基于DenseNet-GRU的混凝土拱坝变形深度学习预测模型OA

Traditional prediction methods based on statistics or machine learning often struggle to effec-tively capture the complex mapping relationships between the displacement of concrete arch dams and various influencing factors.Thus,a novel deep learning prediction method was proposed.This method integrated densely connected convolutional networks(DenseNet)and gated recurrent unit(GRU)to form a DenseNet-GRU model,aiming to enhance the accuracy and generalization ability of deformation prediction for concrete arch dams.A typical concrete arch dam located in a certain region of China was selected as a case study,and deformation monitoring data from multiple measuring points were used for empirical analysis.The results indicate that the DenseNet-GRU model can accurately simulate the dis-placement deformation process of all monitoring points.Compared with other deep learning models,it demonstrates higher prediction accuracy and stronger generalization capabilities.This research provides an efficient and reliable prediction tool for dam safety monitoring and health management,and holds sig-nificant theoretical and practical implications for the advancement of dam safety management practices.