基向量引导的支持向量机RC框架抗震韧性评估OA北大核心CSTPCD

Machine learning methods can evaluate the seismic resilience of buildings by establishing a nonlinear mapping relationship between inputs related to building information and seismic parameters and outputs representing resilience indicators.However,large training datasets pose challenges due to the computation of large-scale inverse matrices,leading in low computational efficiency and high memory usage.To address this issue,we propose a basis vector guided support vector machine regression(BVLS-SVMR)model.Replacing the original large-scale basis vectors for building predictive models,this model extracts small-scale sub-samples from large training datasets and maps them into a high-dimensional feature space as basis vectors.To validate its accuracy and efficiency,seismic resilience data from 9 356 reinforced concrete(RC)frame buildings(school buildings)were used.Compared with the support vector machine(LS-SVMR)model and the traditional finite element method(FEM),these results demonstrate the BVLS-SVMR model exhibited a test set prediction accuracy difference of only 0.011 compared to the LS-SVMR model and its computation time was only 1/10 of the LS-SVMR model and 1/21 709 of the traditional FEM.This proves BVLS-SVMR model's ability to accurately and rapidly predict seismic resilience indicators for school buildings.