Abstract:In this paper, we introduce the deep fully connected neural network into the field of dam deformation prediction. Based on the training samples of dam multi-source monitoring data, we establish a dam deformation prediction model based on deep fully connected neural network. We use several common deep optimization learning algorithms to optimize the model training, select the optimal learning algorithm by comparing the change curves of each loss function, and further construct a deep fully connected neural network dam deformation prediction model based on the optimal learning algorithm. Finally, we test and analyze the model in combination with the test samples of the dam multi-source monitoring data, and compare the prediction results with those of the traditional BP neural network. The research shows that the deep fully connected neural network in this paper has high prediction accuracy and strong practicability. It can provide a certain reference value for dam safety monitoring.
YANG Heng,YUE Jianping,XING Yin et al. Research on Dam Deformation Prediction Based on Deep Fully Connected Neural Network[J]. jgg, 2021, 41(2): 162-166.