面向抗倒塌地震动强度指标选取的特征选择算法性能评估  

Performance evaluation of feature selection algorithm for selection of collapseestimated ground motion intensity measures

作　　者：胡进军[1,2] 刘亦恒 刘巴黎 HU Jinjun;LIU Yiheng;LIU Bali(Key Laboratory of Earthquake Engineering and Engineering Vibration,Institute of Engineering Mechanics,China Earthquake Administration,Harbin 150080,China;Key Laboratory of Earthquake Disaster Mitigation,Ministry of Emergency Management,Harbin 150080,China;School of Civil Engineering,Hunan University of Science and Technology,Xiangtan 411201,China)

机构地区：[1]中国地震局工程力学研究所地震工程与工程振动重点实验室,黑龙江哈尔滨150080 [2]地震灾害防治应急管理部重点实验室,黑龙江哈尔滨150080 [3]湖南科技大学土木工程学院,湖南湘潭411201

出　　处：《地震工程与工程振动》2024年第6期1-11,共11页Earthquake Engineering and Engineering Dynamics

基　　金：国家自然科学基金面上项目(52478568);国家自然科学基金青年项目(52408529)。

摘　　要：为了筛选有效预测结构倒塌能力的地震动强度指标,对比分析了MIC、ReliefF、XGBoost和Lasso这4种常见特征选择算法用于地震动强度指标筛选时的性能。基于单自由度结构增量动力分析结果及地震动强度指标建立特征选择回归模型,根据回归模型输出权重及频数得到欧氏距离大小排序并筛选地震动强度指标,利用筛选结果对特征选择算法的性能进行评估。同时基于2层、4层、8层和12层钢筋混凝土框架结构的增量动力分析结果对筛选后强度指标建立最小二乘回归模型,以残差的标准差变化衡量不同特征选择算法筛选出的地震动强度指标对结构倒塌的预测能力。结果表明:基于Lasso回归算法筛选的地震动强度指标比其他算法用于结构倒塌预测时准确率提高31%。结果可为基于性能地震工程(performance-based earthquake engineering,PBEE)框架下结构易损性分析中及地震动不确定性分析中地震动强度指标筛选的特征选择算法提供参考,也可为结构倒塌预测的地震动强度指标筛选提供有效特征选择算法参考。To identify an efficient and accurate feature selection algorithm for filtering seismic intensity indicators,the performance of four common feature selection algorithms,MIC,ReliefF,XGBoost and Lasso,was compared and analyzed.Based on the incremental dynamic analysis results of single-degree-of-freedom structures and the ground motion features,the feature selection regression model was established,the ground motion features was sorted and screened according to the Euclidean distance,the performance of the feature selection algorithm was evaluated according to the screening results,and the least squares regression model was established based on the incremental dynamic analysis results of the 2-storey,4-storey,8-storey and 12-storey reinforced concrete frame structures,and the standard deviation change of residual was used to measure the prediction ability of ground motion intensity measure selected by different feature selection algorithms for structural collapse.The results show that the accuracy of the ground motion features screened by the Lasso regression algorithm is 31%higher than that of other algorithms when used for structural collapse prediction.The results can be used as a feature selection algorithm reference for the selection of ground motion intensity measures in the uncertainty analysis of ground motion in the structural vulnerability analysis under the performance-based earthquake engineering(PBEE)framework,and can also be used as an effective feature selection algorithm reference for the selection of ground motion intensity measure s suitable for structural collapse prediction.

关 键 词：地震动强度指标 特征选择算法 结构倒塌分析 增量动力分析 最小二乘回归 

分 类 号：P315.9[天文地球—地震学]