The development of the Chinese economy is associated with many changes in vehicle loads, particularly the increasing numbers of heavy trucks and other types of vehicles. However, currently, no standard vehicle has been identified for studying the fatigue design of highway bridges in China. Using the weigh-in-motion system, the authors obtained vehicle loads of 23 Chinese provinces. Miner’s rule for cumulative fatigue damage was then used to categorize each vehicle as one of eight representative types according to load. From these types, five equivalent vehicle models were found. Subsequently, simplified fatigue load spectra for highway bridges were obtained for each province. The six-axle vehicle can be used as the standard fatigue vehicle for most provinces, except in Zhejiang and Guizhou, in which three- and four-axle vehicles are most representative. Also, a standard fatigue vehicle for China is proposed.

This paper presents an experimental study that sought to characterize the effect of corrosion and fatigue load on the behavior of reinforcing steel bars. The surface profiles of corroded steel bars and the relationship between fatigue life and corrosion morphology were investigated. First, the corrosion process was accelerated by applying external direct current. Next, fatigue tests were performed on uncorroded specimens and precorroded specimens, and the results are presented and analyzed. A method for calculating the three-dimensional (3D) corrosion morphology coefficient of corroded steel bar is proposed. The fatigue life of corroded bars is significantly reduced because of corrosion morphology, and a logarithmic linear relationship exists between fatigue life and the corrosion morphology coefficient. Considering the corrosion morphology of corroded steel bars, a method is provided to calculate the fatigue life of a corroded steel bar based on the properties of uncorroded steel bar and the 3D surface morphology coefficient of corroded steel bars. The prediction results for corroded steel bars agree well with the experimental observations.

The reliability of concrete in complex environments is the basis for improving the reliability of concrete structures. A concrete reliability assessment based on multiple degradation factor is proposed. First, chloride penetration and fatigue damage are selected as two degradation parameters for concrete, and series tests are developed to investigate the surface chloride concentration, diffusion coefficient, and degradation of residual strength under coupled effect of chloride penetration and fatigue. Then, two marginal distribution functions of the durability reliability of the concrete are deduced based on two failure modes. Subsequently, fatigue loading the parameters of different typical copula functions are obtained by using the maximum likelihood method (MLM), and copula function selection is carried out according to the Akaike information criterion (AIC) criterion. The joint distribution function is then derived by fusing the two marginal distribution functions using the selected copula function, and a remaining durability reliability model is obtained. Finally, the proposed model is verified by experimental data. This method should be considered as a reference for the evaluation and reliability calculation of concrete 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 τ_u alongside intermediate physical quantities including rust expansion stress σ_r, hoop stress σ_θ, and damage variable D_θ. 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.