基于BP神经网络的RC框架结构地震易损性曲面分析:考虑地震动强度和持时的影响  

SEISMIC FRAGILITY SURFACE ANALYSIS OF RC FRAME STRUCTURES BASED ON BP NEURAL NETWORKS:ACCOUNTING FOR THE EFFECTS OF GROUND MOTION INTENSITY AND DURATION

作　　者：程诗焱 韩建平[1,2] 于晓辉[3,4] 吕大刚[3,4] CHENG Shi-yan;HAN Jian-ping;YU Xiao-hui;LÜ Da-gang(Key Laboratory of Disaster Prevention and Mitigation in Civil Engineering of Gansu Province,Lanzhou University of Technology,Lanzhou 730050,China;Institute of Earthquake Protection and Disaster Mitigation,Lanzhou University of Technology,Lanzhou 730050,China;Key Lab of Structure Dynamic Behavior and Control of China Ministry of Education,Harbin Institute of Technology,Harbin 150090,China;Key Lab of Smart Prevention and Mitigation of Civil Engineering Disaster of the Ministry of Industry and Information Technology,Harbin Institute of Technology,Harbin 150090,China)

机构地区：[1]兰州理工大学甘肃省土木工程防灾减灾重点实验室,兰州730050 [2]兰州理工大学防震减灾研究所,兰州730050 [3]哈尔滨工业大学结构工程灾变与控制教育部重点实验室,哈尔滨150090 [4]哈尔滨工业大学土木工程学院智能防灾减灾工业与信息化部重点实验室,哈尔滨150090

出　　处：《工程力学》2021年第12期107-117,共11页Engineering Mechanics

基　　金：国家自然科学基金项目(51578273);教育部长江学者和创新团队发展计划项目(IRT_17R51)。

摘　　要：与短持时地震动相比,长持时地震动会加剧结构的损伤,增加结构的失效概率,因此有必要更充分地研究地震动持时特性对结构地震易损性分析结果的影响。该文提出了一种基于BP神经网络的地震易损性曲面分析方法,使用神经网络模型,综合考虑地震动强度和持时特性对结构地震需求的影响,并进行地震易损性分析,得到不同损伤水平下考虑地震动持时特性的结构易损性曲面。选用3个不同高度的钢筋混凝土框架结构为研究对象,分别选择具有长、短持时特性的2组地震动记录为输入,采用BP神经网络模型建立地震动强度指标与结构响应间的关系,在此基础上得到目标地震易损性曲面,并对该方法的有效性进行讨论。分析结果表明,研究建立的BP神经网络模型精度较高,依据该方法可得到可信的损伤概率分析结果。相比于传统方法,神经网络可以更为有效和准确地建立持时与结构损伤的相关关系,得到考虑持时特性的易损性分析结果。该文的方法亦可进一步拓展,将更多地震动特性纳入地震易损性分析过程,具有明确的应用前景。Comparing with short-duration ground motions,long-duration ground motions may intensify the damage and increase the failure probability of structures.Therefore,it is necessary to thoroughly investigate the influence of ground motion duration characteristics on the seismic fragility analysis results.A seismic fragility surface analysis approach based on back propagation(BP)artificial neural networks was proposed.It can account for the effect of both ground motion intensity and duration.Seismic fragility analysis was conducted to get the fragility surfaces under different damage levels.Three reinforced concrete fame structures with different heights were taken as the study cases.Long-and short-duration record sets were selected as the inputs.BP neural network models were employed to build the relationship between the ground motion intensity measures and structural responses,and the seismic fragility surfaces of the investigated structures were obtained.The validity of the proposed approach was discussed.The analysis results show that the accuracy of the established BP neural network model is high.It indicates that the fragility analysis results by this approach is reliable.Comparing with the conventional procedures,the neural network is capable of building more effective correlation models between the ground motion duration and structural damage to obtain fragility analysis results that account for ground motion duration.This approach can be further expanded to include more ground motion characteristics into the program for seismic fragility analysis.It has a definite application prospect.

关 键 词：地震动持时 BP神经网络 向量型强度指标 地震易损性 钢筋混凝土框架结构 

分 类 号：TU375[建筑科学—结构工程]