Multi-zone parametric inverse analysis of super high arch dams using deep learning networks based on measured displacements

Abstract

Parametric inverse analysis/identification provides significant information for structural damage detection and construction in dam engineering. The main challenge in inverse analysis is to enhance the computational accuracy and efficiency for complex structures, especially for super high arch dams with many zone parameters. This study developed a high-precision deep learning-based surrogate model for rapid inverse analysis of concrete arch dams. The relationship between mechanical parameters and multi-point displacement response is interpreted by convolutional neural networks (CNN)-based surrogate model. The proposed model is integrated with the Latin hypercube sampling and a meta-heuristic optimization algorithm for rapid inverse analysis strategy. The objective function is defined as the distance between the displacement predicted by the surrogate model and the measured displacement. The proposed approach is tested on an actual super high concrete arch dam. Results show that the proposed approach can achieve high accuracy and improve the computational efficiency by 95.83 % compared with the direct finite element method.