水力发电学报2025，Vol.44Issue(1)：41-53,13.DOI:10.11660/slfdxb.20250104
沥青混凝土心墙砂砾石坝的地震响应预测模型与应用
Seismic response prediction model of asphalt concrete core sand-gravel dams and its application
摘要
Abstract
Although deep learning has been widely used to predict the nonlinear seismic response of structures,how to construct its network framework and how to select its hyperparameters are still controversial issues,because either of them may lead to problems such as low computational efficiency and low-accuracy predictions.The seismic response of asphalt concrete core sand-gravel(ACCSG)dams is usually depicted by a data series,which can actually be mined and predicted by a time series prediction model.This paper presents a long short-term memory(LSTM)neural network model that is based on the genetic algorithm(GA)and the particle swarm optimization(PSO)algorithm.This GAPSO-LSTM model overcomes the drawback of low prediction accuracy caused by difficulty in determining the hyperparameters of the traditional network structure,and achieves the accurate prediction goal of the nonlinear dynamic response of an ACCSG dam.Its prediction accuracy is compared with the convolutional neural network(CNN)model,LSTM single neural network model,and PSO-LSTM neural network model without GA optimization.The results show that compared with the other network models,the GAPSO-LSTM network model has higher prediction accuracy for the seismic response of an ACCSG dam.It overcomes the blindness of subjective selection of hyperparameters,and relieves the local convergence problem of the PSO algorithm,thus providing a new idea for seismic performance evaluation of ACCSG dams.关键词
沥青混凝土心墙砂砾石坝/地震响应预测/长短时记忆网络/遗传算法/粒子群算法/时间序列Key words
asphalt concrete core sand-gravel dam/seismic response prediction/long short-term memory/genetic algorithm/particle swarm optimization algorithm/time series分类
建筑与水利引用本文复制引用
杜敏,张社荣,王超,路彤..沥青混凝土心墙砂砾石坝的地震响应预测模型与应用[J].水力发电学报,2025,44(1):41-53,13.基金项目
国家自然科学基金项目(51979188) （51979188）
云南省重大科技专项计划项目(202102AF080001) （202102AF080001）
