给定地震场景下的随机地震动降维模拟OA北大核心CSTPCD

Based on the earthquake scenarios,a dimension-reduction model capable of predicting and simulating the stochastic ground motion acceleration process was developed.Firstly,1766 strong mo-tion records were selected and grouped according to fault types and site classification.Parameters of evolutionary power spectrum(EPS)for each group were identified.Secondly,based on the parameters for earthquake scenarios and the EPS,a Gaussian process regression model(GPRM)was trained.Meanwhile,the K-fold cross-validation method was adopted to verify its prediction effectiveness and accuracy.Finally,using the spectral representation method of non-stationary random processes and in-corporating the concept of dimension reduction for random functions,the dimension reduction simula-tion of stochastic ground motion was achieved under predefined earthquake scenarios.Numerical ex-amples showed that the predicted samples were consistent with the measured records in terms of fre-quency spectrum,peak values,and duration of strong motion,which verified the suitability of the pro-posed methodology in engineering applications.The research provides reasonable artificial ground mo-tion data for target areas,and lays a foundation for random seismic response analysis and reliability evaluations of engineering structures.