Prediction of Smooth Hysteretic Model Parameters Using Support Vector Regression

This study developed five artificial intelligence–based models for predicting smooth hysteretic model (SHM) parameters. Recently, an SHM based on the Bouc–Wen model was developed to determine damage accumulation and path dependence of reloading. The model comprises five main parameters that describe the seismic behavior of ductile, flexure-dominated reinforced concrete (RC) bridge columns. However, each time-variant parameter can be derived only through practical experiments and cannot be tested on actual structures; therefore, the SHM is not very practical. In this study, support-vector regression (SVR) was adopted to exploit the advantages of the developed SHM, which exhibits superior performance to other existing hysteresis models. Nine different RC bridge columns were tested under displacement time histories, and a total of 119 and 81 samples were acquired for stiffness degradation and pinching parameters, respectively. Of the samples, 80% were used for training and the remaining 20% were used for testing. Information on the longitudinal reinforcement ratio, aspect ratio, and displacement or residual displacement of individual columns was set as the SVR model input, and the pinching and stiffness degradation parameters were set as the model output. Time-variant parameters could be predicted accurately with limited deviation and error percentages. Moreover, hysteresis loops were generated using the SVR prediction results and the identified SHM parameters were compared with experimental data. The results indicated that the seismic behavior of the RC bridge columns could be estimated with high reliability using the proposed method without the support of experimental progress for further damage prediction.