Multi-output displacement health monitoring model for concrete gravity dam in severely cold region based on clustering of measured dam temperature field

Constructing an accurate dam displacement health monitoring (DHM) model is crucial to ensure the safety of the dam. However, previous studies on DHM focused on the analysis and prediction of a single measurement point, with little work on multiple measurement points, which leads to low efficiency in evaluating the overall status of dams. Furthermore, the majority of these models are based on hydraulic engineering in moderate climatic areas, which results in low accuracy when applied to severely cold regions. To address these issues, the HT c T model is proposed based on full consideration of extreme climate and engineering measures in cold regions, which replaces the commonly used harmonic function or air temperature with the features extracted from the measured temperature field after clustering as a temperature factor. The method effectively overcomes multicollinearity while ensuring accuracy. Moreover, multi-output least-square support vector regression (MOLSSVR), a multi-output model that can forecast multiple measurement points simultaneously, is proposed. By combining it with the HT c T, the efficiency of the model is significantly improved. In addition, grey wolf optimization (GWO) is introduced to search for the optimal hyperparameters of the coupling model. The feasibility, accuracy, robustness, and long-term predictive capability of the proposed model are validated with measured displacements of a concrete gravity dam in a severely cold region. The results show that the proposed model outperforms the four popular machine learning (ML) models, including two support vector regression (SVR)-based models and two neural network-based models, which illustrate that the proposed model has outstanding accuracy and excellent long-term predictive capability. It provides an accurate and efficient novel approach for dam displacement safety monitoring in severely cold regions.

Automated summary

Constructing an accurate dam displacement health monitoring (DHM) model is crucial to ensure the safety of the dam. However, previous studies on DHM focused on the analysis and prediction of a single measurement point, with little work on multiple measurement points, which leads to low efficiency in evaluating the overall status of dams. To address these issues, the HTcT model is proposed based on full consideration of extreme climate and engineering measures in cold regions, and the multi-output least-square support vector regression (MOLSSVR) is introduced to forecast multiple measurement points simultaneously. The proposed model outperforms popular machine learning models and provides an accurate and efficient approach for dam displacement safety monitoring in severely cold regions.