Uncertainty-informed regional deformation diagnosis of arch dams

Abstract

Accurately predicting dam deformation is crucial for understanding its operational status. However, existing models struggle to effectively capture the spatiotemporal correlations in monitoring data and quantify uncertainty within dam systems. This paper presents an innovative uncertainty quantification model for evaluating regional deformation in arch dams. First, a method to extract the spatiotemporal correlation features is proposed. Considering the multidimensional deformation at measurement points, correlations among various points are analyzed through improved self-organizing map clustering and federated Kalman filtering. Second, a temporal convolutional network is employed for improved lower and upper bound estimation, and a quality-driven loss function is adopted to optimize model parameters. Finally, engineering case studies demonstrate that this model can generate reliable prediction intervals for regional deformation, thereby aiding in risk analysis and diagnostics.