Prediction of seismic acceleration response of precast segmental self-centering concrete filled steel tube single-span bridges based on machine learning method

Abstract

The precast segmental self-centering concrete-filled steel tube (PSCFST) bridge is not only the ideal choice for fast and environmentally friendly construction but also has good seismic and resilience properties. Our research group has carried out shaking table test research on the PSCFST bridge, but due to the limitation of test equipment and site, no damage test has been carried out. To further study the seismic performance of PSCFST bridges when subjected to larger ground motions, machine learning (ML) models are developed to predict the seismic performance of PSCFST. A novel combined prediction model based on Conv1D-LSTM was proposed to predict the PSCFST bridge acceleration response. Two other commonly used ML methods including XGBoost and Random forest regression (RFR) are also used for comparison purposes. A database of ML prediction models is established based on 116 sets of input ground motion (GM) and superstructure acceleration response from shaking table tests. Then, the data of RSN292 60% GM were selected as the prediction test data. Furthermore, based on the Opensees platform, the PSCFST fiber finite element (FFE) model was established and validated by the shaking table test results, then the dynamic time history analysis of the ground motion with larger amplitude (greater than the input ground motion assignment of the shaking table test) was carried out. The superstructure acceleration response of 70%–120% of the RSN 292 GM is obtained by the FFE model and used as the data set for the ML prediction model. After that, the superstructure acceleration response is obtained through three prediction models. Comparing the simulation and prediction results shows that all the Conv1D-LSTM, XGBoost, and RFR models can reliably predict the acceleration response of the PSCFST bridge. In all cases, the Conv1D-LSTM model performed outperforms the XGBoost and RFR models. The determination coefficients (R2) of Conv1D-LSTM, XGBoost, and RFR model for the prediction of superstructure response are 0.9643, 0.8780, and 0.9623, respectively.