Deep transfer learning-based time-varying model for deformation monitoring of high earth-rock dams

Abstract

The analysis of deformation, a critical aspect of high earth-rock dams, holds immense importance for ensuring the safety and stability of dam operations. The structural behavior of high earth-rock dams exhibits time-varying nonlinear characteristics influenced by materials and loads. Over time, the fitting and prediction abilities of static dam Structural Health Monitoring (SHM) models tend to diminish. To address this, a novel SHM model is proposed in this study. It leverages deep transfer learning to enhance prediction accuracy and generalization by incorporating a starting point timestamp and employing a transfer learning approach. The methodology begins with the construction of an encoder structure based on the graph convolutional network and long and short-term memory model. Additionally, the attention mechanism-based encoder structure is designed to include starting point time markers. Knowledge migration is then executed through transfer learning, thereby improving the model's generalization to the time-varying deformation challenge. The proposed model is applied to a horizontal displacement monitoring project for a 185.5 m-high panel rockfill dam. Ablation experiments demonstrate that the transfer learning method effectively enhances the model's handling of time-varying deformation by improving prediction accuracy, with a more pronounced effect observed for measurement points close to the top of the dam and the upstream dam face. Comparison with eight baseline models validates that the proposed model achieves optimal prediction, fitting performance, and generalization. Consequently, the model emerges as a more suitable choice for the deformation health monitoring of high earth-rock dam projects.