Intelligent Prediction of Freeze–Thaw Damage and Auxiliary Mix Proportion Design for Steel Fibre Phase-Change Concrete for Cold Region Airport Pavements

Freeze–thaw damage significantly reduces the performance and durability of airport pavements in cold regions. Traditional assessment methods, such as the F300 freeze–thaw test, are time-consuming and hinder rapid optimisation of mix design. In addition, previous studies have mostly relied on long-term laboratory testing and have evaluated phase-change concrete (PCC) independently, without considering synergistic effects. These approaches lack fast, synergy-aware predictive capability and interpretable tools for mix proportion design, resulting in a gap between laboratory research and practical engineering applications. To address this issue, this study proposes an intelligent and explainable framework for predicting freeze–thaw damage and guiding mix design of steel fibre-reinforced phase-change concrete (SF–PCC). A boundary-controlled experimental programme was first conducted, varying steel fibre (SF) content from 0 to 1.2% and phase-change material (PCM) content from 0 to 12% under fixed mixture conditions. The freeze–thaw test results were recorded sequentially and used to construct a supervised learning dataset. Then, an XGBoost model was developed to predict two key durability indicators: relative dynamic modulus of elasticity (RDEM) and mass loss. SHAP (SHapley Additive exPlanations) analysis was further applied to quantify feature importance and interaction effects. The model achieved high predictive accuracy (R2 = 0.9938 for mass loss and R2 = 0.9935 for RDEM) under controlled experimental conditions. After 300 freeze–thaw cycles, the reference mix exhibited an RDEM of 61.2%, while optimised configurations showed improved performance. The economical design (9% PCM + 0.9% SF) achieved an RDEM of 66.8%, and the high-performance design (12% PCM + 1.2% SF) reached 72.6%. These results demonstrate that the proposed framework can effectively enhance durability and support rapid preliminary decision-making. The framework significantly accelerates freeze–thaw performance evaluation by enabling near-instant prediction and serves as an efficient supplementary tool for mix design optimisation alongside conventional laboratory testing. It also provides interpretable, data-driven insights for the design of freeze–thaw-resistant airport pavement concrete in cold regions.