A Deformation Prediction Model for Arch Dams Integrating Physical Mechanisms and Bayesian Optimization Gaussian Process Regression

Arch dam deformation is a key indicator for evaluating structural safety. Influenced by the coupled effects of multiple factors such as water pressure, temperature, and aging, it exhibits nonlinear and nonstationary characteristics. Traditional statistical models have clear physical meanings but struggle to capture complex nonlinear relationships, while purely data‐driven models offer high predictive accuracy but lack interpretability. This paper proposes an arch dam deformation prediction model that integrates physical mechanisms with Bayesian optimization Gaussian process regression (BGPR). First, based on heat conduction principles, the influence of solar radiation on the dam surface temperature is quantified, and a physically enhanced input feature set is constructed. Second, variational mode decomposition (VMD) is employed to extract the primary trend component of the deformation series, which is combined with historical lagged values and original environmental features to form an augmented feature set. Third, BGPR is utilized for prediction using the augmented features. Finally, through feature grouping contribution analysis, the influence of hydraulic pressure, temperature, and aging components on deformation is quantitatively assessed. Engineering case validation demonstrates that the proposed model outperforms comparative models in both prediction accuracy and interpretability, providing a new technical pathway for arch dam deformation prediction.