GA-BP artificial neural networks for predicting the seismic response of arch dams
YU Jingchi1, JIN Aiyun2, PAN Jianwen1, WANG Jinting1, ZHANG Chuhan1
1. State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China; 2. SINOHYDRO BUREAU 9 Co., LTD., Guiyang 550081, China
Abstract：Arch dams may be subjected to strong earthquakes during their lifecycle and their seismic response has attracted extensive attention in dam engineering. Nonlinear finite element seismic response analyses of arch dams require large amounts of computational effort. This paper presents a back propagation (BP) genetic algorithm (GA) method for predict the seismic responses of arch dams which replaces some of the finite element analysis calculations and significantly reduces the computational cost compared with the finite element method. A BP neural network was trained and validated for the Dagangshan arch dam based on 390 nonlinear dynamic response cases calculated using the finite element method with the structural response as the BP neural network output and the seismic intensity parameter, IM, as the input. The results show that the GA-BP neural network can properly predict the dam seismic response and give reasonable seismic response curves using 30% of the 390 cases for training which shows that the GA-BP neural network can save 70% of the nonlinear finite element cost.
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2022, Vol. 62 
