Inversion of Thermal Parameters and Temperature Field Prediction for Concrete Box Girders Based on BO-XGBoost

To mitigate thermal cracking in concrete box girders during construction, this study introduces an inversion method for thermal parameters by integrating machine learning with finite element simulation. The research aims to accurately identify key thermal parameters—thermal conductivity k, total hydration heat Q0, convection coefficient h, and reaction coefficient m—through an efficient and reliable data-driven approach. An orthogonal experimental design was used to construct a representative sample database, and a Bayesian-optimized XGBoost (BO-XGBoost) model was developed to establish a nonlinear mapping between temperature peaks and thermal parameters. Validated against field monitoring data from a prestressed concrete continuous rigid-frame bridge, the method demonstrated high accuracy: the inversiontemperature curves closely matched measured data, with a maximum peak temperature error of only 1.40 °C (relative error 2.5%). Compared to conventional machine learning models (DT, SVR, BP and LSTM), BO-XGBoost showed superior predictive performance and convergence efficiency. The proposed approach provides a scientific basis for real-time temperature control and crack prevention in concrete box girders and is applicable to temperature field analysis in mass concrete structures.