A Physics–Data Dual-Driven PINN Framework Incorporating Non-Uniform Rust Expansion for Predicting Bond Strength of Corroded RC Structures

Bond strength degradation of corroded reinforced concrete (RC) structures involves complex mechanical interactions that are difficult to capture through experimental or purely data-driven methods alone. This study proposes a physics-informed neural network (PINN) framework that embeds a thin-walled cylinder rust expansion model as differentiable soft constraints in the loss function, achieving deep integration of mechanical priors with data-driven learning. The PINN predicts the bond strength τu alongside intermediate physical quantities including rust expansion stress σr, hoop stress σθ, and damage variable Dθ. A four-stage progressive training strategy is adopted to balance data fitting and physical consistency. The results show that the proposed PINN improves R2 by 5.49–15.66% and reduces SMAPE by 33.91–58.87% compared to XGBoost, RF, and SVM, while achieving an average prediction error of 1.66% in experimental validation. Under the cover thickness extrapolation scenario (15 mm → 10 mm), the PINN maintains prediction errors within 1.5%, significantly outperforming both ML models and empirical formulas. The framework’s interpretability is further demonstrated through decomposition of the bond strength into frictional and mechanical interlock components, providing mechanistic insight into the corrosion-induced failure process.