Deep learning enabled seismic fragility evaluation of structures subjected to mainshock‑aftershock earthquakes  

作　　者：Shan He Yuchen Liao Peng Patrick Sun Ruiyang Zhang 

机构地区：[1]School of Civil Engineering,Southeast University,Nanjing,China [2]Department of Civil,Environmental,and Construction Engineering,University of Central Florida,Orlando,FL,USA [3]Key Laboratory of Concrete and Prestressed Concrete Structures of the Ministry of Education,Southeast University,Nanjing,China [4]National and Local Joint Engineering Research Center for Intelligent Construction and Maintenance,Southeast University,Nanjing,China

出　　处：《Urban Lifeline》2024年第1期16-31,共16页城市生命线(英文)

基　　金：supported by the National Natural Science Foundation of China(Grant No.52208466);the Fundamental Research Funds for the Central Universities。

摘　　要：Mainshock-aftershock earthquakes have gained significant attention since accumulated damages induced by multiple shocks are likely to cause failure of structures.This paper presents a deep learning approach based on a Gated Recurrent Unit(GRU)network for assessing the seismic fragility of structures under mainshock-aftershock scenarios.The GRU network is utilized to create a surrogate model that captures the nonlinear relationship between seismic responses and mainshock-aftershock earthquakes.Subsequently,seismic fragility analysis is conducted based on double incremental dynamic analysis,employing the trained GRU network.A single-degree-of-freedom system with Bouc-Wen hysteretic behavior was investigated to demonstrate the proposed approach.The results indicate that the approach shows a substantial reduction in computational costs and holds promising potential for evaluating the seismic fragility of structures exposed to mainshock-aftershock earthquakes.

关 键 词：Mainshock-aftershock earthquakes Seismic fragility analysis Deep learning Gate recurrent unit neural network incremental dynamic analysis 

分 类 号：P31[天文地球—固体地球物理学]