Long-term dam behavior prediction with deep learning on graphs - He, Peng; Pan, Jianbo; Li, Yueyang (Journal of computational design and engineering, 2022)
Abstract Dam displacement prediction is one of the most crucial considerations for ensuring the dam’s long-term safe operation. Most existing models focus on predicting individual displacement and ignore the spatial and temporal correlation of data. To address these issues, a novel prediction model based on attention mechanism and graph convolutional network is proposed. To extract the spatial and temporal correlation of the original data, the position embedding and aggregation modules are employed in the prediction model. Through the aggregation module, a spatial-temporal graph is constructed. The spatial-temporal chart connects spatial diagrams of different time steps together. To capture the spatial-temporal features in the constructed graph, a recurrent graph convolutional module is employed. Through the recurrent graph convolutional module, the spatial-temporal features are used to predict the dam displacement. For verification, an arch dam is taken as an example. Comparing eight baseline models, the proposed model is more effective than other prediction models. Therefore, the proposed model can be adapted for engineering applications. Graphical Abstract Graphical Abstract