CNN-BiLSTM-based deformation prediction model for extra-high arch dams
To improve the deformation prediction accuracy of extra-high arch dams,a dam deformation prediction model based on convolutional neural network(CNN)and bidirectional long and short-term memory network(BiLSTM)was proposed.The model used CNN to capture the spatial relationship be-tween the data for feature extraction,which was then fed into BiLSTM for the consideration of evolutio-nary patterns in the time dimension.A richer and integrated feature representation was obtained through feature fusion and splicing of fully connected layers,which was finally mapped to the prediction output layer for arch dam deformation prediction.Taking an arch dam as an example,the CNN-BiLSTM mo-del was verified to have high accuracy and stability in evaluation indexes such as RMSE,providing a new idea for the safety monitoring of concrete arch dam structures.
concrete arch damsconvolutional neural networksbidirectional long and short-term memory networkspredictive modeling
万方数据
维普
