Seismic performance assessment of elliptic braced frames incorporating XADAS dampers

Seismic performance assessment of elliptic braced frames incorporating XADAS dampers | 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 Seismic performance assessment of elliptic braced frames incorporating XADAS dampers Arefeh Sadat Faalnazari, Abbas Haghollahi, Mohammad Hadi Bagherinejad This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7722571/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract The elliptic brace is a new lateral resistance system recently presented. This paper investigates the performance of a yielding damper named X-shaped added damping and stiffness (XADAS) on the elliptic braced frame. For this purpose, 12 XADAS dampers with different numbers of plates, dimensions, and thicknesses were utilized on three braced frames. Also, three sections, including BOX 60×4, BOX 80×6, and BOX 100×10, were considered for the braces. The models were subjected to a cyclic loading and a non-linear static analysis. Three parameters, dissipated energy, fracture tendency and response modification factor, were evaluated to compare the models. The numerical analysis illustrated that the frames without dampers dissipate more energy at the end of loading due to higher stiffness, but they also experience more failures. Therefore, the models were compared at an equal value of dissipated energy and fracture tendency for a correct comparison. In comparing the models with the same fracture tendency, it was observed that the frame with a damper can dissipate up to 93% more energy than the model without a damper. The models with 15 cm-high dampers have experienced less damage, and in the models equipped with 9 cm-high dampers, more damage has been transferred to the dampers. The results showed that the model braced by the BOX 80×6 and equipped with the damper that the number, height and thickness of plates are respectively equal to 6, 15cm and 8mm has the best performance regarding dissipated energy, fracture tendency and response modification factor. Elliptical brace Yielding damper Nonlinear analysis Dissipated energy Fracture tendency Response modification factor Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 Introduction Structures usually remain in the elastic range during low and moderate earthquakes and enter the plastic range when they are under the effect of a severe earthquake. Ductility and energy absorption are the important indexes that show the ability of structures against severe earthquakes. These indexes are revealed when the structures enter the plastic zone and plastic hinges are formed (Elnashai & Di Sarno, 2015 ). As a result, a significant amount of earthquake energy is absorbed by the local plastic hinges in the lateral resisting system of structures. The members that enter the plastic zone after the earthquake must be repaired or replaced, which is difficult or impossible. If it is possible to concentrate the absorption of earthquake energy in special devices such as dampers, the subsequent damage to the main elements of the structure will be reduced and the possibility of operation after the earthquake will be provided (Di Sarno & Elnashai, 2005 , 2009 ; Lu et al., 2023 ). There are various passive control methods for retrofitting structures, such as damper (Ghasemi Jouneghani et al., 2024 ; Khatibinia et al., 2019 ; Naderpour et al., 2020 ; Nouri et al., 2024 ). The Added Damping And Stiffness (ADAS) system (Whittaker et al., 1988 ) is a passive control system consisting of integrated parallel steel plates that significantly increase the damping of a structure by inelastic deformation of the steel plates (Soong & Dargush, 1997 ; Yang et al., 2023 ). The ADAS dampers act as structural fuses, preventing damage to other members by focusing on their nonlinear behavior. One of the advantages of ADAS dampers is the ability to replace and operate after an earthquake. Also, the structure can return to its initial strength and stiffness before (Tsai et al., 1993 ). XADAS and TADAS with X-shaped and triangular plates have a better performance in energy dissipation than dampers with rectangular plates (ADAS). In ADAS, only two ends of the rectangular plate are yielded, but in X-shaped and triangular plates, the entire height of the plate is yielded, and the entire volume of steel can participate in energy dissipation (Khatibinia et al., 2021 ; Xia & Hanson, 1992 ). The plates of TADAS dampers have sharp corners in their narrowest part, which causes stress concentration and then a failure in this area (TahamouliRoudsari et al., 2018 ). Many experimental and numerical papers have been presented on the behavior of XADAS dampers. Also, the overall performance of the structure has been investigated by combining the XADAS dampers with different types of lateral resistance systems. Sajjadi Alehashem et al. ( 2008 ) examined various multi-story concentric bracing frames (CBF), Chevron, and eccentric bracing frames (EBF) equipped with the TADAS and XADAS dampers under different earthquakes. The period of the first mode of structures equipped with XADAS and TADAS dampers was obtained as 1.39 and 1.23, respectively, which is more than other frames. The amount of base shear force created in systems equipped with dampers was reduced by more than 50% compared to other investigated systems in different earthquakes. Also, the acceleration of the roof in systems with these types of dampers was less compared to other systems. In comparing the frames equipped with XADAS and TADAS dampers, the base shear force and the acceleration of the roof in the systems with XADAS were lower than those with TADAS. Wu et al. ( 2012 ) designed and tested an XADAS damper and evaluated the seismic performance of the frames equipped with this damper and observed that energy absorption was higher in the structures with dampers and the members' damage was reduced compared to structures without dampers. TahamouliRoudsari et al. ( 2018 ) tested several types of XADAS and TADAS dampers on one-story and one-bay concrete frames with Chevron braces. The effective stiffness, final strength, ductility, and strength reduction factor of all frames equipped with dampers were increased several times compared to the reference frame. They illustrated that the dissipated energy in the structure equipped with the XADAS damper is 25% more than the structure with TADAS. Also, the structure with XADAS has more ductility, strength, and strength reduction factors than that with TADAS. Recently, new forms of braces in structures have been considered. The elliptic braced resisting frame (ELBRF) is a modern lateral bracing system introduced in 2016 by Ghasemi and Haghollahi. The elliptical form of the brace dissipates considerable energy, which is one advantage of this structural bracing system. Also, using an elliptical brace allows the placement of windows and even doors in the place of the brace. In the comparison between the new ELBRF and conventional frames, such as the special moment resisting frame (SMRF), the X-braced frame, the V-braced frame (Inverted V-braced CBF), and the knee-braced resisting frame (KBRF), the results illustrated that the energy absorption of ELBRF system is up to 9 times more than other systems. Moreover, compared to SMRF and KBRF, the lateral stiffness of the elliptical brace is higher, and compared to X-braced CBF and Inverted V-braced CBF, it has similar or lower stiffness. The plastic hinges in conventional frames develop in beams, columns, or braces. In contrast, in ELBRF, the plastic hinges only occur in the braces (Ghasemi Jouneghani et al., 2022 ; Ghasemi Jouneghani et al., 2023 ; Ghasemi Jouneghani et al., 2016 ). With the increase in the number of stories in ELBRF, despite the other investigated structures, plastic hinges will be formed in the upper stories, and this indicates the beginning of the process of destruction from the upper stories (Ghasemi Jouneghani et al., 2019 ; Ghasemi Jouneghani et al., 2021 ). Also, the hysteresis diagram of ELBRFs is stable, and there is no pinching. No decrease in the stiffness and strength has been observed in the curves up to 5% drift (Ghasemi Jouneghani & Haghollahi, 2020a ; Ghasemi Jouneghani et al., 2020b ). Adding brackets at the corners of the brace results in a 15% increase in energy absorption capacity and a 10% increase in lateral stiffness. Different models of ELBRFs are 10–15% heavier than the X-bracing system, but their ductility and energy absorption are more than the X-bracing system (Ghasemi Jouneghani & Haghollahi, 2020c ). To evaluate the demand and capacity of the ELBRF system, incremental dynamic analysis (IDA) was performed. The ductility and overstrength factor are 2.1 and 3.1, respectively. Based on the ultimate limit state and allowable stress methods, the response modification factor is 6.5 and 9.5, respectively (Ghasemi Jouneghani & Haghollahi, 2020d ). The comprehensive experimental and numerical studies on elliptic bracing prove the appropriate seismic and cyclic performance of elliptic bracing. Structures designed against earthquakes must have sufficient stiffness and ductility to control deformation and prevent any possible damage. Stiffness and ductility are two characteristics in structures that must be balanced. It is better to design a structural system that combines these characteristics best considering the costs. As mentioned, adding a damper is an effective way to provide ductility in structures. Also, a proper bracing system combined with a damper can simultaneously improve the resistance and ductility of structures. Since the high effect of XADAS dampers is known in different structures, the elliptic braced resisting frame equipped with an XADAS damper (ELBRF-XADAS) can be used as a new seismic system in structures. Based on the advantages of the XADAS damper that were stated in the articles, this paper evaluates its effect on ELBRF. In the second section of this article, the numerical modeling and calibration with the experimental results are discussed. In the third part, the scenarios and parameters studied are presented. In the fourth part, the analysis results and the selection of the best model are discussed. 2 Modeling of structural elements and calibration In this study, ABAQUS software was used for numerical modeling. Experimental models of XADAS and ELBRF were utilized for validation and calibrations. 2.1 Modeling of damper An XADAS damper proposed by Wu 2012 was modeled and calibrated in ABAQUS. The geometric specifications of the model are shown in Fig. 1 a. The modulus of elasticity, Poisson ratio, and ultimate strain are 200 GPa, 0.3, and 0.2, respectively. The yield and ultimate stress ( F y , F u ) are 160 MPa and 250 MPa, respectively. For modeling, the X-shaped plates were connected to the upper and lower rectangular plates using tie connections (Bagherinejad & Haghollahi, 2020a ). The upper rectangular plate was fixed in all directions of displacement and rotation, while the lower plate was subjected to the cyclic load specified in Fig. 1 b. All parts were meshed using 4-node shell elements (S4R) (Bagherinejad & Haghollahi, 2020b ). The mesh size for the rectangular and X-shaped plates was 20 mm and 8 mm, respectively. The isotropic hardening model was applied to the nonlinear behavior of steel materials. The model was analyzed using nonlinear static analysis and the nonlinearity of geometry and material were considered. Figure 1 c shows the Von Mises stress contour at the end of loading. Figure 1 d shows the hysteresis diagram results of the numerical analysis performed with the ABAQUS software compared to the experimental test results. The numerical results illustrate a good match and accuracy of the numerical modeling compared to experimental results. 2.2 Modeling of ELBRF Herein, the frame proposed by Ghasemi Jouneghani and Haghollahi ( 2020c ) and shown in Fig. 2a, is presented to validate the modeling of an ELBRF. The dimensions of the frame and brace are provided in Fig. 2a. Figure 2. ELBRF model, a) Dimensions (in millimeter), b) ATC-24 loading pattern, c) Comparison of hysteresis behavior for numerical and experimental models, d) Deformed shape of numerical model, e) Deformed shape of experimental model(Ghasemi Jouneghani & Haghollahi, 2020a ) The material's Poisson ratio is 0.3, and its ultimate strain is 0.2. Other mechanical characteristics of the materials are listed in Table 1 . The loading pattern in all frames follows the ATC24 1992 loading protocol (see Fig. 2b). The target displacement is 78 mm, corresponding to a 5.1% drift. Isotropic hardening was employed to study the cyclic behavior of materials (Maurya et al. 2013). All parts were meshed using a 4-node shell element (S4R). The beam, column, and brace mesh sizes were considered 30 mm, 30 mm, and 20 mm, respectively. The hysteresis diagrams of the numerical frame are compared with the experimental in Fig. 2c. Figures 2d and 2e shows the deformation of numerical and experimental models at the end of loading. The figures indicate a good agreement between the numerical and experimental results. Table 1 Mechanical characteristics of materials Steel material Elastic modulus (GPa) Static yield (MPa) Static ultimate (MPa) Beam & column (HEB 160) 203.2 350 450 Brace (BOX) 205.6 250 400 Plate (Thk. = 10 mm) 205.6 250 400 Plate (Thk. = 20 mm) 202.3 250 400 Damper (X plate) 205.6 250 400 3 Scenarios and parameters studied In this section, the cyclic behavior of the XADAS damper on the ELBRF frame is investigated through the simulations in ABAQUS. For this purpose, various scenarios are created to study the installation of dampers on the ELBRF frames. In these scenarios, the dimensions and cross-sections of the beam and columns are kept constant while three different sizes of BOX 60×4, BOX 80×6, and BOX 100×10 are considered for the cross-section of the brace. According to the previous research (Solaimani Nezhad & Mahmoudi, 2021 ; TahamouliRoudsari et al., 2018 ), the number of damper plates is 4 and 6, the height of dampers is 150 mm and 90 mm (TahamouliRoudsari et al., 2018 ), and the thickness of damper plates is 4, 6, and 8 mm. The damper height greater than 150 mm was not considered due to creating an inappropriate elliptical shape. Figure 3 a illustrates the dimensions of each damper plate. Various parameters, including the number, thickness, and height of plates, are defined in Table 2 . The naming of models follows the pattern depicted in Fig. 3 b. The damper is connected to the beam and brace in the upper part of the brace with two upper and lower plates using a tie connection. Figure 3 c shows one of the models made in the ABAQUS software. Table 2 Specifications of the proposed models XADAS Damper No. Model name Brace section Plate number Plate thickness (mm) Plate height (mm) 1 F60 BOX 60×4 - - - 2 F60n4t4h15 BOX 60×4 4 4 15 3 F60n4t6h15 BOX 60×4 4 6 15 4 F60n4t8h15 BOX 60×4 4 8 15 5 F60n6t4h15 BOX 60×4 6 4 15 6 F60n6t6h15 BOX 60×4 6 6 15 7 F60n6t8h15 BOX 60×4 6 8 15 8 F60n4t4h9 BOX 60×4 4 4 9 9 F60n4t6h9 BOX 60×4 4 6 9 10 F60n4t8h9 BOX 60×4 4 8 9 11 F60n6t4h9 BOX 60×4 6 4 9 12 F60n6t6h9 BOX 60×4 6 6 9 13 F60n6t8h9 BOX 60×4 6 8 9 14 F80 BOX 80×6 - - - 15 F80n4t4h15 BOX 80×6 4 4 15 16 F80n4t6h15 BOX 80×6 4 6 15 17 F80n4t8h15 BOX 80×6 4 8 15 18 F80n6t4h15 BOX 80×6 6 4 15 19 F80n6t6h15 BOX 80×6 6 6 15 20 F80n6t8h15 BOX 80×6 6 8 15 21 F80n4t4h9 BOX 80×6 4 4 9 22 F80n4t6h9 BOX 80×6 4 6 9 23 F80n4t8h9 BOX 80×6 4 8 9 24 F80n6t4h9 BOX 80×6 6 4 9 25 F80n6t6h9 BOX 80×6 6 6 9 26 F80n6t8h9 BOX 80×6 6 8 9 27 F100 BOX 100×10 - - - 28 F100n4t4h15 BOX 100×10 4 4 15 29 F100n4t6h15 BOX 100×10 4 6 15 30 F100n4t8h15 BOX 100×10 4 8 15 31 F100n6t4h15 BOX 100×10 6 4 15 32 F100n6t6h15 BOX 100×10 6 6 15 33 F100n6t8h15 BOX 100×10 6 8 15 34 F100n4t4h9 BOX 100×10 4 4 9 35 F100n4t6h9 BOX 100×10 4 6 9 36 F100n4t8h9 BOX 100×10 4 8 9 37 F100n6t4h9 BOX 100×10 6 4 9 38 F100n6t6h9 BOX 100×10 6 6 9 39 F100n6t8h9 BOX 100×10 6 8 9 4 Results of parametric studies The following sections present the results of nonlinear analyses. Figure 4 a shows hysteresis diagrams Models with identical bracing cross-sections are grouped in a series. For example, the F60 series includes the models with the BOX 60×4 brace. Next, to compare the nonlinear behavior of the models, three parameters of energy dissipation, fracture tendency, and response modification factor of the frames have been measured and compared. 4.1 Comparison of energy dissipation The area under the force-displacement diagram equals the amount of energy dissipation. The graphs depicting the energy dissipation in each model are illustrated in Fig. 4 b. The values of energy dissipation at the end of loading are presented in Table 3 . As shown, the energy dissipation increases when the number of damper plates and their thickness increase. Furthermore, by comparing the energy dissipation of the models with the same damper cross-section area (for example, n4t6 and n6t4), it is indicated that n4t6 dampers dissipated more energy than n6t4 dampers. This proves that the plate thickness affects energy dissipation more than the number of plates. At the end of loading, models without dampers dissipated more energy than models with dampers due to the higher stiffness of these models than models with dampers. Among the models with dampers in the F60, F80, and F100 series, respectively, the F60n6t8h15, F80n6t8h9, and F100n6t8h9 models have the highest amount of energy dissipation at the end of loading. The F100 model has dissipated the most energy compared to other models. The F60n4t4h15 model failed before reaching the target displacement and could not withstand more cyclic load, which indicates the weakness of this damper in comparison to the F60 frame stiffness. 4.2 Fracture tendency In this study, the equivalent plastic strain criterion (PEEQ) is used as a failure criterion and fracture tendency (Bagherinejad & Haghollahi, 2018 ). According to Eq. ( 1 ), the equivalent plastic strain (PEEQ) checks the tendency to fracture and local ductility. The equivalent strain is a scalar quantity that shows the cumulative values and accumulation of strains at any point in the structure (ABAQUS, 2014 ; Wang et al., 2015 ). $$\:PEEQ={\dot{\:\stackrel{\sim}{\epsilon\:}\:}}^{pl}\left|\genfrac{}{}{0pt}{}{}{0}\right.+\underset{0}{\overset{t}{\int\:}}{\dot{\:\stackrel{\sim}{\epsilon\:}\:}}^{pl}dt\:.\:{\dot{\stackrel{\sim}{\epsilon\:}\:}}^{pl}=\sqrt{\frac{2}{3}{\dot{\epsilon\:}}^{pl}.{\dot{\epsilon\:}}^{pl}}$$ 1 Where \(\:{\dot{\epsilon\:}}^{pl}\) is the rate of plastic flow, \(\:\dot{\stackrel{\sim}{\epsilon\:}\:}\) is the equivalent plastic strain rate, \(\:{\dot{\stackrel{\sim}{\epsilon\:}\:}}^{pl}\left|\genfrac{}{}{0pt}{}{}{0}\right.\) is the initial equivalent plastic strain rate and t is the time duration of the analysis. PEEQ values at the end of loading are presented in Table 3 . The results show that in the F60 series models, the highest damage occurred in the F60n4t4h15 model, while the lowest occurred in the F60n4t6h15 or F60n6t4h15 models. In the F80 series models, the highest damage happened in the F80n4t4h9 model, and the lowest occurred in the F80n6t4h15 model. For the F100 series models, the highest damage is in the F100n4t8h9 model, and the lowest is in the F100n6t6h15 model. Overall, the highest damage across all models is in the F100n4t8h9 model, and the lowest is in the F80n6t4h15 model. In the F60, F80, and F100 model series, the damper can reduce damage by 14%, 61%, and 30%, respectively. Figure 5 shows the deformed frames and their PEEQ contour at the end of loading. In the models without dampers, the maximum PEEQ value occurred in the middle of each quarter of the elliptical brace, where the brace connects to the beam and columns. In some models with dampers, in addition to the reduced PEEQ value, the maximum damage occurred only in the damper. It indicates the proper performance of the damper as a fuse. Also, no damage was observed at the beam-brace and column-brace connections. Additionally, based on the comparison of PEEQ values in Table 3 , it can be concluded that the models with 15cm-high dampers experienced less damage at the end of loading than those with 9cm-high. Moreover, in the models with 9cm-high dampers, the max PEEQ occurred in the dampers, which avoided damage to structural components. Generally, the F80 series models with a 15cm-high damper have the lowest fracture tendency. This indicates these dampers' proper performance and compatibility with the bracing cross-section. 4.3 Comparison of the behavior of models at the same energies When the stiffness of a structure is reduced and a displacement is applied to it, it is evident that the energy spent for displacement is reduced. Therefore, the energy dissipation is also reduced when a damper is added to a structure. Now, it should be checked that when two structures have the same energy dissipation, which one suffers less damage? And vice versa, when two structures have the same damage, which one has dissipated more energy. In general, it can be concluded from the cyclic behavior of the two structures that the structure with less damage is better if the energy absorbed in both structures is equal. Also, the structure with more energy dissipation is better if the damage in both structures is equal. Herein, for a comprehensive comparison, the minimum dissipated energy value is used as the base energy for each series to compare the models. The Maximum PEEQ in Equal Energy Dissipation value (MPEED) was assessed for each model, and the results are detailed in Table 3 . As shown, it is illustrated that at the specified energy level (minimum energy), the damage rate has significantly increased with the decrease in the height of the damper. This indicates that at this energy level, frames with shorter dampers suffer more damage due to the occurrence of plastic hinges. Also, increasing the thickness and number of damper plates will result in more significant damage in the frames with lower-height dampers. The F80n6t4h15 model has the lowest PEEQ in an equal energy value. Table 3. Maximum energy dissipation and PEEQ values In the previous experimental and numerical study on ELBRF, it was observed that important elements of the frame (including the plates connecting the elliptical brace to the beam and columns) experienced considerable stress that led to cracks and damage. By equipping the frames with XADAS dampers, a significant reduction in the fracture tendency (PEEQ) at the connecting plates could be observed. Also, the fracture tendency transfers to the dampers. Figure 6 a displays the cracks and damage in the experimental model from the previous study without dampers (Ghasemi Jouneghani and Haghollahi 2020a ). In contrast, Fig. 6 b shows the images of the same parts in the numerical model with an XADAS damper. 4.4 Comparison of the behavior of models in the same PEEQ As described in the previous section, the minimum PEEQ value (PEEQ = 2.21) is considered the baseline PEEQ to compare the energy dissipation in the models. The Maximum Energy Dissipation in Equal PEEQ (MEDEP) is evaluated for each model, and the results are presented in Fig. 8 a. In the equal PEEQ, the maximum energy dissipation in the F60, F80, and F100 series in models without dampers is 43.1, 73.9, and 106 ton-m, respectively. With the placement of dampers, the maximum energy dissipation reaches 59.4, 142.6, and 98.3 ton-m. As it is apparent in the diagrams, with the placement of a 15cm-high damper in the F60 and F80 series models, the energy dissipation has increased, while it is the opposite in the F100 models. In the F80 series models with 15cm-high dampers, the MEDEP has increased by 93% compared to the model without dampers. This demonstrates the superior performance of this brace cross-section with this form of damper compared to the other models. 4.5 Comparison of response modification factor (R) The ductility of a structure refers to its ability to absorb energy. This energy absorption capacity allows the reduction of the response spectrum of linear elastic design using the response modification factor ( R ). The response modification factor of a model under the cyclic loading can be calculated by obtaining push of the cyclic curve and the equivalent bilinear curve. In designing structures, the level of V 0 (maximum base shear) is reduced to the level of V s , which corresponds to the formation of the first plastic hinge. This level is commonly referred to as the "first significant yield" level, and after that, the behavior of the structure and its responses gradually become inelastic (NEHRP, 1988 ). As shown in Fig. 7 , to bilinearize the curve by regarding FEMA 356 (2000), V y (effective yield strength) should be selected according to the intersection between the original nonlinear analysis curve and the first segment of the bilinear curve (0.6 V y ) and as a result, the area under the original nonlinear analysis curve should equal the bilinear curve (FEMA, 2000 ). The R factor in the Load and Resistance Factor Design (LRFD) method is determined by using Eq. ( 2 ): $$\:R=\frac{{V}_{E}}{{V}_{s}}$$ 2 Where V E is the elastic design force and V s is the first significant yield force. In Fig. 8 b, the push of cyclic curves is presented and the values of the R are calculated. Figure 8 c displays the calculated values of R for each model. Among the models with the BOX100 for the brace, the F100n6t6h15 model has the highest response modification factor value of 7.57, representing a 15% increase compared to its value in the model without a damper (6.12). Among the models with a BOX80 bracing cross-section, the F80n6t8h15 model has the highest behavior coefficient factor (7.36), which is 3% higher than the model without a damper (7.14). For the models with the BOX60 brace, there was no significant increase in the value of the behavior coefficient in the models with dampers compared to the model without dampers. 5 Conclusions This study investigated the performance of ELBRFs equipped with XADAS dampers. It investigated 12 types of dampers with 4 and 6 number of plates, 4, 6, and 8mm thicknesses, and 9 and 15 cm heights. These dampers were placed on the frames with an elliptical brace. The cross-sections of the brace include BOX100, BOX80, and BOX60. The frames were analyzed using nonlinear static cyclic analysis. The most important results obtained from the numerical analysis are presented below. The comparison between experimental models without dampers in the previous studies and the numerical models with dampers (in this paper) revealed that XADAS dampers significantly reduce the amount of plastic strain and damage in the connections of elliptical braces to the beam and columns, compared to the frames without dampers. Additionally, the results showed that the plastic hinge location moves from the connections, beams, columns, and braces to the damper, and making the damper a fuse. In all models with dampers, the damage rate was increased as the damper height decreased. Additionally, increasing the damper height to more than 15cm was unsuitable because it changed the proper form of the elliptical brace. Considering the values of PEEQ at the end of loading and the same energy level, it was found that equipping frames with a 15cm-high damper effectively reduces the PEEQ and damage consequently. Also, in the models with a 9cm-high damper, most failures occurred in the damper, and the main components of the frame were without damage. Increasing the number and thickness of plates in dampers increased the energy dissipation of structure. When comparing the dissipated energy of n4t6 and n6t4 dampers with the same cross-section, it was observed that the dissipated energy of the models with n4t6 dampers is more. This illustrated that the plate thickness has a more significant effect on energy dissipation than the number of damper plates. The models with BOX80 braces and a 15cm-high damper tended to fracture lower than others. Among these, the F80n6t4h15 model had the lowest fracture tendency. Additionally, these models dissipated up to 93% more energy than the model without a damper, while all had the same fracture tendency (equal PEEQ). Based on the numerical results and achieved PEEQ values, using the XADAS dampers in the elliptical braced frames can reduce fracture tendency (PEEQ = 2.21) by 61% compared to the elliptical braced frames without dampers (PEEQ = 5.47). The response modification factor (R) in the ELBRFs with XADAS dampers (R = 7.5) increased by 15% compared to the model without dampers (R = 6.12). The models with BOX80 braces and 15cm-high dampers significantly had less damage than others. The F80n6t8h15 is the most suitable model due to its higher energy dissipation and more response modification factor (R). Declarations Author Contribution contribution of individual authors to manuscript preparation is defined in terms of the following criteria:a.Conceptualization;b.Data curation;c.Formal analysis;d.Funding acquisition;e.Investigation;f.Methodology;g.Project administration;h.Resources;i.Software;j.Supervision;k.Validationl.Visualization;m. Writing – original draft;n.Writing – review & editing;Author’s First and Last Name.(a -g).% contribution to the totalArefeh Sadat Faalnazari.a, b, e, f,i, and l-n.. 40 Abbas Haghollahi.c, h, j, k, and n.30Mohammad Hadi Bagherinejad.c, h, j, k, and n.30 References ABAQUS. (2014). Analysis User's Manual .In (Version V.6-14) Dassault Systèmes Simulia. Bagherinejad, M. H., & Haghollahi, A. (2018). Topology optimization of steel plate shear walls in the moment frames. Steel and Composite Structures , 29 (6), 771-783. https://doi.org/10.12989/SCS.2018.29.6.771 Bagherinejad, M. H., & Haghollahi, A. (2020a). New form of perforated steel plate shear wall in simple frames using topology optimization. Structural Engineering and Mechanics , 74 (3), 325-339. https://doi.org/10.12989/SEM.2020.74.3.325 Bagherinejad, M. H., & Haghollahi, A. (2020b). Study on Topology Optimization of Perforated Steel Plate Shear Walls in Moment Frame Based on Strain Energy. 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Structures , 14 , 75-87. https://doi.org/https://doi.org/10.1016/j.istruc.2018.02.005 Tsai, K. C., Chen, H. W., Hong, C. P., & Su, Y. F. (1993). Design of steel triangular plate energy absorbers for seismic-resistant construction. Earthquake spectra , 9 , 505-528. Wang, M., Yang, W., Shi, Y., & Xu, J. (2015). Seismic behaviors of steel plate shear wall structures with construction details and materials. J. Constr. Steel Res. , 107 , 194-210. Whittaker, A., Bertero, V., Alonso, J., & Thompson, C. (1988). Earthquake Simulator Testing of Steel Plate Added Damping and Stiffness Elements . https://doi.org/10.13140/RG.2.1.1455.3207 Wu, C. X., Zhou, Y., Tong, J. G., & Han, J. J. (2012, September). Study on the seismic performance of X-added damping and stiffness energy dissipation device 15th World Conference on Earthquake Engineering 2012, Lisbon, Portugal. Xia, C., & Hanson, R. D. (1992). Influence of ADAS Element Parameters on Building Seismic Response. 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Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 15 Dec, 2025 Reviews received at journal 29 Nov, 2025 Reviewers agreed at journal 08 Nov, 2025 Reviewers agreed at journal 07 Nov, 2025 Reviewers invited by journal 05 Nov, 2025 Editor assigned by journal 29 Sep, 2025 Submission checks completed at journal 29 Sep, 2025 First submitted to journal 26 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":265857,"visible":true,"origin":"","legend":"\u003cp\u003eDamper designed by (Wu et al., 2012), a) Dimensions (in millimeter), b) Cyclic loading, c) Von Mises stress contour at the end of loading, d) Comparing the hysteresis diagram of the tested damper and the ABAQUS model\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7722571/v1/fcddec222286d2919b866aff.png"},{"id":96139660,"identity":"63e9317c-ebee-4764-a0d9-53664b9f7f50","added_by":"auto","created_at":"2025-11-18 05:06:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":370660,"visible":true,"origin":"","legend":"\u003cp\u003eELBRF model, a) Dimensions (in millimeter), b) ATC-24 loading pattern, c) Comparison of hysteresis behavior for numerical and experimental models, d) Deformed shape of numerical model, e) Deformed shape of experimental model(Ghasemi Jouneghani \u0026amp; Haghollahi, 2020a)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7722571/v1/d00ab7db909a753937d68c9d.png"},{"id":96139661,"identity":"2660fccf-91b8-4d6f-b519-6729a0188821","added_by":"auto","created_at":"2025-11-18 05:06:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":96615,"visible":true,"origin":"","legend":"\u003cp\u003eModeling in Abaqus, a) Dimensions of X-shaped sheets of modeled dampers (mm), b) Naming method of each model, c) ELBRF model equipped with XADAS\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7722571/v1/e6b1fcfd8581f681761e8e2a.png"},{"id":96139663,"identity":"9ed3fded-64da-485f-a721-436b6da24fd3","added_by":"auto","created_at":"2025-11-18 05:06:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":299141,"visible":true,"origin":"","legend":"\u003cp\u003eCurves of models, a) Force-deformation curves under cyclic loading, b) Energy dissipation curves\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7722571/v1/2e298efdaaa7fcaf9f594617.png"},{"id":96139691,"identity":"7d94498d-c93d-4c42-a256-172f6a5a47f6","added_by":"auto","created_at":"2025-11-18 05:06:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":296935,"visible":true,"origin":"","legend":"\u003cp\u003ePEEQ contour and failure modes at the end of loading\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7722571/v1/a51aba52289586aeb901dc7a.png"},{"id":96139667,"identity":"08884066-b47b-41f3-a6c7-3659e98ea42e","added_by":"auto","created_at":"2025-11-18 05:06:38","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":283091,"visible":true,"origin":"","legend":"\u003cp\u003ea) Cracks and plastic areas in the laboratory model (Ghasemi Jouneghani \u0026amp; Haghollahi, 2020a), b) PEEQ contour in numerical modeling of the model with damper F100n6t8h9\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7722571/v1/13a040a83309cc2b5b50a7f1.png"},{"id":96248191,"identity":"5f3be01b-811d-4203-ac2f-13df712119c0","added_by":"auto","created_at":"2025-11-19 07:28:09","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":43085,"visible":true,"origin":"","legend":"\u003cp\u003eCalculation of response modification factor\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7722571/v1/546024159e9935bd29a1c655.png"},{"id":96248997,"identity":"64352063-a4d9-477a-b417-761f6e04c945","added_by":"auto","created_at":"2025-11-19 07:29:53","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":171087,"visible":true,"origin":"","legend":"\u003cp\u003eGraph of models, a) energy dissipation values in equal PEEQ, b) Push of cyclic curves, c) Values of response modification factor (\u003cem\u003eR\u003c/em\u003e)\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7722571/v1/d76254f2c57372cf8692689d.png"},{"id":96256739,"identity":"5aff85e5-95fe-4a2b-aa0b-9aff192f904f","added_by":"auto","created_at":"2025-11-19 07:50:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2772710,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7722571/v1/c6e9d188-7785-4182-832f-fee890c7edb3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Seismic performance assessment of elliptic braced frames incorporating XADAS dampers","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eStructures usually remain in the elastic range during low and moderate earthquakes and enter the plastic range when they are under the effect of a severe earthquake. Ductility and energy absorption are the important indexes that show the ability of structures against severe earthquakes. These indexes are revealed when the structures enter the plastic zone and plastic hinges are formed (Elnashai \u0026amp; Di Sarno, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). As a result, a significant amount of earthquake energy is absorbed by the local plastic hinges in the lateral resisting system of structures. The members that enter the plastic zone after the earthquake must be repaired or replaced, which is difficult or impossible. If it is possible to concentrate the absorption of earthquake energy in special devices such as dampers, the subsequent damage to the main elements of the structure will be reduced and the possibility of operation after the earthquake will be provided (Di Sarno \u0026amp; Elnashai, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Lu et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThere are various passive control methods for retrofitting structures, such as damper (Ghasemi Jouneghani et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Khatibinia et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Naderpour et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nouri et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The Added Damping And Stiffness (ADAS) system (Whittaker et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1988\u003c/span\u003e) is a passive control system consisting of integrated parallel steel plates that significantly increase the damping of a structure by inelastic deformation of the steel plates (Soong \u0026amp; Dargush, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The ADAS dampers act as structural fuses, preventing damage to other members by focusing on their nonlinear behavior. One of the advantages of ADAS dampers is the ability to replace and operate after an earthquake. Also, the structure can return to its initial strength and stiffness before (Tsai et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). XADAS and TADAS with X-shaped and triangular plates have a better performance in energy dissipation than dampers with rectangular plates (ADAS). In ADAS, only two ends of the rectangular plate are yielded, but in X-shaped and triangular plates, the entire height of the plate is yielded, and the entire volume of steel can participate in energy dissipation (Khatibinia et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xia \u0026amp; Hanson, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). The plates of TADAS dampers have sharp corners in their narrowest part, which causes stress concentration and then a failure in this area (TahamouliRoudsari et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMany experimental and numerical papers have been presented on the behavior of XADAS dampers. Also, the overall performance of the structure has been investigated by combining the XADAS dampers with different types of lateral resistance systems. Sajjadi Alehashem et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) examined various multi-story concentric bracing frames (CBF), Chevron, and eccentric bracing frames (EBF) equipped with the TADAS and XADAS dampers under different earthquakes. The period of the first mode of structures equipped with XADAS and TADAS dampers was obtained as 1.39 and 1.23, respectively, which is more than other frames. The amount of base shear force created in systems equipped with dampers was reduced by more than 50% compared to other investigated systems in different earthquakes. Also, the acceleration of the roof in systems with these types of dampers was less compared to other systems. In comparing the frames equipped with XADAS and TADAS dampers, the base shear force and the acceleration of the roof in the systems with XADAS were lower than those with TADAS. Wu et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) designed and tested an XADAS damper and evaluated the seismic performance of the frames equipped with this damper and observed that energy absorption was higher in the structures with dampers and the members' damage was reduced compared to structures without dampers. TahamouliRoudsari et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) tested several types of XADAS and TADAS dampers on one-story and one-bay concrete frames with Chevron braces. The effective stiffness, final strength, ductility, and strength reduction factor of all frames equipped with dampers were increased several times compared to the reference frame. They illustrated that the dissipated energy in the structure equipped with the XADAS damper is 25% more than the structure with TADAS. Also, the structure with XADAS has more ductility, strength, and strength reduction factors than that with TADAS.\u003c/p\u003e\u003cp\u003eRecently, new forms of braces in structures have been considered. The elliptic braced resisting frame (ELBRF) is a modern lateral bracing system introduced in 2016 by Ghasemi and Haghollahi. The elliptical form of the brace dissipates considerable energy, which is one advantage of this structural bracing system. Also, using an elliptical brace allows the placement of windows and even doors in the place of the brace. In the comparison between the new ELBRF and conventional frames, such as the special moment resisting frame (SMRF), the X-braced frame, the V-braced frame (Inverted V-braced CBF), and the knee-braced resisting frame (KBRF), the results illustrated that the energy absorption of ELBRF system is up to 9 times more than other systems. Moreover, compared to SMRF and KBRF, the lateral stiffness of the elliptical brace is higher, and compared to X-braced CBF and Inverted V-braced CBF, it has similar or lower stiffness. The plastic hinges in conventional frames develop in beams, columns, or braces. In contrast, in ELBRF, the plastic hinges only occur in the braces (Ghasemi Jouneghani et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ghasemi Jouneghani et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ghasemi Jouneghani et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). With the increase in the number of stories in ELBRF, despite the other investigated structures, plastic hinges will be formed in the upper stories, and this indicates the beginning of the process of destruction from the upper stories (Ghasemi Jouneghani et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ghasemi Jouneghani et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Also, the hysteresis diagram of ELBRFs is stable, and there is no pinching. No decrease in the stiffness and strength has been observed in the curves up to 5% drift (Ghasemi Jouneghani \u0026amp; Haghollahi, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e; Ghasemi Jouneghani et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e). Adding brackets at the corners of the brace results in a 15% increase in energy absorption capacity and a 10% increase in lateral stiffness. Different models of ELBRFs are 10\u0026ndash;15% heavier than the X-bracing system, but their ductility and energy absorption are more than the X-bracing system (Ghasemi Jouneghani \u0026amp; Haghollahi, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020c\u003c/span\u003e). To evaluate the demand and capacity of the ELBRF system, incremental dynamic analysis (IDA) was performed. The ductility and overstrength factor are 2.1 and 3.1, respectively. Based on the ultimate limit state and allowable stress methods, the response modification factor is 6.5 and 9.5, respectively (Ghasemi Jouneghani \u0026amp; Haghollahi, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020d\u003c/span\u003e). The comprehensive experimental and numerical studies on elliptic bracing prove the appropriate seismic and cyclic performance of elliptic bracing.\u003c/p\u003e\u003cp\u003eStructures designed against earthquakes must have sufficient stiffness and ductility to control deformation and prevent any possible damage. Stiffness and ductility are two characteristics in structures that must be balanced. It is better to design a structural system that combines these characteristics best considering the costs. As mentioned, adding a damper is an effective way to provide ductility in structures. Also, a proper bracing system combined with a damper can simultaneously improve the resistance and ductility of structures. Since the high effect of XADAS dampers is known in different structures, the elliptic braced resisting frame equipped with an XADAS damper (ELBRF-XADAS) can be used as a new seismic system in structures. Based on the advantages of the XADAS damper that were stated in the articles, this paper evaluates its effect on ELBRF. In the second section of this article, the numerical modeling and calibration with the experimental results are discussed. In the third part, the scenarios and parameters studied are presented. In the fourth part, the analysis results and the selection of the best model are discussed.\u003c/p\u003e"},{"header":"2 Modeling of structural elements and calibration","content":"\u003cp\u003eIn this study, ABAQUS software was used for numerical modeling. Experimental models of XADAS and ELBRF were utilized for validation and calibrations.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Modeling of damper\u003c/h2\u003e\u003cp\u003eAn XADAS damper proposed by Wu 2012 was modeled and calibrated in ABAQUS. The geometric specifications of the model are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea. The modulus of elasticity, Poisson ratio, and ultimate strain are 200 GPa, 0.3, and 0.2, respectively. The yield and ultimate stress (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eu\u003c/em\u003e\u003c/sub\u003e) are 160 MPa and 250 MPa, respectively. For modeling, the X-shaped plates were connected to the upper and lower rectangular plates using tie connections (Bagherinejad \u0026amp; Haghollahi, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). The upper rectangular plate was fixed in all directions of displacement and rotation, while the lower plate was subjected to the cyclic load specified in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb. All parts were meshed using 4-node shell elements (S4R) (Bagherinejad \u0026amp; Haghollahi, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e). The mesh size for the rectangular and X-shaped plates was 20 mm and 8 mm, respectively. The isotropic hardening model was applied to the nonlinear behavior of steel materials. The model was analyzed using nonlinear static analysis and the nonlinearity of geometry and material were considered.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec shows the Von Mises stress contour at the end of loading. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed shows the hysteresis diagram results of the numerical analysis performed with the ABAQUS software compared to the experimental test results. The numerical results illustrate a good match and accuracy of the numerical modeling compared to experimental results.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Modeling of ELBRF\u003c/h2\u003e\u003cp\u003eHerein, the frame proposed by Ghasemi Jouneghani and Haghollahi (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020c\u003c/span\u003e) and shown in Fig.\u0026nbsp;2a, is presented to validate the modeling of an ELBRF. The dimensions of the frame and brace are provided in Fig.\u0026nbsp;2a.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;2.\u003c/b\u003e ELBRF model, a) Dimensions (in millimeter), b) ATC-24 loading pattern, c) Comparison of hysteresis behavior for numerical and experimental models, d) Deformed shape of numerical model, e) Deformed shape of experimental model(Ghasemi Jouneghani \u0026amp; Haghollahi, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThe material's Poisson ratio is 0.3, and its ultimate strain is 0.2. Other mechanical characteristics of the materials are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The loading pattern in all frames follows the ATC24 1992 loading protocol (see Fig.\u0026nbsp;2b). The target displacement is 78 mm, corresponding to a 5.1% drift. Isotropic hardening was employed to study the cyclic behavior of materials (Maurya et al. 2013). All parts were meshed using a 4-node shell element (S4R). The beam, column, and brace mesh sizes were considered 30 mm, 30 mm, and 20 mm, respectively. The hysteresis diagrams of the numerical frame are compared with the experimental in Fig.\u0026nbsp;2c.\u003c/p\u003e\u003cp\u003eFigures\u0026nbsp;2d and 2e shows the deformation of numerical and experimental models at the end of loading. The figures indicate a good agreement between the numerical and experimental results.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMechanical characteristics of materials\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSteel material\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eElastic modulus (GPa)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStatic yield (MPa)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStatic ultimate (MPa)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBeam \u0026amp; column (HEB 160)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrace (BOX)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e205.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e400\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlate (Thk. = 10 mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e205.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e400\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlate (Thk. = 20 mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e202.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e400\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDamper (X plate)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e205.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e400\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Scenarios and parameters studied","content":"\u003cp\u003eIn this section, the cyclic behavior of the XADAS damper on the ELBRF frame is investigated through the simulations in ABAQUS. For this purpose, various scenarios are created to study the installation of dampers on the ELBRF frames. In these scenarios, the dimensions and cross-sections of the beam and columns are kept constant while three different sizes of BOX 60\u0026times;4, BOX 80\u0026times;6, and BOX 100\u0026times;10 are considered for the cross-section of the brace. According to the previous research (Solaimani Nezhad \u0026amp; Mahmoudi, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; TahamouliRoudsari et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), the number of damper plates is 4 and 6, the height of dampers is 150 mm and 90 mm (TahamouliRoudsari et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), and the thickness of damper plates is 4, 6, and 8 mm. The damper height greater than 150 mm was not considered due to creating an inappropriate elliptical shape. Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea illustrates the dimensions of each damper plate. Various parameters, including the number, thickness, and height of plates, are defined in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The naming of models follows the pattern depicted in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb. The damper is connected to the beam and brace in the upper part of the brace with two upper and lower plates using a tie connection. Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec shows one of the models made in the ABAQUS software.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSpecifications of the proposed models\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eXADAS Damper\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrace section\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlate number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlate thickness (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlate height (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 60\u0026times;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF60n4t4h15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 60\u0026times;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF60n4t6h15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 60\u0026times;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF60n4t8h15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 60\u0026times;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF60n6t4h15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 60\u0026times;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF60n6t6h15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 60\u0026times;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF60n6t8h15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 60\u0026times;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF60n4t4h9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 60\u0026times;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF60n4t6h9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 60\u0026times;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF60n4t8h9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 60\u0026times;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF60n6t4h9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 60\u0026times;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF60n6t6h9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 60\u0026times;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF60n6t8h9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 60\u0026times;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 80\u0026times;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF80n4t4h15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 80\u0026times;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF80n4t6h15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 80\u0026times;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF80n4t8h15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 80\u0026times;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF80n6t4h15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 80\u0026times;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF80n6t6h15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 80\u0026times;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF80n6t8h15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 80\u0026times;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF80n4t4h9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 80\u0026times;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF80n4t6h9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 80\u0026times;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF80n4t8h9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 80\u0026times;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF80n6t4h9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 80\u0026times;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF80n6t6h9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 80\u0026times;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF80n6t8h9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 80\u0026times;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 100\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF100n4t4h15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 100\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF100n4t6h15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 100\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF100n4t8h15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 100\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF100n6t4h15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 100\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF100n6t6h15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 100\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF100n6t8h15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 100\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF100n4t4h9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 100\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF100n4t6h9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 100\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF100n4t8h9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 100\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF100n6t4h9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 100\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF100n6t6h9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 100\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF100n6t8h9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOX 100\u0026times;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"4 Results of parametric studies","content":"\u003cp\u003eThe following sections present the results of nonlinear analyses. Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea shows hysteresis diagrams Models with identical bracing cross-sections are grouped in a series. For example, the F60 series includes the models with the BOX 60\u0026times;4 brace. Next, to compare the nonlinear behavior of the models, three parameters of energy dissipation, fracture tendency, and response modification factor of the frames have been measured and compared.\u003c/p\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Comparison of energy dissipation\u003c/h2\u003e\n \u003cp\u003eThe area under the force-displacement diagram equals the amount of energy dissipation. The graphs depicting the energy dissipation in each model are illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb. The values of energy dissipation at the end of loading are presented in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. As shown, the energy dissipation increases when the number of damper plates and their thickness increase. Furthermore, by comparing the energy dissipation of the models with the same damper cross-section area (for example, n4t6 and n6t4), it is indicated that n4t6 dampers dissipated more energy than n6t4 dampers. This proves that the plate thickness affects energy dissipation more than the number of plates. At the end of loading, models without dampers dissipated more energy than models with dampers due to the higher stiffness of these models than models with dampers. Among the models with dampers in the F60, F80, and F100 series, respectively, the F60n6t8h15, F80n6t8h9, and F100n6t8h9 models have the highest amount of energy dissipation at the end of loading. The F100 model has dissipated the most energy compared to other models. The F60n4t4h15 model failed before reaching the target displacement and could not withstand more cyclic load, which indicates the weakness of this damper in comparison to the F60 frame stiffness.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Fracture tendency\u003c/h2\u003e\n \u003cp\u003eIn this study, the equivalent plastic strain criterion (PEEQ) is used as a failure criterion and fracture tendency (Bagherinejad \u0026amp; Haghollahi, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). According to Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), the equivalent plastic strain (PEEQ) checks the tendency to fracture and local ductility. The equivalent strain is a scalar quantity that shows the cumulative values and accumulation of strains at any point in the structure (ABAQUS, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wang et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:PEEQ={\\dot{\\:\\stackrel{\\sim}{\\epsilon\\:}\\:}}^{pl}\\left|\\genfrac{}{}{0pt}{}{}{0}\\right.+\\underset{0}{\\overset{t}{\\int\\:}}{\\dot{\\:\\stackrel{\\sim}{\\epsilon\\:}\\:}}^{pl}dt\\:.\\:{\\dot{\\stackrel{\\sim}{\\epsilon\\:}\\:}}^{pl}=\\sqrt{\\frac{2}{3}{\\dot{\\epsilon\\:}}^{pl}.{\\dot{\\epsilon\\:}}^{pl}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\dot{\\epsilon\\:}}^{pl}\\)\u003c/span\u003e\u003c/span\u003eis the rate of plastic flow, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\dot{\\stackrel{\\sim}{\\epsilon\\:}\\:}\\)\u003c/span\u003e\u003c/span\u003eis the equivalent plastic strain rate, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\dot{\\stackrel{\\sim}{\\epsilon\\:}\\:}}^{pl}\\left|\\genfrac{}{}{0pt}{}{}{0}\\right.\\)\u003c/span\u003e\u003c/span\u003e is the initial equivalent plastic strain rate and \u003cem\u003et\u003c/em\u003e is the time duration of the analysis.\u003c/p\u003e\n \u003cp\u003ePEEQ values at the end of loading are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The results show that in the F60 series models, the highest damage occurred in the F60n4t4h15 model, while the lowest occurred in the F60n4t6h15 or F60n6t4h15 models. In the F80 series models, the highest damage happened in the F80n4t4h9 model, and the lowest occurred in the F80n6t4h15 model. For the F100 series models, the highest damage is in the F100n4t8h9 model, and the lowest is in the F100n6t6h15 model. Overall, the highest damage across all models is in the F100n4t8h9 model, and the lowest is in the F80n6t4h15 model. In the F60, F80, and F100 model series, the damper can reduce damage by 14%, 61%, and 30%, respectively. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows the deformed frames and their PEEQ contour at the end of loading.\u003c/p\u003e\n \u003cp\u003eIn the models without dampers, the maximum PEEQ value occurred in the middle of each quarter of the elliptical brace, where the brace connects to the beam and columns. In some models with dampers, in addition to the reduced PEEQ value, the maximum damage occurred only in the damper. It indicates the proper performance of the damper as a fuse. Also, no damage was observed at the beam-brace and column-brace connections. Additionally, based on the comparison of PEEQ values in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, it can be concluded that the models with 15cm-high dampers experienced less damage at the end of loading than those with 9cm-high. Moreover, in the models with 9cm-high dampers, the max PEEQ occurred in the dampers, which avoided damage to structural components. Generally, the F80 series models with a 15cm-high damper have the lowest fracture tendency. This indicates these dampers\u0026apos; proper performance and compatibility with the bracing cross-section.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Comparison of the behavior of models at the same energies\u003c/h2\u003e\n \u003cp\u003eWhen the stiffness of a structure is reduced and a displacement is applied to it, it is evident that the energy spent for displacement is reduced. Therefore, the energy dissipation is also reduced when a damper is added to a structure. Now, it should be checked that when two structures have the same energy dissipation, which one suffers less damage? And vice versa, when two structures have the same damage, which one has dissipated more energy. In general, it can be concluded from the cyclic behavior of the two structures that the structure with less damage is better if the energy absorbed in both structures is equal. Also, the structure with more energy dissipation is better if the damage in both structures is equal.\u003c/p\u003e\n \u003cp\u003eHerein, for a comprehensive comparison, the minimum dissipated energy value is used as the base energy for each series to compare the models. The Maximum PEEQ in Equal Energy Dissipation value (MPEED) was assessed for each model, and the results are detailed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. As shown, it is illustrated that at the specified energy level (minimum energy), the damage rate has significantly increased with the decrease in the height of the damper. This indicates that at this energy level, frames with shorter dampers suffer more damage due to the occurrence of plastic hinges. Also, increasing the thickness and number of damper plates will result in more significant damage in the frames with lower-height dampers. The F80n6t4h15 model has the lowest PEEQ in an equal energy value.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Maximum energy dissipation and PEEQ values\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cimg 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\"\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003eIn the previous experimental and numerical study on ELBRF, it was observed that important elements of the frame (including the plates connecting the elliptical brace to the beam and columns) experienced considerable stress that led to cracks and damage. By equipping the frames with XADAS dampers, a significant reduction in the fracture tendency (PEEQ) at the connecting plates could be observed. Also, the fracture tendency transfers to the dampers. Figure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea displays the cracks and damage in the experimental model from the previous study without dampers (Ghasemi Jouneghani and Haghollahi \u003cspan class=\"CitationRef\"\u003e2020a\u003c/span\u003e). In contrast, Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb shows the images of the same parts in the numerical model with an XADAS damper.\u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4 Comparison of the behavior of models in the same PEEQ\u003c/h2\u003e\n \u003cp\u003eAs described in the previous section, the minimum PEEQ value (PEEQ\u0026thinsp;=\u0026thinsp;2.21) is considered the baseline PEEQ to compare the energy dissipation in the models. The Maximum Energy Dissipation in Equal PEEQ (MEDEP) is evaluated for each model, and the results are presented in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ea.\u003c/p\u003e\n \u003cp\u003eIn the equal PEEQ, the maximum energy dissipation in the F60, F80, and F100 series in models without dampers is 43.1, 73.9, and 106 ton-m, respectively. With the placement of dampers, the maximum energy dissipation reaches 59.4, 142.6, and 98.3 ton-m. As it is apparent in the diagrams, with the placement of a 15cm-high damper in the F60 and F80 series models, the energy dissipation has increased, while it is the opposite in the F100 models. In the F80 series models with 15cm-high dampers, the MEDEP has increased by 93% compared to the model without dampers. This demonstrates the superior performance of this brace cross-section with this form of damper compared to the other models.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e4.5 Comparison of response modification factor (R)\u003c/h2\u003e\n \u003cp\u003eThe ductility of a structure refers to its ability to absorb energy. This energy absorption capacity allows the reduction of the response spectrum of linear elastic design using the response modification factor (\u003cem\u003eR\u003c/em\u003e). The response modification factor of a model under the cyclic loading can be calculated by obtaining push of the cyclic curve and the equivalent bilinear curve. In designing structures, the level of \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e (maximum base shear) is reduced to the level of \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e, which corresponds to the formation of the first plastic hinge. This level is commonly referred to as the \u0026quot;first significant yield\u0026quot; level, and after that, the behavior of the structure and its responses gradually become inelastic (NEHRP, \u003cspan class=\"CitationRef\"\u003e1988\u003c/span\u003e). As shown in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e, to bilinearize the curve by regarding FEMA 356 (2000), \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e (effective yield strength) should be selected according to the intersection between the original nonlinear analysis curve and the first segment of the bilinear curve (0.6\u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e) and as a result, the area under the original nonlinear analysis curve should equal the bilinear curve (FEMA, \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe \u003cem\u003eR\u003c/em\u003e factor in the Load and Resistance Factor Design (LRFD) method is determined by using Eq. (\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e):\u003c/p\u003e\n \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$\\:R=\\frac{{V}_{E}}{{V}_{s}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003eE\u003c/em\u003e\u003c/sub\u003e is the elastic design force and \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e is the first significant yield force.\u003c/p\u003e\n \u003cp\u003eIn Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eb, the push of cyclic curves is presented and the values of the \u003cem\u003eR\u003c/em\u003e are calculated. Figure \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ec displays the calculated values of \u003cem\u003eR\u003c/em\u003e for each model. Among the models with the BOX100 for the brace, the F100n6t6h15 model has the highest response modification factor value of 7.57, representing a 15% increase compared to its value in the model without a damper (6.12). Among the models with a BOX80 bracing cross-section, the F80n6t8h15 model has the highest behavior coefficient factor (7.36), which is 3% higher than the model without a damper (7.14). For the models with the BOX60 brace, there was no significant increase in the value of the behavior coefficient in the models with dampers compared to the model without dampers.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eThis study investigated the performance of ELBRFs equipped with XADAS dampers. It investigated 12 types of dampers with 4 and 6 number of plates, 4, 6, and 8mm thicknesses, and 9 and 15 cm heights. These dampers were placed on the frames with an elliptical brace. The cross-sections of the brace include BOX100, BOX80, and BOX60. The frames were analyzed using nonlinear static cyclic analysis. The most important results obtained from the numerical analysis are presented below.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eThe comparison between experimental models without dampers in the previous studies and the numerical models with dampers (in this paper) revealed that XADAS dampers significantly reduce the amount of plastic strain and damage in the connections of elliptical braces to the beam and columns, compared to the frames without dampers. Additionally, the results showed that the plastic hinge location moves from the connections, beams, columns, and braces to the damper, and making the damper a fuse.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eIn all models with dampers, the damage rate was increased as the damper height decreased. Additionally, increasing the damper height to more than 15cm was unsuitable because it changed the proper form of the elliptical brace.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eConsidering the values of PEEQ at the end of loading and the same energy level, it was found that equipping frames with a 15cm-high damper effectively reduces the PEEQ and damage consequently. Also, in the models with a 9cm-high damper, most failures occurred in the damper, and the main components of the frame were without damage.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eIncreasing the number and thickness of plates in dampers increased the energy dissipation of structure.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eWhen comparing the dissipated energy of n4t6 and n6t4 dampers with the same cross-section, it was observed that the dissipated energy of the models with n4t6 dampers is more. This illustrated that the plate thickness has a more significant effect on energy dissipation than the number of damper plates.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe models with BOX80 braces and a 15cm-high damper tended to fracture lower than others. Among these, the F80n6t4h15 model had the lowest fracture tendency. Additionally, these models dissipated up to 93% more energy than the model without a damper, while all had the same fracture tendency (equal PEEQ).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eBased on the numerical results and achieved PEEQ values, using the XADAS dampers in the elliptical braced frames can reduce fracture tendency (PEEQ\u0026thinsp;=\u0026thinsp;2.21) by 61% compared to the elliptical braced frames without dampers (PEEQ\u0026thinsp;=\u0026thinsp;5.47).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe response modification factor (R) in the ELBRFs with XADAS dampers (R\u0026thinsp;=\u0026thinsp;7.5) increased by 15% compared to the model without dampers (R\u0026thinsp;=\u0026thinsp;6.12).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe models with BOX80 braces and 15cm-high dampers significantly had less damage than others. The F80n6t8h15 is the most suitable model due to its higher energy dissipation and more response modification factor (R).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003econtribution of individual authors to manuscript preparation is defined in terms of the following criteria:a.Conceptualization;b.Data curation;c.Formal analysis;d.Funding acquisition;e.Investigation;f.Methodology;g.Project administration;h.Resources;i.Software;j.Supervision;k.Validationl.Visualization;m. Writing \u0026ndash; original draft;n.Writing \u0026ndash; review \u0026amp; editing;Author\u0026rsquo;s First and Last Name.(a -g).% contribution to the totalArefeh Sadat Faalnazari.a, b, e, f,i, and l-n.. 40 Abbas Haghollahi.c, h, j, k, and n.30Mohammad Hadi Bagherinejad.c, h, j, k, and n.30\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eABAQUS. 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Development and performance research of an Xadas damper with a double-phased yield mechanism. \u003cem\u003eStructures\u003c/em\u003e,\u003cem\u003e 54\u003c/em\u003e, 1420-1439. https://doi.org/10.1016/j.istruc.2023.05.141\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-mechanics-and-materials-in-design","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [International Journal of Mechanics and Materials in Design](https://link.springer.com/journal/10999)","snPcode":"10999","submissionUrl":"https://submission.springernature.com/new-submission/10999/3","title":"International Journal of Mechanics and Materials in Design","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Elliptical brace, Yielding damper, Nonlinear analysis, Dissipated energy, Fracture tendency, Response modification factor","lastPublishedDoi":"10.21203/rs.3.rs-7722571/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7722571/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe elliptic brace is a new lateral resistance system recently presented. This paper investigates the performance of a yielding damper named X-shaped added damping and stiffness (XADAS) on the elliptic braced frame. For this purpose, 12 XADAS dampers with different numbers of plates, dimensions, and thicknesses were utilized on three braced frames. Also, three sections, including BOX 60\u0026times;4, BOX 80\u0026times;6, and BOX 100\u0026times;10, were considered for the braces. The models were subjected to a cyclic loading and a non-linear static analysis. Three parameters, dissipated energy, fracture tendency and response modification factor, were evaluated to compare the models. The numerical analysis illustrated that the frames without dampers dissipate more energy at the end of loading due to higher stiffness, but they also experience more failures. Therefore, the models were compared at an equal value of dissipated energy and fracture tendency for a correct comparison. In comparing the models with the same fracture tendency, it was observed that the frame with a damper can dissipate up to 93% more energy than the model without a damper. The models with 15 cm-high dampers have experienced less damage, and in the models equipped with 9 cm-high dampers, more damage has been transferred to the dampers. The results showed that the model braced by the BOX 80\u0026times;6 and equipped with the damper that the number, height and thickness of plates are respectively equal to 6, 15cm and 8mm has the best performance regarding dissipated energy, fracture tendency and response modification factor.\u003c/p\u003e","manuscriptTitle":"Seismic performance assessment of elliptic braced frames incorporating XADAS dampers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 05:06:33","doi":"10.21203/rs.3.rs-7722571/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-15T21:20:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-29T11:14:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"101915622212746224826647102209386087554","date":"2025-11-08T05:20:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"136615838437979492903906674941903855961","date":"2025-11-08T03:50:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-05T23:57:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-29T15:51:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-29T15:50:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Mechanics and Materials in Design","date":"2025-09-26T13:52:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-mechanics-and-materials-in-design","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [International Journal of Mechanics and Materials in Design](https://link.springer.com/journal/10999)","snPcode":"10999","submissionUrl":"https://submission.springernature.com/new-submission/10999/3","title":"International Journal of Mechanics and Materials in Design","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"51f473d5-0f45-4dea-83cd-2944ef65b3a1","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-14T02:23:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-18 05:06:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7722571","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7722571","identity":"rs-7722571","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}