Multi-point hybrid prediction model for concrete dam deformation based on the rapid identification of structural performance evolution state

Abstract

The deformation prediction model of concrete dams provides a scientific basis for long-term safety evaluations. However, traditional models primarily focus on characterizing the weight differences of deformation explanatory factors at a specific moment while ignoring the variations in causality induced by the evolution of structural performance. Additionally, the complex nonlinear mapping relationships significantly increase the difficulty of rapidly identifying the structural performance evolution states across different regions of dams. To address this issue, a multi-point hybrid prediction model for concrete dam deformation based on the rapid identification of structural performance evolution state is proposed in this paper. During the identification process, standard particle swarm optimization is employed to optimize the objective function, while hierarchical surrogate-model-assisted (HSMA) mechanism is proposed to improve the multi-dimensional adaptability by deeply coupling the iteration. With the identification results, a multi-point hybrid model for deformation prediction based on Bayesian optimized support vector regression is constructed. Taking an aging concrete dam as example, the application results indicate that the HSMA mechanism significantly improves the efficiency and accuracy of multi-dimensional identification of structural performance, while the fitting and prediction results of the proposed model exhibit a favorable agreement with the prototype monitoring data. After certain improvements and extensions, this method can be applied to construct the digital twin platforms for water conservancy engineering, providing technical support for monitoring spatiotemporal deformation.