A unified COMSOL framework for comparative analysis of DNN, GP and PCE in predicting bridge deck temperature and icing state

To improve bridge deck icing warning, this study investigates two related tasks, namely bridge deck temperature prediction and bridge deck icing-state prediction, within a unified surrogate-modeling framework. Three representative surrogate models, including deep neural network (DNN), Gaussian process (GP), and polynomial chaos expansion (PCE), are established and compared in COMSOL under consistent workflows, datasets, and evaluation metrics. For bridge deck temperature prediction, real monitoring data collected from a bridge in Shandong Province are used. For bridge deck icing-state prediction, both a MATLAB-generated synthetic dataset and a real dataset derived from monitored samples through physical labeling and class balancing are considered. The results show that all three surrogate models can effectively predict bridge deck temperature, among which GP achieves the best overall regression performance, while DNN performs comparably well. For icing-state prediction, DNN exhibits the best overall classification performance on both the synthetic and real datasets. GP and PCE show relatively stronger ability to identify icy samples on the real dataset, but this advantage is accompanied by an increased probability of false alarms. The results indicate that the suitability of surrogate models is task-dependent: GP is more advantageous for bridge deck temperature regression, whereas DNN provides a better balance of accuracy, robustness, and generalization for bridge deck icing-state prediction. This study provides a useful reference for surrogate-model selection in bridge deck icing warning.