Data–Physics-Driven Multi-Point Hybrid Deformation Monitoring Model Based on Bayesian Optimization Algorithm–Light Gradient-Boosting Machine

Single-point deformation monitoring models fail to reflect the structural integrity of the concrete gravity dams, and traditional regression methods also have shortcomings in capturing complex nonlinear relationships among variables. To solve these problems, this paper develops a data–physics-driven multi-point hybrid deformation monitoring model based on Bayesian Optimization Algorithm–Light Gradient-Boosting Machine (BOA-LightGBM). Building upon conventional single-point models, spatial coordinates are incorporated as explanatory variables to derive a multi-point deformation monitoring model that accounts for spatial correlations. Subsequently, the finite element method (FEM) is employed to simulate the hydrostatic component at each monitoring point under actual reservoir water levels. Finally, a hybrid model is constructed by integrating the derived mathematical expression, simulated hydrostatic components, and the BOA-LightGBM algorithm. A case study demonstrates that the proposed model effectively incorporates spatial deformation characteristics within dam sections and achieves satisfactory fitting and prediction accuracy compared to traditional single-point monitoring models. With further refinement and extension, the proposed modeling theory and methodology presented in this study can also provide valuable references for safety monitoring of other hydrostatic structures.