建筑结构学报2024，Vol.45Issue(7)：1-12,12.DOI:10.14006/j.jzjgxb.2023.0568
基于多元特征驱动的既有建筑地震损伤机器学习预测方法
Multi-feature-driven prediction of seismic damage for existing buildings using machine learning
摘要
Abstract
Rapid prediction of seismic damage of existing buildings is of paramount importance for post-earthquake emergency response and accelerating rescue and recovery in urban regions.To achieve rapid and quantitative diagnosis of the safety of existing buildings,a multi-feature driven method for rapid evaluation and prediction of seismic damage is proposed.For reinforced concrete structures,through measured dynamic properties in-situ and nonlinear performance parameters obtained from HAZUS manual,a numerical model of existing building is developed.Using the ATC-63 ground motion record sets and a data-driven nonlinear damage index,a large database is generated.The feature engineering is adopted to reveal the correlations between various input features.A random forest machine learning model is employed to predict the structural seismic damage quantitatively based on design features,measured structural features,and ground motion features.The coefficient of determination for the test set is 0.99,and the proportion of samples with a relative error within±20％is 99.23％.Model interpretability analyses reveal the importance of various input features on the output results,with structural modal periods being one of the most critical features.Finally,using the data from existing seismic stations,the model successfully predicts the damage condition of the examined existing buildings under a specific ground motion.The result indicates that the proposed framework of seismic damage prediction for existing building structures exhibits ideal predictive accuracy and efficiency.关键词
既有多龄期建筑/多元特征/机器学习/快速评估/损伤评估指标Key words
existing multi-age building/multi-feature/machine learning/rapid evaluation/damage evaluation index分类
天文与地球科学引用本文复制引用
王律己,黄晨宇,单伽锃,余桦,苏金蓉..基于多元特征驱动的既有建筑地震损伤机器学习预测方法[J].建筑结构学报,2024,45(7):1-12,12.基金项目
国家自然科学基金项目(52278312),上海市青年科技启明星计划(20QC1400700),地震科技星火计划项目(XH21027). （52278312）
