018, Yang Xiang et al., 2017, Heng Li and Chen, 2017, Laila Elhifnawy et al., 2017, He and Agrawal, 2008, Bozorgnia, 2004, Naeim, 1998, S., 1997, Aki, 1980, Bommer, 2001, Ghatee et al., 2007, Haibo Yang, 2014, Aruna Rawat. et al., 2017). Since VCE effectively exists in near-field area sensitively depending on the distance from a source and the earthquake magnitude (Collier and Elanashai, 2001, Asgarian Behrouz et al., 2012); There are a few methods that the VCEs are commonly transformed to an additional weight which is vertically distributed in different levels of a structure. Correspondingly, the effect of vertical component in demand spectral acceleration response of a single degree of freedom is shown to be significant by a study on the Hyogo-ken Nanbu earthquake (Loh, 1997). And also, Bousias et al. (2002) have shown the effect of near-field on a capacity curve of the column (Bousias, 2002). Since biaxial excitation may also produce different inelastic (damage and/or plastic) surfaces in tensorial material matrices space can produce different elastic/inelastic load-deflection manner (Su and Wong, 2007, Sima et al., 2008); From a different point of view, the performance of components which are governed by biaxial stress in a material matrix such as shear walls and structures consist of them can be impacted significantly by biaxial excitation. The flexibility of shear wall structure is lesser than the other structural systems (actually lesser inter-story drift) but the inter-story drifts can be affected by vertical and horizontal excitations simultaneously. These effects may be amplified when shear wall is to be in moderate size; Walls are considered moderate if their aspect ratio is between 1.5 to 3 (FEMA-273, 1996), hence, they face wide diversity modes of failure (Sheu and Huang, 1992). The capacity-demand evaluation process that is based on the intersection of capacity and demand curves could be affected by considering the static analysis procedure in a near-field area where it has different excitation characteristics corresponding to distance from a source. So, a modified procedure is conceived to ensure the response of the shear wall subjected to biaxial excitation. In spite of all above mentioned, a study by analytical and experimental procedures shows that the nonlinear static analysis may be applicable for the near-field area with the decent response of structure (Wuchuan Pu et al., 2018, Basu, 2007, Ghatee et al., 2009a, Ghatee et al., 2009b, Ghatee et al., 2010). Thus, the present study is formed in such a way to develop modification factor in the nonlinear static procedure to achieve a reliable response. The modification factors are developed based on the validation of a pseudo performance point and the differentiation of pseudo and actual performance points which are derived from demand and capacity curves. The modification factors are formed based on distance from a source and steel ratio of moderate shear wall. Four earthquakes are considered in the following text such as Northridge, Lomaperita, Kobe, and Landers in 3 distances as 0, 15, 30 kilometers. Finally, verification procedures with experimental data have presented that the modification factors can effectively provide reliable outcomes. Materials And Methods Earthquake loading The selection of a suitable set from the collected earthquake events plays an important role in developing general results. As the nonlinear response of a structural system strongly depends on characteristics of motion such as frequency content, magnitude, strong motion duration, and pulse sequencing; therefore, several ground motions representing a range of amplitudes and frequencies should be utilized to anticipate the upper and lower bounds of the nonlinear response. Considering the characteristics of the earthquakes, which are tabulated below, show that the four earthquake records of Northridge, 1994, Loma Prieta, 1989 and Landers, 1992(Iwan and Gates, 1997) Kobe, 1995 are able to generalize the results. Besides the target drift straightly depends on the performance level. So, there should be a reliable statistical study, for instance, the SEAOC has presented the values of earthquake loadings and the corresponding drift targets for the structures, i.e., in Life Safety (LS), the drift is 1.5% while in Collapse Prevention (CP) is 2.5%. Although all these values are not accurate, it has no significant impact in comparison with the actual performance point and the pseudo performance point that is the overall procedure of the present study. It should be noted that for shortening of the sentences in the graphs and tables, the probability of exceedance 2% and 10% per 50 years are demonstrated by 2-50 or 10-50, respectively. It is obvious due to the table that the effect of near-field characteristics on the response of structures almost vanishes after 30 km. Therefore, the performance points are captured in 0, 15, and 30 Km distances. Besides, it is difficult to record the VCE from the exact distances from a source. Hence, the vertical records are scaled based on the statistical values of V/H ratios which are developed based on the 104 worldwide records by Ambraseys et.al. In 1996(Ambraseys, 1996, Peer.Berkeley.edu). Material model When static horizontal and vertical cyclic loadings are imposed on a structure, nonlinear characteristics, and damage aspects of a material matrix within each component may produce different performances (Su and Wong, 2007). In most mathematical models that represent the behavior of concrete, the stress-strain relationship is considered linear in plastic surfaces before damage criteria. Although this definition of concrete behavior can be a good estimation, it can be totally different for the structures and elements subjected to biaxial stress, vertical and horizontal earthquakes, damage state, and damage mode. In other words, a pair of the same component which is affected by different protocols of bidirectional cyclic loadings may have disparate performances due to differences in decreasing stiffness, hardening and/or softening in the material matrix and diminishing of energy absorption capacities because of different cyclic loading protocols (Mo and Chan, 1996, Loh, 1997). All the above mentioned are shown schematically in Figure 1. The performance of components, which are governed by biaxial stress in the material matrix, can be impacted significantly by biaxial excitation. Since biaxial excitation may also produce different inelastic (damage and/or plastic) surfaces in tensorial material matrices space afterward produce different elastic/inelastic load-deflection manner (Su and Wong, 2007, Sima et al., 2008). The nonlinear behavior of the moderate aspect ratio of shear wall is more complex and involves different modes of failure in comparison with the other aspect ratios. This leads to different aspects of damage, behavior, and performance of the shear wall which is correlated with the different aspects of reinforced concrete damage. Hence, an appropriate material model that is able to consider different aspects of damages and failure modes should be utilized in a numerical simulation to produce acceptable performance points. Therefore, a material model has been developed by authors (Ghatee et al., (Ghatee et al., 2018, in press)) which is capable of handling the characteristics of near-field earthquakes (the partial unloading/reloading effects), their effects on concrete material matrices for the multitude of damage modes, and realistic stress-strain relationships of concrete under crack closure-reopening phenomenon, is adopted for this study. This model has been developed based on the damage formulations which has covered all cyclic loading conditions including partial and complete unloading/reloading ones; the program of the model has been generated via ABAQUS which has been published separately (Ghatee et al., 2017, Ghatee et al., 2018, in press) Development of performance points As discussed before, VCE has a significant impact on the behavior of the structure. In order to develop the modification factor this effect should be taken into account by considering it as an additional mass for a preliminary NDA analysis. The design is done with respect to VCE and HCE, distance from a source, and certain reinforcements of moderate shear wall to reach drift target of the top of the wall to a limitation value corresponding to performance level (Botta and Mezzi, 2008). Within the iteration procedure, the mass has changed, from 8950 kg to 9250, which makes the drift change of 0.08m to 0.1m to capture the actual performance point. In this way, to reach the actual performance point, the wall was allowed to reach the drift target which is 1.5% in LS. After the mass causes the drift limitation, the mass and the actual performance point is captured. This procedure is shown in Figure 2. Hereafter the mass is transformed into a force and applied to the shear wall to reach the displacement under a cyclic procedure to displacement target which results in pseudo performance point. For generating pseudo performance points, the nonlinear static analysis is adopted in such a way that the increasing horizontal displacement loading history in presence of constant vertical load in compression state is applied to the top of the shear wall until the drift of the wall reached limitation values (drift targets) in different performance levels. This procedure is shown in Figure 3. With an evaluation of actual and pseudo performance point, the modification factor is developed. Verification procedure Considering the time interval between peak vertical and horizontal accelerations effects on the response of the structure, the first part of the records cannot be dropped. Hence, in the present study, the end part of the records is only dropped out based on the 95 percent of delivered seismic energy. Due to this verification procedure, five experimental results of shear walls (SW21 to SW25) with moderate aspect ratio and different concrete and steel properties are adopted. These shear walls have been monotonically tested by Lefas et.al. (Lefas . L et al., 1990) and the corresponding results have been used also in the verification procedure of the other material models by Vecchio (Vecchio, 1992) and Wu et.al.(Wu et al., 2006). The experiment setup, boundary conditions, material properties, which have been used in the experiments, and finite element models are shown in Figure 5, Tables 2 and 3. The finite element modeling and mesh discretization, which is adopted in the present study, is illustrated in Figure 6. The present material constitutive law was adopted due to simulation. The maximum top displacements of the walls, which have been reached due to experiments procedure in CP are adopted as targets of displacement loadings which are applied to the models via finite element analyses. The target displacements are presented in Table 4. Considering Table 4. All analytical simulations, which have been done via the other or present studies, are developed based on the target displacements with small variations but are not developed based on the exact values. It is because of the fact that the target displacements, which have been developed by experiments, are located in the collapse limit of the shear walls. These targets may cause some numerical instability in finite element simulation based on constitutive law capability, material properties (such as damage indices and steel ratio), convergence rates, number of iterations, and time intervals. The variation of imposed displacement on top of the walls is also presented in Table 4. Results And Discussion In this study, the modification factors are presented in such a way that can be directly applied in capacity curves of moderate shear wall in a modified PBSD procedure. Since the modification factors are developed based on the evaluation procedure of performance points, there is a capacity-demand evaluation process that is based on the intersection of capacity and demand curves commonly called the performance point. Besides the actual performance points can be captured from drift demand curves, if the displacement of the top of the shear wall reaches displacement objectives. While the pseudo performance point can be captured from the push of hysteresis behavior of the shear wall which is illustrated in Figures 7 to 9. The final results are presented in tables 5, 6, and 7. The base shear value of the pseudo performance point should reach the actual one that has been presented in Figure 10. Considering above mentioned, the modified shear capacity of the moderate shear wall is adopted which can be developed based on the following formulation: where;V cm is modified shear capacity, V c is provided shear capacity and M f is modification factor for moderate shear wall subjected to performance level, steel ratio, and distance from a source, respectively. The PBSD method for moderate shear walls is conceptually modified to enhance its accuracy and reliability subjected to near-field excitation. This modification is done by application of some general modification factors which may be applied in the capacity of a moderate shear wall. The modification factor’s values corresponding with different sizes of moderate shear walls are different. Nevertheless, the modification factors, which may be developed by other sizes of moderate shear walls, should be located in a narrow band of variations. This is the most important issue for the achievement of reliability on the estimation of moderate shear wall performance with different size subjected to near-field excitation. The modification factors can also be generated by a trend-surface, which passes through the modification factor points in three-dimensional spaces. The Cartesian axes X, Y, and Z of the three-dimensional spaces are steel ratio, distance from a source, and the modification factors, respectively. The trend-surfaces are calculated by MATLAB software. The generalized modification factors can be achieved by averaging the previously developed modification factors. It is not recommended that the modification factors greater than one to be utilized in the practical case of shear wall design. Because the modified performance points will be located in the greater safety margin limit if the maximum values of the modification factors are adapted equally to one. This issue is just a recommendation to keep a greater safety limit in practical cases. The surfaces of modification factors generally show that the effects of near-field characteristics on the moderate shear walls are reduced if the steel ratios of the moderate shear wall increase and vice versa. It is deduced from the effect of the partial unloading-reloading state of plain concrete on the overall response of the moderate shear wall which is decreased by overcoming the behavior of the steel layer. Moreover, the axial demand loads subjected to one of the distances are commonly decreased, if the steel ratio increases. Since the self-weight of the shear walls are participated in their inelastic responses, while the axial demand forces are calculated based on lumped masses only. Also, the total demand masses subjected to one of the distances are commonly increased, if the steel ratio increases; because the axial force demand of a shear wall with a high steel ratio should be greater than a shear wall with a low steel ratio. Considering the capacity curves, in a condition that the steel ratio of the wall is not small, hardening of steel reinforcement can play an effective role in diminution or even enhance shear wall performance. Considering the terminology of PBSD, the demand force of a component is defined as a minimum requirement of component force resistance, which the component remains in the performance level. When distance goes to a large extent, total demand masses on the top of the moderate shear wall commonly increases, and the axial demand forces of moderate shear walls commonly decrease. It is because, first due to updating of the shear wall design in the iteration procedure, the steel ratios increase. Hence, the weights of shear walls increase; and the axial force demand in close distance to a source should be greater than the far distance from the source. Conclusion The modification of the PBSD method subjected to near-field excitation is successfully developed by directly applying the modification factors in the capacity curves. All of the modification factors are developed based on different sizes (3*3m, 3*8.4m, 5*10m), aspect ratios (2, 2.8), and the same conditions (Northridge earthquake, CP 2-50, ρ=4.4%, distance from a source 15km) can be compared as presented in Table 8. Considering Table 8, there are two comparison procedures between the modification factors for different sizes. The modification factors can be compared, firstly, by the modification factors which have been produced by similar earthquake imposed to the models, secondly, by the general modification factors which have been developed by the response’s average of three different earthquakes loading for generalization. The variations of modification factors in the second comparison procedure are greater than the first comparison procedure (Table 8). Because the comparison procedure should be done in such a way that all boundary conditions to be similar. The second comparison procedure has been presented for a demonstration of the fact that the variations of modification factors in the second comparison procedure are even small. Considering modification factor ratios within Table 8, average variations of the modification factors affected by different size of shear wall in the first comparison procedure (subjected to Northridge earthquakes) are 4.9% (COV=0.18%) in presence of tensile vertical loads and 6.6% (COV=0.33%) in presence of compressive vertical loads. The average variations of the modification factors in the second comparison procedure are 11.4% (COV=0.97%) in presence of tensile vertical loads and 9.9% (COV=0.74%) in presence of compressive vertical loads. Considering the results of the present study, the axial demand forces of moderate shear walls are commonly decreased, when the distance goes to a large value. It is because the axial force demand in close distance to a source should be greater than a far distance from the source. For instance, in Loma Prieta PE-10-50 and steel ratio 4.4%, the axial demand loads subjected to different distances 0, 15, and 30 km are provided as 91245.38, 84313.4, and 70337.64 N, respectively. The minimum of the factors for CP and LS is equal to 0.737 and 0.802, respectively. The values declare that, in the worst condition, the performance point of moderate shear wall can be affected up to 26.3% by near-field characteristics. The modification factors have greater values than 1.0 if the steel ratios are large and the performance level, which is adapted to be, low. I mean that the capacity of moderate shear wall in LS may be increased from provided capacity depending on the distance from a source and steel ratio. In case of a greater modification factor than 1, an increase of the distance from a source may consequently increase the effect of near-field characteristics, when a large amount of steel ratio and the low-performance level such as LS is selected. The effects of near-field characteristics on moderate shear wall are almost reduced when the distance from a source is increased. Clearly, the modification factors should be different, if the size of moderate shear walls is changed. However, based on the fact that the PBSD method is an estimation method, the proposed modification factors are acceptable, if the difference of the values or ratios of the modification factors produced by other moderate aspect ratios below. The results of the present study show that the modification of the PBD method for moderate shear wall should be accounted for in a wide range of steel ratios and distances from a source earthquake if the collapse-prevention performance level has been adopted. However, in life-safety performance level, this modification should be accounted for in a narrow range of low steel ratio intermingled with a small distance from a source of an earthquake. The variations show that the general modification factors, which are presented in the present study in the last part for moderate shear wall, can be utilized for different sizes of moderate shear wall with relatively high confidence (88.6% captured from the maximum case of average variation). References AKI, K. R., P.G. 1980. Quantitative Seismology: Theory and Methods , W.H. Freeman and Company. ARUNA RAWAT., NASEEF UMMER. & VASANT MATSAGAR 2017. Performance of bi-directional elliptical rolling rods for base isolation of buildings under near-fault earthquakes. Advances in Structural Engineering . ASGARIAN BEHROUZ, NOROUZI ANAHITA, A. P. & MASOUD, A. M. 2012. Evaluation of Seismic Performance of Moment Resisting Frames Considering Vertical Component of Ground Motion. Advances in Structural Engineering, 15. BASU, P. C. R., A.D. 2007. Deflection-based method for seismic response analysis of concrete walls: Benchmarking of CAMUS experiment. 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Collection of horizontal components of near field earthquakes and their major characteristics and their Scaled vertical earthquakes records Horizontal earthquakes loading collection (Iwan, 1997, SAC, 1997) and their Scaled vertical earthquakes records 2-50 10-50 Index Record Magnitude (Richter) Distance (Km) PGA (m/s 2 ) PGA (m/Sec 2 ) (Scale factor) Index Record Magnitude (Richter) Distance (Km) PGA (m/s 2 ) PGA (m/Sec 2 ) (Scale factor) d=0 km d=15km d=30km d=0 km d=15 km d=30 km LA26 Northridge, 1994, California, Rinaldi RS 6.7 7.5 9.253 13.01 (2.713) 11.77 (6.61) 10.53 (7.52) LA16 Northridge, 1994, California, Rinaldi RS 6.7 7.5 5.686 8 (1.67) 7.23 (4.06) 6.47 (4.62) LA22 Kobe, 1995, Nishi-Akashi 6.9 3.4 9.027 13.02 (3.58) 11.77 (4.42) 10.53 (6.79) LA12 Loma Prieta, 1989, San-Francisco, Gilroy, Ary 3, Gilroy sewage plant 7 3.5 4.638 6.77 (1.52) 6.12 (2.75) 5.47 (5.89) LA24 Loma Prieta, 1989, San-Francisco, Gilroy, Ary 3, Gilroy sewage plant 7 12 9.509 13.88 (3.11) 12.55 (5.65) 11.22 (12.08) LA07 Landers,1992, Los-Angeles, Barstow-Vineyard & H 7.3 36 4.129 6.23 (3.50) 5.67 (3.19) 5.12 (3.13) Table 5.The results of actual performance points (CP, 2-50 ). CP, 2-50 LS, 10-50 ρ % Distance From Source (km) Northridge Earthquake Kobe Earthquake Loma Prieta Earthquake Northridge Earthquake Lenders Earthquake Loma Prieta Earthquake F V =Vertical Force (N) =m*PGAv F V =Vertical Force (N) =m*PGAv V=Shear demand (N) V=Shear demand (N) M L =Lumped Mass (kg) M L =Lumped Mass (kg) 0.39% 0 km 73788.04 67217.07 60269.74 72828.76 92654.43 77555.09 272803.2 291496.2 308619.8 279057.1 292612.3 311216.4 5671.64 5230.90 4342.20 9092.23 14872.30 11455.70 15 km 63462.75 60364.80 57069.87 67631.59 97070.40 75100.36 286032.1 308129.1 309398.1 280682.6 263551.8 303268.6 5387.33 5128.70 4547.40 9354.30 17120.00 12271.30 30 km 57081.84 58083.05 53328.66 60507.57 85667.84 77610.00 282880.4 278046.6 287481.6 260717.1 250749.6 246407.6 5426.03 5433.40 4753.00 9352.02 16732.00 14188.30 1.05% 0 km 64345.51 34844.06 28330.47 88111.92 129814.50 103039.40 954956.0 954956.0 954956.0 689454.0 954956.0 954956.0 4945.85 2711.60 2041.10 11000.24 20837.00 15220.00 15 km 64482.31 59258.42 51324.48 80253.07 117168.80 97285.36 719993.8 727293.3 797164.3 737016.6 695086.8 745276.5 5473.88 5034.70 4089.60 11100.01 20664.70 15896.30 30 km 62513.10 66974.99 64962.68 72347.54 99904.00 94555.51 765492.5 736372.8 720699.5 809901.7 804038.4 756286.8 5942.31 6265.20 5789.90 11182.00 19512.50 17286.20 4.4% 0 km 21232.32 20575.42 16830.89 69353.94 117978.10 91245.38 700082.0 679436.9 788501.7 756999.1 602647.3 693695.6 1632.00 1601.20 1212.60 8658.42 18937.10 13477.90 15 km 17367.14 19213.35th 15833.08 62070.92 105848.10 84313.40 733671.3 673542.6 775905.9 764031.3 605517.2 676748.2 1474.29 1632.40 1261.60 8585.19 18668.10 13776.70 30 km 15704.89 17928.20 16402.52 52452.10 77152.77 70337.64 799451.0 683448.2 709168.8 678293.8 648212.5 632984.2 1492.86 1677.10 1461.90 8106.97 15068.90 12858.80 Table 6. The results of pseudo performance points ρ % Distance From Source (km) Northridge Earthquake 2-50 10-50 Vertical load V’=Shear capacity Vertical load V’=Shear capacity Compressive vertical load Tensile vertical load Compressive vertical load Tensile vertical load 0.39% 0km ±73788.04 196807 196585 ± 72828.76 202445 215566 15km ±63462.75 196917 200002 ± 67631.59 195664 207579 30km ±57081.84 189580 178111 ± 60507.57 177951 174988 1.05% 0km ±64345.51 666856 671074 ± 88111.92 478382 501197 15km ±64482.31 479674 483077 ± 80253.07 496205 517023 30km ±62513.10 499118 469757 ± 72347.54 543853 537376 4.4 % 0km ±21232.32 499641 500759 ± 69353.94 451084 462507 15km ±17367.14 495775 497023 ± 62070.92 448378 454611 30km ±15704.89 496130 495342 ± 52452.10 486934 462003 Table 8.Calculation of modification factor ratios of moderate shear walls (CP-2-50, ρ=4.4%). Model Size Thick-ness Aspect ratio Modification factor Modification factor ratio Compressive vertical load Tensile vertical load Compressive vertical load Tensile vertical load 1* 3m*6m 0.25m 2 0.78487 0.77554 1 1 2 3m*8.4m 0.3m 2.8 0.84371 0.85259 1.0749678 1.0993501 3 5m*10m 0.35m 2 0.8421 0.8525 1.0729165 1.0992341 Mean of variations%: 1.049295 1.066195 Cov (%): 0.1824 0.3286 1** 3m*6m 0.25m 2 0.98651 0.97975 1 1 2 3m*8.4m 0.3m 2.8 0.84371 0.85259 1.169253 1.149146 3 5m*10m 0.35m 2 0.8421 0.8525 1.171488 1.149267 Mean: 1.11358 1.099471 Cov (%): 0.9677 0.7421 * Northridge earthquake,15km ** General modification factors based on the average of moderate shear wall subjected to different earthquakes 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. 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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-259219","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":13963076,"identity":"62fe8754-85c3-488f-9133-5288ddf0ead2","order_by":0,"name":"Peyman Ghatee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYPACOQYGCSD1AYjZ2InTYszAA9TCOAOkhZkULcw8IDYhLebsZww/3WAwkLeXbj742ObXNnk+ZgbGDx9zcGux7Mkxls5hMDDskTmWbJzbd9uwjZmBWXLmNtxaDA7kGAC1/GHskcgxk87tuc0I1MLGzItPy/k3xr+Bttj3SOR//23Zc9uesJYbQMOBWhKBtgDD6sftRIJaLGc8K7POMTBI7rmRZizZ23A7uY2ZsRmvX8z5kzffzqkwsG2fkfzww48/t23ntzcf/PARn8MYOAxAJAQwtoHJBtzqwVrYHyBx/+BVPApGwSgYBSMUAAACTktUyiv17wAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-9386-7097","institution":"Islamic Azad University Shiraz","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Peyman","middleName":"","lastName":"Ghatee","suffix":""},{"id":13963077,"identity":"c8505d87-b164-42be-b89c-a66555ace050","order_by":1,"name":"Mohd Saleh Jaafar","email":"","orcid":"","institution":"UPM - Serdang: Universiti Putra 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16:13:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-259219/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-259219/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":6580268,"identity":"0fd209cc-7edf-4d7f-8cc5-8c8ffb2640c5","added_by":"auto","created_at":"2021-03-03 22:22:16","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":71957,"visible":true,"origin":"","legend":"Schematically illustration of differences between actual and linearized unloading curves Consideration within an example of biaxial analysis\n","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-259219/v1/86079a99ca0ab4de83cc102e.jpg"},{"id":6579935,"identity":"b6a5195e-7a52-4593-92c3-30dca130ab5e","added_by":"auto","created_at":"2021-03-03 22:19:16","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":103522,"visible":true,"origin":"","legend":"Flowchart of actual performance points determination.","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-259219/v1/d9ce5512ccf43969827a6ba4.jpg"},{"id":6579940,"identity":"fdbc3e93-342a-4960-a213-8b3c9377ec89","added_by":"auto","created_at":"2021-03-03 22:19:16","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":87812,"visible":true,"origin":"","legend":"Flowchart of pseudo performance points and capacity curve determination.","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-259219/v1/37cd8837fc1b3d75f791615e.jpg"},{"id":6579936,"identity":"3f6619ce-9c16-42ff-8fcc-fd40f3d05fab","added_by":"auto","created_at":"2021-03-03 22:19:16","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":53775,"visible":true,"origin":"","legend":"Utilized duration in present study subjected to horizontal component of Northridge earthquake.","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-259219/v1/41d1c7daf5a22ffa50717f66.jpg"},{"id":6580267,"identity":"50f6b350-9868-4d20-848f-648fdc74e4e5","added_by":"auto","created_at":"2021-03-03 22:22:16","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":181464,"visible":true,"origin":"","legend":"Finite element modeling of moderate shear walls by Vecchio (1992)\nAnd its experimental details tested by Lefas et.al (1990)\n","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-259219/v1/ca4008c61ec2469eb943d131.jpg"},{"id":6580472,"identity":"c7e2b8cb-00c6-4456-9d2d-eb2223e5da02","added_by":"auto","created_at":"2021-03-03 22:25:16","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":40802,"visible":true,"origin":"","legend":"Finite element modeling\nThe moderate shear walls in present study.\n","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-259219/v1/85851f1e7a485e1e87e48a95.jpg"},{"id":6580916,"identity":"75d2a7d0-7b84-44fa-a80c-4c492e23000f","added_by":"auto","created_at":"2021-03-03 22:28:16","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":129272,"visible":true,"origin":"","legend":"Hysteresis of the moderate shear wall subjected to\nNorthridge earthquake, PE 2-50(left side), PE 10-50 (right side)\n","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-259219/v1/59a15ae9fb694c698c377c64.jpg"},{"id":6579945,"identity":"78e7c51f-d7d0-4083-9c93-6cd34bad742a","added_by":"auto","created_at":"2021-03-03 22:19:16","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":134908,"visible":true,"origin":"","legend":"Hysteresis of the moderate shear wall subjected to\nNorthridge earthquake, d=15 km, PE 2-50(left side), PE 10-50 (right side)\n","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-259219/v1/4637fb5fd56601a81a1dd3ea.jpg"},{"id":6579942,"identity":"746a3df0-e9e2-425d-a096-a9b7ade0ac8d","added_by":"auto","created_at":"2021-03-03 22:19:16","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":136299,"visible":true,"origin":"","legend":"Hysteresis of the moderate shear wall subjected to\nNorthridge earthquake, d=30 km, PE 2-50(left side), PE 10-50 (right side) \n","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-259219/v1/4ef478d102032006cb60b9d2.jpg"},{"id":6580272,"identity":"ed4cba8c-d410-4cbe-931e-56474de704ad","added_by":"auto","created_at":"2021-03-03 22:22:16","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":103113,"visible":true,"origin":"","legend":"Demand-Capacity curve and modification factors\nFor Northridge earthquake, CP (10-50), steel ratio 4.4%,\nIn presence of tensile vertical load.\n","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-259219/v1/17be225786bdf47c684df7f7.jpg"},{"id":6580474,"identity":"30fbf952-d24d-42de-a9b7-f45ff8312a08","added_by":"auto","created_at":"2021-03-03 22:25:16","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":180924,"visible":true,"origin":"","legend":"Two-dimensional surface of the generalize modification factors\nIn CP (2-50)\n","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-259219/v1/2dc319d8fef8bbcaaa6a2e7d.jpg"},{"id":6580271,"identity":"d72b7b59-66e4-4c18-ab8b-7fd536358f0b","added_by":"auto","created_at":"2021-03-03 22:22:16","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":178796,"visible":true,"origin":"","legend":"Two-dimensional surface of the generalize modification factors\nIn LS (10-50)\n","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-259219/v1/d1e59d34d4f7a62f7378cc91.jpg"},{"id":13674776,"identity":"cfb3cc8d-4854-442a-b275-af85ec9e2be4","added_by":"auto","created_at":"2021-09-17 11:23:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1520398,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-259219/v1/1700b4a2-28bf-4800-bde7-880154824990.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eModification of PBSD Procedure for Moderate Shear Walls Subjected to Near-Field Excitation\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe main scope of the performance-based seismic design (PBSD) method is conceptually formed based on the development of structural and component performances with the utilization of the nonlinear static analysis instead of nonlinear dynamic analysis which its goal is to capture a reliable result by reduction of the computational effort. Meanwhile, a rational procedure which can provide a reliable result in an area that a structure is subjected to synchronous vertical (VCE) and horizontal (HCE) earthquake excitations (biaxial excitation) has not been clarified yet ((H. J. JIANG et al., 2011, He and Agrawal, 2008, Ellys Lim and Chouw, 2018, Laila Elhifnawy et al., 2017, Collier and Elanashai, 2001, Naeim, 1998, S., 1997, Huy T. Tran et al., 2016). Researchers indicated that in the near-field area due to intensities of VCE and HCE and change of their characteristics, the interaction between them and distance from a source, performances of components and structures may be considerably affected (Nikolaos Simos et al., 2018, Sinan Akkar et al., 2018, Jalal Kiani et al., 2018, Yang Xiang et al., 2017, Heng Li and Chen, 2017, Laila Elhifnawy et al., 2017, He and Agrawal, 2008, Bozorgnia, 2004, Naeim, 1998, S., 1997, Aki, 1980, Bommer, 2001, Ghatee et al., 2007, Haibo Yang, 2014, Aruna Rawat. et al., 2017). Since VCE effectively exists in near-field area sensitively depending on the distance from a source and the earthquake magnitude (Collier and Elanashai, 2001, Asgarian Behrouz et al., 2012); There are a few methods that the VCEs are commonly transformed to an additional weight which is vertically distributed in different levels of a structure. Correspondingly, the effect of vertical component in demand spectral acceleration response of a single degree of freedom is shown to be significant by a study on the Hyogo-ken Nanbu earthquake (Loh, 1997). And also, Bousias et al. (2002) have shown the effect of near-field on a capacity curve of the column (Bousias, 2002). Since biaxial excitation may also produce different inelastic (damage and/or plastic) surfaces in tensorial material matrices space can produce different elastic/inelastic load-deflection manner (Su and Wong, 2007, Sima et al., 2008); From a different point of view, the performance of components which are governed by biaxial stress in a material matrix such as shear walls and structures consist of them can be impacted significantly by biaxial excitation. The flexibility of shear wall structure is lesser than the other structural systems (actually lesser inter-story drift) but the inter-story drifts can be affected by vertical and horizontal excitations simultaneously. These effects may be amplified when shear wall is to be in moderate size; Walls are considered moderate if their aspect ratio is between 1.5 to 3 (FEMA-273, 1996), hence, they face wide diversity modes of failure (Sheu and Huang, 1992). The capacity-demand evaluation process that is based on the intersection of capacity and demand curves could be affected by considering the static analysis procedure in a near-field area where it has different excitation characteristics corresponding to distance from a source. So, a modified procedure is conceived to ensure the response of the shear wall subjected to biaxial excitation. In spite of all above mentioned, a study by analytical and experimental procedures shows that the nonlinear static analysis may be applicable for the near-field area with the decent response of structure (Wuchuan Pu et al., 2018, Basu, 2007, Ghatee et al., 2009a, Ghatee et al., 2009b, Ghatee et al., 2010). Thus, the present study is formed in such a way to develop modification factor in the nonlinear static procedure to achieve a reliable response. The modification factors are developed based on the validation of a pseudo performance point and the differentiation of pseudo and actual performance points which are derived from demand and capacity curves. The modification factors are formed based on distance from a source and steel ratio of moderate shear wall. Four earthquakes are considered in the following text such as Northridge, Lomaperita, Kobe, and Landers in 3 distances as 0, 15, 30 kilometers. Finally, verification procedures with experimental data have presented that the modification factors can effectively provide reliable outcomes.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEarthquake loading\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe selection of a suitable set from the collected earthquake events plays an important role in developing general results. As the nonlinear response of a structural system strongly depends on characteristics of motion such as frequency content, magnitude, strong motion duration, and pulse sequencing; therefore, several ground motions representing a range of amplitudes and frequencies should be utilized to anticipate the upper and lower bounds of the nonlinear response. Considering the characteristics of the earthquakes, which are tabulated below, show that the four earthquake records of Northridge, 1994, Loma Prieta, 1989 and Landers, 1992(Iwan and Gates, 1997) Kobe, 1995 are able to generalize the results. Besides the target drift straightly depends on the performance level. So, there should be a reliable statistical study, for instance, the SEAOC has presented the values of earthquake loadings and the corresponding drift targets for the structures, i.e., in Life Safety (LS), the drift is 1.5% while in Collapse Prevention (CP) is 2.5%. Although all these values are not accurate, it has no significant impact in comparison with the actual performance point and the pseudo performance point that is the overall procedure of the present study. It should be noted that for shortening of the sentences in the graphs and tables, the probability of exceedance 2% and 10% per 50 years are demonstrated by 2-50 or 10-50, respectively.\u003c/p\u003e\n\u003cp\u003eIt is obvious due to the table that the effect of near-field characteristics on the response of structures almost vanishes after 30 km. Therefore, the performance points are captured in 0, 15, and 30 Km distances. Besides, it is difficult to record the VCE from the exact distances from a source. Hence, the vertical records are scaled based on the statistical values of V/H ratios which are developed based on the 104 worldwide records by Ambraseys et.al. In 1996(Ambraseys, 1996, Peer.Berkeley.edu).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMaterial model\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen static horizontal and vertical cyclic loadings are imposed on a structure, nonlinear characteristics, and damage aspects of a material matrix within each component may produce different performances (Su and Wong, 2007). In most mathematical models that represent the behavior of concrete, the stress-strain relationship is considered linear in plastic surfaces before damage criteria. Although this definition of concrete behavior can be a good estimation, it can be totally different for the structures and elements subjected to biaxial stress, vertical and horizontal earthquakes, damage state, and damage mode. In other words, a pair of the same component which is affected by different protocols of bidirectional cyclic loadings may have disparate performances due to differences in decreasing stiffness, hardening and/or softening in the material matrix and diminishing of energy absorption capacities because of different cyclic loading protocols (Mo and Chan, 1996, Loh, 1997). All the above mentioned are shown schematically in Figure 1.\u003c/p\u003e\n\u003cp\u003eThe performance of components, which are governed by biaxial stress in the material matrix, can be impacted significantly by biaxial excitation. Since biaxial excitation may also produce different inelastic (damage and/or plastic) surfaces in tensorial material matrices space afterward produce different elastic/inelastic load-deflection manner (Su and Wong, 2007, Sima et al., 2008).\u003c/p\u003e\n\u003cp\u003eThe nonlinear behavior of the moderate aspect ratio of shear wall is more complex and involves different modes of failure in comparison with the other aspect ratios. This leads to different aspects of damage, behavior, and performance of the shear wall which is correlated with the different aspects of reinforced concrete damage. Hence, an appropriate material model that is able to consider different aspects of damages and failure modes should be utilized in a numerical simulation to produce acceptable performance points. Therefore, a material model has been developed by authors (Ghatee et al., (Ghatee et al., 2018, in press)) which is capable of handling the characteristics of near-field earthquakes (the partial unloading/reloading effects), their effects on concrete material matrices for the multitude of damage modes, and realistic stress-strain relationships of concrete under crack closure-reopening phenomenon, is adopted for this study. This model has been developed based on the damage formulations which has covered all cyclic loading conditions including partial and complete unloading/reloading ones; the program of the model has been generated via ABAQUS which has been published separately (Ghatee et al., 2017, Ghatee et al., 2018, in press)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDevelopment of performance points\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs discussed before, VCE has a significant impact on the behavior of the structure. In order to develop the modification factor this effect should be taken into account by considering it as an additional mass for a preliminary NDA analysis. The design is done with respect to VCE and HCE, distance from a source, and certain reinforcements of moderate shear wall to reach drift target of the top of the wall to a limitation value corresponding to performance level (Botta and Mezzi, 2008). Within the iteration procedure, the mass has changed, from 8950 kg to 9250, which makes the drift change of 0.08m to 0.1m to capture the actual performance point. In this way, to reach the actual performance point, the wall was allowed to reach the drift target which is 1.5% in LS. After the mass causes the drift limitation, the mass and the actual performance point is captured. This procedure is shown in Figure 2. Hereafter the mass is transformed into a force and applied to the shear wall to reach the displacement under a cyclic procedure to displacement target which results in pseudo performance point. For generating pseudo performance points, the nonlinear static analysis is adopted in such a way that the increasing horizontal displacement loading history in presence of constant vertical load in compression state is applied to the top of the shear wall until the drift of the wall reached limitation values (drift targets) in different performance levels. This procedure is shown in Figure 3. With an evaluation of actual and pseudo performance point, the modification factor is developed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVerification procedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsidering the time interval between peak vertical and horizontal accelerations effects on the response of the structure, the first part of the records cannot be dropped. Hence, in the present study, the end part of the records is only dropped out based on the 95 percent of delivered seismic energy.\u003c/p\u003e\n\u003cp\u003eDue to this verification procedure, five experimental results of shear walls (SW21 to SW25) with moderate aspect ratio and different concrete and steel properties are adopted. These shear walls have been monotonically tested by Lefas et.al. (Lefas . L et al., 1990) and the corresponding results have been used also in the verification procedure of the other material models by Vecchio (Vecchio, 1992) and Wu et.al.(Wu et al., 2006). The experiment setup, boundary conditions, material properties, which have been used in the experiments, and finite element models are shown in Figure 5, Tables 2 and 3.\u003c/p\u003e\n\u003cp\u003eThe finite element modeling and mesh discretization, which is adopted in the present study, is illustrated in Figure 6. The present material constitutive law was adopted due to simulation. The maximum top displacements of the walls, which have been reached due to experiments procedure in CP are adopted as targets of displacement loadings which are applied to the models via finite element analyses. The target displacements are presented in Table 4.\u003c/p\u003e\n\u003cp\u003eConsidering Table 4. All analytical simulations, which have been done via the other or present studies, are developed based on the target displacements with small variations but are not developed based on the exact values. It is because of the fact that the target displacements, which have been developed by experiments, are located in the collapse limit of the shear walls. These targets may cause some numerical instability in finite element simulation based on constitutive law capability, material properties (such as damage indices and steel ratio), convergence rates, number of iterations, and time intervals. The variation of imposed displacement on top of the walls is also presented in Table 4.\u003c/p\u003e"},{"header":"Results And Discussion","content":"\u003cp\u003eIn this study, the modification factors are presented in such a way that can be directly applied in capacity curves of moderate shear wall in a modified PBSD procedure. Since the modification factors are developed based on the evaluation procedure of performance points, there is a capacity-demand evaluation process that is based on the intersection of capacity and demand curves commonly called the performance point. Besides the actual performance points can be captured from drift demand curves, if the displacement of the top of the shear wall reaches displacement objectives. While the pseudo performance point can be captured from the push of hysteresis behavior of the shear wall which is illustrated in Figures 7 to 9. The final results are presented in tables 5, 6, and 7. The base shear value of the pseudo performance point should reach the actual one that has been presented in Figure 10.\u003c/p\u003e\n\u003cp\u003eConsidering above mentioned, the modified shear capacity of the moderate shear wall is adopted which can be developed based on the following formulation:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"\" /\u003e\u003c/p\u003e\n\u003cp\u003ewhere;V\u003csub\u003ecm\u003c/sub\u003e\u0026nbsp;is modified shear capacity, V\u003csub\u003ec\u003c/sub\u003e\u0026nbsp;is provided shear capacity and M\u003csub\u003ef\u003c/sub\u003e\u0026nbsp;is modification factor for moderate shear wall subjected to performance level, steel ratio, and distance from a source, respectively.\u003c/p\u003e\n\u003cp\u003eThe PBSD method for moderate shear walls is conceptually modified to enhance its accuracy and reliability subjected to near-field excitation. This modification is done by application of some general modification factors which may be applied in the capacity of a moderate shear wall. The modification factor\u0026rsquo;s values corresponding with different sizes of moderate shear walls are different. Nevertheless, the modification factors, which may be developed by other sizes of moderate shear walls, should be located in a narrow band of variations. This is the most important issue for the achievement of reliability on the estimation of moderate shear wall performance with different size subjected to near-field excitation.\u003c/p\u003e\n\u003cp\u003eThe modification factors can also be generated by a trend-surface, which passes through the modification factor points in three-dimensional spaces. The Cartesian axes X, Y, and Z of the three-dimensional spaces are steel ratio, distance from a source, and the modification factors, respectively. The trend-surfaces are calculated by MATLAB software. The generalized modification factors can be achieved by averaging the previously developed modification factors. It is not recommended that the modification factors greater than one to be utilized in the practical case of shear wall design. Because the modified performance points will be located in the greater safety margin limit if the maximum values of the modification factors are adapted equally to one. This issue is just a recommendation to keep a greater safety limit in practical cases.\u003c/p\u003e\n\u003cp\u003eThe surfaces of modification factors generally show that the effects of near-field characteristics on the moderate shear walls are reduced if the steel ratios of the moderate shear wall increase and vice versa. It is deduced from the effect of the partial unloading-reloading state of plain concrete on the overall response of the moderate shear wall which is decreased by overcoming the behavior of the steel layer. Moreover, the axial demand loads subjected to one of the distances are commonly decreased, if the steel ratio increases. Since the self-weight of the shear walls are participated in their inelastic responses, while the axial demand forces are calculated based on lumped masses only. Also, the total demand masses subjected to one of the distances are commonly increased, if the steel ratio increases; because the axial force demand of a shear wall with a high steel ratio should be greater than a shear wall with a low steel ratio.\u003c/p\u003e\n\u003cp\u003eConsidering the capacity curves, in a condition that the steel ratio of the wall is not small, hardening of steel reinforcement can play an effective role in diminution or even enhance shear wall performance. Considering the terminology of PBSD, the demand force of a component is defined as a minimum requirement of component force resistance, which the component remains in the performance level. When distance goes to a large extent, total demand masses on the top of the moderate shear wall commonly increases, and the axial demand forces of moderate shear walls commonly decrease. It is because, first due to updating of the shear wall design in the iteration procedure, the steel ratios increase. Hence, the weights of shear walls increase; and the axial force demand in close distance to a source should be greater than the far distance from the source.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe modification of the PBSD method subjected to near-field excitation is successfully developed by directly applying the modification factors in the capacity curves. All of the modification factors are developed based on different sizes (3*3m, 3*8.4m, 5*10m), aspect ratios (2, 2.8), and the same conditions (Northridge earthquake, CP 2-50, \u0026rho;=4.4%, distance from a source 15km) can be compared as presented in Table 8. Considering Table 8, there are two comparison procedures between the modification factors for different sizes. The modification factors can be compared, firstly, by the modification factors which have been produced by similar earthquake imposed to the models, secondly, by the general modification factors which have been developed by the response\u0026rsquo;s average of three different earthquakes loading for generalization. The variations of modification factors in the second comparison procedure are greater than the first comparison procedure (Table 8). Because the comparison procedure should be done in such a way that all boundary conditions to be similar. The second comparison procedure has been presented for a demonstration of the fact that the variations of modification factors in the second comparison procedure are even small. Considering modification factor ratios within Table 8, average variations of the modification factors affected by different size of shear wall in the first comparison procedure (subjected to Northridge earthquakes) are 4.9% (COV=0.18%) in presence of tensile vertical loads and 6.6% (COV=0.33%) in presence of compressive vertical loads. The average variations of the modification factors in the second comparison procedure are 11.4% (COV=0.97%) in presence of tensile vertical loads and 9.9% (COV=0.74%) in presence of compressive vertical loads. Considering the results of the present study, the axial demand forces of moderate shear walls are commonly decreased, when the distance goes to a large value. It is because the axial force demand in close distance to a source should be greater than a far distance from the source. For instance, in Loma Prieta PE-10-50 and steel ratio 4.4%, the axial demand loads subjected to different distances 0, 15, and 30 km are provided as 91245.38, 84313.4, and 70337.64 N, respectively. The minimum of the factors for CP and LS is equal to 0.737 and 0.802, respectively. The values declare that, in the worst condition, the performance point of moderate shear wall can be affected up to 26.3% by near-field characteristics. The modification factors have greater values than 1.0 if the steel ratios are large and the performance level, which is adapted to be, low. I mean that the capacity of moderate shear wall in LS may be increased from provided capacity depending on the distance from a source and steel ratio. In case of a greater modification factor than 1, an increase of the distance from a source may consequently increase the effect of near-field characteristics, when a large amount of steel ratio and the low-performance level such as LS is selected. The effects of near-field characteristics on moderate shear wall are almost reduced when the distance from a source is increased. Clearly, the modification factors should be different, if the size of moderate shear walls is changed. However, based on the fact that the PBSD method is an estimation method, the proposed modification factors are acceptable, if the difference of the values or ratios of the modification factors produced by other moderate aspect ratios below. The results of the present study show that the modification of the PBD method for moderate shear wall should be accounted for in a wide range of steel ratios and distances from a source earthquake if the collapse-prevention performance level has been adopted. However, in life-safety performance level, this modification should be accounted for in a narrow range of low steel ratio intermingled with a small distance from a source of an earthquake. The variations show that the general modification factors, which are presented in the present study in the last part for moderate shear wall, can be utilized for different sizes of moderate shear wall with relatively high confidence (88.6% captured from the maximum case of average variation).\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003eAKI, K. R., P.G. 1980. \u003cem\u003eQuantitative Seismology: Theory and Methods\u003c/em\u003e, W.H. Freeman and Company.\u003c/p\u003e\n\u003cp\u003eARUNA RAWAT., NASEEF UMMER. \u0026amp; VASANT MATSAGAR 2017. Performance of bi-directional elliptical rolling rods for base isolation of buildings under near-fault earthquakes. \u003cem\u003eAdvances in Structural Engineering\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eASGARIAN BEHROUZ, NOROUZI ANAHITA, A. P. \u0026amp; MASOUD, A. M. 2012. 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Estimating the response of steel structures subjected to vertical seismic excitation: Idealized model and inelastic displacement ratio. \u003cem\u003eEngineering Structures\u003c/em\u003e.\u003c/p\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Collection of horizontal components of near field earthquakes and their major characteristics and their Scaled vertical earthquakes records\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"16\" width=\"683\"\u003e\n\u003cp\u003eHorizontal earthquakes loading collection (Iwan, 1997, SAC, 1997) and their Scaled vertical earthquakes records\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"31\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"6\" width=\"250\"\u003e\n\u003cp\u003e\u003cstrong\u003e2-50\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"8\" width=\"332\"\u003e\n\u003cp\u003e\u003cstrong\u003e10-50\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"31\"\u003e\n\u003cp\u003eIndex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"70\"\u003e\n\u003cp\u003eRecord\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"38\"\u003e\n\u003cp\u003e\u003cstrong\u003eMagnitude\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Richter)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"29\"\u003e\n\u003cp\u003e\u003cstrong\u003eDistance (Km)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"41\"\u003e\n\u003cp\u003e\u003cstrong\u003ePGA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(m/s\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"142\"\u003e\n\u003cp\u003e\u003cstrong\u003ePGA (m/Sec\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;(Scale factor)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"31\"\u003e\n\u003cp\u003eIndex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"83\"\u003e\n\u003cp\u003eRecord\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"38\"\u003e\n\u003cp\u003e\u003cstrong\u003eMagnitude\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Richter)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"28\"\u003e\n\u003cp\u003e\u003cstrong\u003eDistance (Km)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"41\"\u003e\n\u003cp\u003e\u003cstrong\u003ePGA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(m/s\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"111\"\u003e\n\u003cp\u003e\u003cstrong\u003ePGA (m/Sec\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;(Scale factor)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42\"\u003e\n\u003cp\u003ed=0\u003c/p\u003e\n\u003cp\u003ekm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"50\"\u003e\n\u003cp\u003ed=15km\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"50\"\u003e\n\u003cp\u003ed=30km\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"37\"\u003e\n\u003cp\u003ed=0\u003c/p\u003e\n\u003cp\u003ekm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"37\"\u003e\n\u003cp\u003ed=15\u003c/p\u003e\n\u003cp\u003ekm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"37\"\u003e\n\u003cp\u003ed=30\u003c/p\u003e\n\u003cp\u003ekm\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"31\"\u003e\n\u003cp\u003eLA26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003eNorthridge,\u003c/p\u003e\n\u003cp\u003e1994, California, Rinaldi RS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e6.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29\"\u003e\n\u003cp\u003e7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"41\"\u003e\n\u003cp\u003e9.253\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"42\"\u003e\n\u003cp\u003e13.01\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e(2.713)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"50\"\u003e\n\u003cp\u003e11.77\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e(6.61)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"50\"\u003e\n\u003cp\u003e10.53\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e(7.52)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"31\"\u003e\n\u003cp\u003eLA16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003eNorthridge,\u003c/p\u003e\n\u003cp\u003e1994, California, Rinaldi RS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e6.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28\"\u003e\n\u003cp\u003e7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"41\"\u003e\n\u003cp\u003e5.686\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"37\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e(1.67)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"37\"\u003e\n\u003cp\u003e7.23\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e(4.06)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"37\"\u003e\n\u003cp\u003e6.47\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e(4.62)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"31\"\u003e\n\u003cp\u003eLA22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003eKobe,\u003c/p\u003e\n\u003cp\u003e1995, Nishi-Akashi\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e6.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29\"\u003e\n\u003cp\u003e3.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"41\"\u003e\n\u003cp\u003e9.027\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"42\"\u003e\n\u003cp\u003e13.02\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e(3.58)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"50\"\u003e\n\u003cp\u003e11.77\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e(4.42)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"50\"\u003e\n\u003cp\u003e10.53\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e(6.79)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"31\"\u003e\n\u003cp\u003eLA12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003eLoma Prieta, 1989, San-Francisco, Gilroy, Ary 3, Gilroy sewage plant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28\"\u003e\n\u003cp\u003e3.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"41\"\u003e\n\u003cp\u003e4.638\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"37\"\u003e\n\u003cp\u003e6.77\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e(1.52)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"37\"\u003e\n\u003cp\u003e6.12\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e(2.75)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"37\"\u003e\n\u003cp\u003e5.47\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e(5.89)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"31\"\u003e\n\u003cp\u003eLA24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003eLoma Prieta, 1989, San-Francisco, Gilroy, Ary 3, Gilroy sewage plant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"29\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"41\"\u003e\n\u003cp\u003e9.509\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"42\"\u003e\n\u003cp\u003e13.88\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e(3.11)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"50\"\u003e\n\u003cp\u003e12.55\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e(5.65)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"50\"\u003e\n\u003cp\u003e11.22\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e(12.08)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"31\"\u003e\n\u003cp\u003eLA07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003eLanders,1992, Los-Angeles, Barstow-Vineyard \u0026amp; H\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e7.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"41\"\u003e\n\u003cp\u003e4.129\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"37\"\u003e\n\u003cp\u003e6.23\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e(3.50)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"37\"\u003e\n\u003cp\u003e5.67\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e(3.19)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"37\"\u003e\n\u003cp\u003e5.12\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e(3.13)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" alt=\"\" /\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.The results of actual performance points (CP, 2-50\u003c/strong\u003e\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"94\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"234\"\u003e\n\u003cp\u003e\u003cstrong\u003eCP, 2-50\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"255\"\u003e\n\u003cp\u003e\u003cstrong\u003eLS, 10-50\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" width=\"47\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026rho; %\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" width=\"47\"\u003e\n\u003cp\u003e\u003cstrong\u003eDistance From Source (km)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e\u003cstrong\u003eNorthridge Earthquake\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u003cstrong\u003eKobe Earthquake\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u003cstrong\u003eLoma Prieta Earthquake\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u003cstrong\u003eNorthridge Earthquake\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u003cstrong\u003eLenders\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEarthquake\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u003cstrong\u003eLoma Prieta Earthquake\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" width=\"234\"\u003e\n\u003cp\u003e\u003cstrong\u003eF\u003csub\u003eV\u003c/sub\u003e=Vertical Force (N) =m*PGAv\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"255\"\u003e\n\u003cp\u003e\u003cstrong\u003eF\u003csub\u003eV\u003c/sub\u003e=Vertical Force (N) =m*PGAv\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" width=\"234\"\u003e\n\u003cp\u003e\u003cstrong\u003eV=Shear demand (N)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"255\"\u003e\n\u003cp\u003e\u003cstrong\u003eV=Shear demand (N)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" width=\"234\"\u003e\n\u003cp\u003e\u003cstrong\u003eM\u003csub\u003eL\u003c/sub\u003e=Lumped Mass (kg)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"255\"\u003e\n\u003cp\u003e\u003cstrong\u003eM\u003csub\u003eL\u003c/sub\u003e=Lumped Mass (kg)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"9\" width=\"47\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.39%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" width=\"47\"\u003e\n\u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ekm\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e73788.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e67217.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e60269.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e72828.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e92654.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e77555.09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e272803.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e291496.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e308619.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e279057.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e292612.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e311216.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e5671.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e5230.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e4342.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e9092.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e14872.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e11455.70\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"47\"\u003e\n\u003cp\u003e\u003cstrong\u003e15\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ekm\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e63462.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e60364.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e57069.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e67631.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e97070.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e75100.36\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e286032.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e308129.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e309398.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e280682.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e263551.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e303268.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e5387.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e5128.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e4547.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e9354.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e17120.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e12271.30\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"47\"\u003e\n\u003cp\u003e\u003cstrong\u003e30 km\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e57081.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e58083.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e53328.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e60507.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e85667.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e77610.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e282880.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e278046.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e287481.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e260717.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e250749.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e246407.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e5426.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e5433.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e4753.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e9352.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e16732.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e14188.30\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"9\" width=\"47\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.05%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" width=\"47\"\u003e\n\u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ekm\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e64345.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e34844.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e28330.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e88111.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e129814.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e103039.40\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e954956.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e954956.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e954956.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e689454.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e954956.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e954956.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e4945.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e2711.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e2041.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e11000.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e20837.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e15220.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"47\"\u003e\n\u003cp\u003e\u003cstrong\u003e15\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ekm\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e64482.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e59258.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e51324.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e80253.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e117168.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e97285.36\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e719993.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e727293.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e797164.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e737016.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e695086.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e745276.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e5473.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e5034.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e4089.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e11100.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e20664.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e15896.30\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"47\"\u003e\n\u003cp\u003e\u003cstrong\u003e30 km\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e62513.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e66974.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e64962.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e72347.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e99904.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e94555.51\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e765492.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e736372.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e720699.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e809901.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e804038.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e756286.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e5942.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e6265.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e5789.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e11182.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e19512.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e17286.20\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"9\" width=\"47\"\u003e\n\u003cp\u003e\u003cstrong\u003e4.4%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" width=\"47\"\u003e\n\u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ekm\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e21232.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e20575.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e16830.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e69353.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e117978.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e91245.38\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e700082.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e679436.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e788501.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e756999.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e602647.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e693695.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e1632.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e1601.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e1212.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e8658.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e18937.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e13477.90\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"47\"\u003e\n\u003cp\u003e\u003cstrong\u003e15\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ekm\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e17367.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e19213.35th\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e15833.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e62070.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e105848.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e84313.40\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e733671.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e673542.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e775905.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e764031.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e605517.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e676748.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e1474.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e1632.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e1261.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e8585.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e18668.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e13776.70\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"47\"\u003e\n\u003cp\u003e\u003cstrong\u003e30 km\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e15704.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e17928.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e16402.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e52452.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e77152.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e70337.64\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e799451.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e683448.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e709168.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e678293.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e648212.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e632984.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e1492.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e1677.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e1461.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e8106.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e15068.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e12858.80\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6. The results of pseudo performance points\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" width=\"54\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026rho; %\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003eDistance From Source (km)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"6\" width=\"435\"\u003e\n\u003cp\u003e\u003cstrong\u003eNorthridge Earthquake\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" width=\"227\"\u003e\n\u003cp\u003e\u003cstrong\u003e2-50\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"208\"\u003e\n\u003cp\u003e\u003cstrong\u003e10-50\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003eVertical load\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"161\"\u003e\n\u003cp\u003eV\u0026rsquo;=Shear capacity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003eVertical load\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"142\"\u003e\n\u003cp\u003eV\u0026rsquo;=Shear capacity\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003eCompressive vertical load\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003eTensile vertical load\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003eCompressive vertical load\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eTensile vertical load\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"54\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.39%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003e0km\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026plusmn;73788.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e196807\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e196585\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e72828.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e202445\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e215566\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003e15km\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026plusmn;63462.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e196917\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e200002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e67631.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e195664\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e207579\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003e30km\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026plusmn;57081.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e189580\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e178111\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e60507.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e177951\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e174988\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"54\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.05%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003e0km\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026plusmn;64345.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e666856\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e671074\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e88111.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e478382\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e501197\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003e15km\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026plusmn;64482.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e479674\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e483077\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e80253.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e496205\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e517023\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003e30km\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026plusmn;62513.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e499118\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e469757\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e72347.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e543853\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e537376\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"54\"\u003e\n\u003cp\u003e\u003cstrong\u003e4.4\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd 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\" alt=\"\" /\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8.Calculation of modification factor ratios of moderate shear walls (CP-2-50, \u0026rho;=4.4%).\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"32\"\u003e\n\u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"59\"\u003e\n\u003cp\u003e\u003cstrong\u003eSize\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"47\"\u003e\n\u003cp\u003e\u003cstrong\u003eThick-ness\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"38\"\u003e\n\u003cp\u003e\u003cstrong\u003eAspect ratio\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"161\"\u003e\n\u003cp\u003e\u003cstrong\u003eModification factor\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"189\"\u003e\n\u003cp\u003e\u003cstrong\u003eModification factor ratio \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eCompressive vertical load\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eTensile vertical load\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eCompressive vertical load\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eTensile vertical load\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"32\"\u003e\n\u003cp\u003e\u003cstrong\u003e1*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e3m*6m\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.25m\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.78487\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.77554\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"32\"\u003e\n\u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e3m*8.4m\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.3m\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e2.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.84371\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.85259\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e1.0749678\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.0993501\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"32\"\u003e\n\u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e5m*10m\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.35m\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.8421\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.8525\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e1.0729165\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.0992341\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" width=\"337\"\u003e\n\u003cp\u003eMean of variations%:\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e1.049295\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.066195\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" width=\"337\"\u003e\n\u003cp\u003eCov (%):\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.1824\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.3286\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"32\"\u003e\n\u003cp\u003e\u003cstrong\u003e1**\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e3m*6m\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.25m\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.98651\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.97975\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"32\"\u003e\n\u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e3m*8.4m\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.3m\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e2.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.84371\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.85259\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e1.169253\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.149146\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"32\"\u003e\n\u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e5m*10m\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"47\"\u003e\n\u003cp\u003e0.35m\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.8421\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.8525\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e1.171488\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.149267\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" width=\"337\"\u003e\n\u003cp\u003eMean:\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e1.11358\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.099471\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" width=\"337\"\u003e\n\u003cp\u003eCov (%):\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.9677\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.7421\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\" width=\"526\"\u003e\n\u003cp\u003e* Northridge earthquake,15km\u003c/p\u003e\n\u003cp\u003e** General modification factors based on the average of moderate shear wall subjected to different earthquakes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"Moderate shear wall, near-field excitation, performance points, modification factors, PBSD method","lastPublishedDoi":"10.21203/rs.3.rs-259219/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-259219/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the near-field areas, VCE effectively exists which is so sensitive depending on the distance from a source and the earthquake magnitude. This has remarkable effects when shear wall is to be in moderate size which causes it to face wide diversity modes of failure. So, a modified procedure is conceived to ensure the response of the shear wall subjected to biaxial excitation. Thereby, there is an evaluation process that is based on the intersection of capacity and demand curves commonly called the performance point. The modification factors are developed based on the validation of performance points from which are derived demand and capacity curves.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Modification of PBSD Procedure for Moderate Shear Walls Subjected to Near-Field Excitation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-03-03 22:19:14","doi":"10.21203/rs.3.rs-259219/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":"88317420-acc8-4bc6-92a0-9e2a821086df","owner":[],"postedDate":"March 3rd, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":2731811,"name":"Biotechnology and Bioengineering"}],"tags":[],"updatedAt":"2021-04-04T03:59:03+00:00","versionOfRecord":[],"versionCreatedAt":"2021-03-03 22:19:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-259219","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-259219","identity":"rs-259219","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}