Deformation prediction is the key for safety monitoring and health assessment for rockfill dams.Current research mostly focuses on single-point deformation prediction models,neglecting the multi-point correlation for the overall modeling.Besides,it is challenging for current models to achieve long-term accurate prediction of drift deformation data.Considering temporal dependence of time series and spatial correlation between multipoint for the deformation of rockfill dams,a spatial-temporal fusion model based on Graph Convolutional Network(GCN)and Recurrent Neural Network(RNN)is proposed for deformation prediction,introducing probabilistic prediction and full-process training.Firstly,the model adaptively performs multipoint features fusion using GCN.Then,the transmissibility of cell states and hidden memories along the time axis in RNN is utilized to realize the mining and fusion of spatial-temporal information.Finally,the parameters of the probabilistic prediction are obtained as linear layer output to improve the model's robustness against noise in monitoring data.In order to enhance its ability to understand the intrinsic relationship between influencing factors and cumulative deformation,the model adopts a full-process training and inference technique,which realizes long-term accurate prediction for drift deformation data.Taking Shuibuya concrete-faced rockfill dam as a study case,we conduct comparison and ablation experi-ment,then present three specific applications of this model in safety monitoring and health assessment for rockfill dams.Our results demonstrate that the model successfully integrates the spatial-temporal information,significantly improving prediction accuracy compared to current models.It addresses the challenges of learning the general law properly and predicting drift deformation data accurately of rockfill dams,and can be applied for long-term deform-ation prediction,anomaly detection and missing data completion of measurement points.
维普
万方数据