Traditional methods of structural or equipment fragility analysis require a large number of numerical simulation samples,which is not applicable to large scale nuclear power structures.Therefore,an efficient technique for the fragility analysis of nuclear power equipment is developed in this paper.Firstly,the random ground motion-soil-structure samples are constructed based on the Lat-in method,and an efficient time-domain SSI partitioned parallel calculation method is used to obtain the seismic response of partial samples.Then,the neural network model(ANN)is trained with limited numerical simulation results,and by quantifying the prediction error and accuracy index of the ANN.An adaptive algorithm is used for subsequent numerical simulation and ANN training until the ac-curacy threshold requirements are all met.This technique can optimize the selection of calculation samples,control the number of samples for numerical simulation,and improve the calculation efficiency of fragility analysis.In addition,this paper integrates the ANN uncertainty into the calculation formula of the fragility curve,and conducts the fragility analysis of a nuclear power equipment based on the logarithmic regression method and the Mento Carlo incremental method,which verifies the ANN uncertainty.
关键词
易损性分析/人工神经网络/蒙特卡洛法/土-结构相互作用/核电设备
Key words
Fragility analysis/Artificial neural network/MC method/SSI/Nuclear power equipment
维普
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