Inversion method for working behavior of prestressed anchor cables in dam foundation driven by structural health monitoring data

Abstract

Accurate knowledge of the working behavior of prestressed anchor cables is essential to ensure the long-term safe operation of dams. However, due to the lack of effective monitoring means, it is hard to analyze the working performance of anchor cables, and there is still a lack of methods to accurately quantify the anchorage effectiveness of prestressed anchor cables in actual hydraulic engineering projects. To solve this problem, this paper presents an inversion method to accurately quantify the behavior of prestressed anchor cables in dam foundations using structural health monitoring data. Firstly, Ensemble Empirical Mode Decomposition (EEMD) and Singular Spectrum Analysis (SSA) are used to denoise monitoring sequences. Secondly, the hydraulic pressure deformation component is separated by constructing a statistical model reflecting the relationship between dam deformation and environmental factors. Meanwhile, a three-dimensional finite element model (FEM) of the dam and its foundation is established in which the water load and anchoring force are applied step by step to simulate the deformation values of the dam under different combinations of working conditions. And a BP neural network is used to fit the finite element forward process. Finally, the inferred values of the anchorage force are derived by comparing the deformation separated by the statistical model with the deformation calculated by the FEM. In order to minimize the fitting difference of the two series of deformations, an improved grey wolf optimization algorithm (IGWO) is introduced to optimize the inverse calculation. The method is applied to a multiple arch dam foundation anchorage project, and its feasibility and effectiveness are verified. The proposed method is capable of providing effective support for ensuring the safe service of prestressed anchor cables of dams.