Deformation Modeling and Prediction of Concrete Dam Using Observed Air Temperature and Enhanced CatBoost Algorithm

Accurate prediction of concrete dam deformation is essential for ensuring structural safety and operational efficiency. This study presents a novel approach for monitoring and predicting concrete dam deformation using observed air temperature data, intelligent optimization, and machine learning techniques. To address the limitations of traditional statistical models in simulating the thermal effects on dam body deformation, this study proposes an improved hydraulic–air temperature–time (HTairT) deformation monitoring model. This model leverages long-term air temperature data and its lagged terms as critical input variables, enabling a more comprehensive understanding of thermal impacts on dam deformation. To capture the complex, nonlinear relationships between environmental factors and dam deformation behavior, we introduce the high-performance CatBoost gradient-boosting algorithm as a regressor. An enhanced Particle Swarm Optimization (PSO) algorithm is utilized for optimizing CatBoost’s parameters, enhancing the model’s predictive accuracy. A high concrete dam, currently in service, is selected as the case study, where two representative deformation monitoring points are used for validation. This research fills a gap by combining CatBoost with an optimized PSO in a deformation monitoring model, providing a novel approach that improves predictive reliability in long-term dam safety monitoring. Experimental results show that the enhanced PSO-optimized CatBoost algorithm achieves higher R2 and lower MSE and MAE values in multiple monitoring points. compared with other benchmark methods Moreover, the importance of factors affecting deformation can be identified using the proposed method, and experimental results indicate that water level and average air temperature of 1–2 days, 3–7 days, and 30–60 days are key factors affecting the deformation of high concrete arch dams.