Seismic fragility surface analysis of rc frame structures based on bp neural networks: accounting for the effects of ground motion intensity and duration

被引：0
|
作者：
Cheng S.-Y. [1 ,2 ]
Han J.-P. [1 ,2 ]
Yu X.-H. [3 ,4 ]
Lü D.-G. [3 ,4 ]
机构：
[1] Key Laboratory of Disaster Prevention and Mitigation in Civil Engineering of Gansu Province, Lanzhou University of Technology, Lanzhou
[2] Institute of Earthquake Protection and Disaster Mitigation, Lanzhou University of Technology, Lanzhou
[3] Key Lab of Structure Dynamic Behavior and Control of China Ministry of Education, Harbin Institute of Technology, Harbin
[4] Key Lab of Smart Prevention and Mitigation of Civil Engineering Disaster of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin
来源：
Gongcheng Lixue/Engineering Mechanics | 2021年 / 38卷 / 12期
关键词：
BP neural networks; Ground motion duration; Reinforced concrete frame structures; Seismic fragility; Vector-valued intensity measures;
D O I：
10.6052/j.issn.1000-4750.2020.11.0837
中图分类号：
学科分类号：
摘要：
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. © 2021, Engineering Mechanics Press. All right reserved.
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页码：107 / 117
页数：10
相关论文
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