Deep learning model for displacement monitoring of super high arch dams based on measured temperature data

Abstract

Dam displacement, an important indicator for the health monitoring of dam structures, can effectively reflect its operational status. Displacement prediction models based on measured data are currently an important tool for dam safety monitoring. However, a significant portion of current models rely on statistical or shallow machine-based approaches, posing challenges in extracting comprehensive and impactful features from environmental factors. To address the above problems, this paper proposes a novel deep learning model that combines Inception architectures with residual connections (Inception-ResNet) and Gate Recurrent Units (GRU). This model employs improved Inception-ResNet blocks with channel attention and spatial attention modules to extract features from dam deformation-related environmental factors sequences at multiple scales. Subsequently, GRU is utilized to learn from long-term dependencies. The proposed model fully combines the remarkable feature extraction capability of the Inception-ResNet block with the learning capability of GRU for long-term dependencies. The availability of the proposed model is tested with measured data of a super high arch dam. The experimental results show that the proposed model outperforms two typical shallow machine learning methods and two typical deep learning models in the four typical monitoring points selected, which demonstrates convincingly that the proposed model is able to predict dam deformation with high accuracy and robustness for dam structure safety monitoring.