TY - JOUR
T1 - Estimating the Behavior Factor of Regular Moment Resisting RC Frames Using Gene Expression
TT - برآورد ضریب رفتار قاب خمشی بتن مسلح منظم با استفاده از بیان ژن
JF - mdrsjrns
JO - mdrsjrns
VL - 23
IS - 1
UR - http://mcej.modares.ac.ir/article-16-54228-en.html
Y1 - 2023
SP - 91
EP - 104
KW - Reinforced concrete frame
KW - Behavior factor
KW - Gene expression
KW - Nonlinear analysis
KW - Push over analysis
N2 - Based on the seismic design, energy absorption by plastic deformation is necessary to prevent structures from collapsing during a severe earthquake. Therefore, estimating the behavior of structures to understand their response to earthquakes is particularly important. Seismic loads applied to structures are more significant than forces applied during design. This reduction in design applied loads is accomplished using a behavior factor. It is necessary to employ a behavior factor when evaluating the behavior of structures using linear analysis. The behavior coefficient depends on ductility coefficient, structural damping coefficient, soil characteristics, earthquake characteristics, over strength coefficient, and design reliability coefficient. While in seismic code, this coefficient is entirely dependent on the type of lateral strength system used. At the same time, the behavior coefficient depends on the structural geometric properties which are investigated in this paper. Since nonlinear analysis is required to determine the effect of earthquake forces during design and nonlinear dynamic analysis is time-consuming, designers typically use nonlinear static analysis. Nonlinear static analysis is one of the nonlinear analysis methods that use the lateral load to represent the earthquake load on the structure statically and increasingly. Estimating the behavior factor before starting the design process is a vital aid to designers. In this paper, we have examined the behavior factor of the reinforced concrete (RC) frame using gene expression programming. Gene expression programming is highly effective in this instance. Its effectiveness largely determines the success of the method. Gene expression programming is a class of genetic algorithms that utilizes a population of individuals, selects them based on their fit, and introduces genetic changes via one or more genetic operators. Numerous inputs are required for this purpose, including the number of stories, the span length, the seismicity of the construction site, and the ratio of the compressive strength of concrete to the yield stress of longitudinal reinforcements. Afterward, 168 RC frames were designed via SAP2000 software, and the behavior factor value was obtained using nonlinear static analysis for each frame and subsequently transferred to the GeneXpro Tools software. The sixth and ninth national building regulations, Iran's seismic code, with the American Concrete Institute Code (ACI318-14), were used to analyze and design the structures examined. In the designed frames, the number of stories is 2, 4, 6, 8, 10, 12, and 15, and the ratio of span length to story height is 1, 15, 2, and 2.5, respectively. The design base accelerations were 0.35, 0.3, and 0.25 in this study, and the longitudinal reinforcements' yield stress was initially set to 340 MPa and then increased to 400 MPa. The obtained results demonstrate that employing the gene expression programming method makes it possible to estimate the reinforced concrete frame's behavior factor with an acceptable degree of accuracy before initiating the design process. Finally, the results show that the variations of the span length and the number of the stories significantly affect behavior factor. Furthermore, as the number of stories increases, the behavior factor decreases initially and then increases. Moreover, the impact of parameters, such as design base acceleration and yield stress of longitudinal reinforcements, is negligible in calculating the behavior coefficient.
M3 10.22034/23.1.6
ER -