Estimation of peak floor acceleration based on support vector regression and p-wave features

Abstract

This study proposes a support vector regression (SVR)–based method to evaluate the peak floor acceleration (PFA) in a building subject to a major earthquake. Support vector machine, which constructs a hyperplane in high-dimensional space to solve multivariate problems, is well known for the outstanding performance in classification and regression. Six P-wave parameters obtained from the vertical component of the first three seconds of the acceleration time history of the ground motion, as well as the floor height and the fundamental period of the structure, are adopted as the eight input variables. With these and the support of SVR, the PFA of a specific building can be rapidly estimated to avoid the necessity of installing accelerometers on the building. The SVR model is trained on 2274 representative earthquake records from the Structure Strong Earthquake Monitoring System (SSEMS) of the Central Weather Bureau (CWB) in Taiwan and tested on 757 independent test earthquake records. As demonstrated, the accuracy of the predicted PFA, located within a one-level difference on the seismic intensity scale of Taiwan from the monitored PFA, is 95.51%. The developed algorithm can be integrated into an existing earthquake early warning system (EEWS) to provide comprehensive protection during major earthquakes.