建筑结构学报2024，Vol.45Issue(5)：81-91,11.DOI:10.14006/j.jzjgxb.2022.0922
基向量引导的支持向量机RC框架抗震韧性评估
Basis vectors-guided support vector machines for seismic resilience assessment of RC frames
摘要
Abstract
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.关键词
基向量/支持向量机/机器学习/钢筋混凝土框架/抗震韧性Key words
basis vectors/support vector machines/machine learning/reinforced concrete frames/seismic resilience分类
土木建筑引用本文复制引用
施文凯,周宇,王尉阔,欧阳谦,骆欢..基向量引导的支持向量机RC框架抗震韧性评估[J].建筑结构学报,2024,45(5):81-91,11.基金项目
湖北省自然科学基金面上项目(2022CFB294),国家自然科学基金青年项目(52208485). （2022CFB294）
