Modeling response of structures under seismic loads is an important factor in Civil Engineering as it crucially affects the design\nand management of structures, especially for the high-risk areas. In this study, novel applications of advanced soft computing\ntechniques are utilized for predicting the behavior of centrically braced frame (CBF) buildings with lead-rubber bearing (LRB)\nisolation system under ground motion effects. These techniques include least square support vector machine (LSSVM), wavelet\nneural networks (WNN), and adaptive neurofuzzy inference system (ANFIS) along with wavelet denoising. The simulation of a\n2D frame model and eight ground motions are considered in this study to evaluate the prediction models. The comparison results\nindicate that the least square support vector machine is superior to other techniques in estimating the behavior of smart structures.
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