Reinforced concrete (RC) is the result of a combination of steel reinforcing rods (which have high tensile) and concrete (which has high compressive strength). Additionally, the prediction of long-term deformations of RC flexural structures and the magnitude of the influence of the relevant material and geometric parameters are important for evaluating their serviceability and safety throughout their life cycles. Empirical methods for predicting the long-term deformation of RC structures are limited due to the difficulty of considering all the influencing factors. In this study, four popular surrogate models, i.e., polynomial chaos expansion (PCE), support vector regression (SVR), Kriging, and radial basis function (RBF), are used to predict the long-term deformation of RC structures. The surrogate models were developed and evaluated using RC simply supported beam examples, and experimental datasets were collected for comparison with common machine learning models (back propagation neural network (BP), multilayer perceptron (MLP), decision tree (DT) and linear regression (LR)). The models were tested using the statistical metrics ${\mathrm{R}}^2$, $\mathrm{R}\mathrm{A}\mathrm{A}\mathrm{E}$, $\mathrm{R}\mathrm{M}\mathrm{A}\mathrm{E}$, $\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}, \mathrm{V}\mathrm{A}\mathrm{F}, \mathrm{P}\mathrm{I}, {\mathrm{A}}_{10-\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{e}\mathrm{x}} \mathrm{a}\mathrm{n}\mathrm{d} {\mathrm{U}}_{95}$. The results show that all four proposed models can effectively predict the deformation of RC structures, with PCE and SVR having the best accuracy, followed by the Kriging model and RBF. Moreover, the prediction accuracy of the surrogate model is much lower than that of the empirical method and the machine learning model in terms of the RMSE. Furthermore, a global sensitivity analysis of the material and geometric parameters affecting structural deflection using PCE is proposed. It was found that the geometric parameters are more influential than the material parameters. Additionally, there is a coupling effect between material and geometric parameters that works together to influence the long-term deflection of RC structures.

Extended exposure to seawater results in the erosion of the structural high-strength steels utilized in marine equipment, primarily due to the infiltration of hydrogen. Consequently, this erosion leads to a decrease in the mechanical properties of the material. In this investigation, the mechanical responses of Q690 structural high-strength steel specimens were investigated by considering various hydrogen charging parameters, such as the current density, charging duration, and solution concentration values. The findings highlighted the significant impacts of electrochemical hydrogen charging parameters on the mechanical behaviors of Q690 steel samples. Specifically, a linear relationship was observed between the mechanical properties and the hydrogen charging current densities, while the associations with the charging duration and solution concentration were nonlinear. Additionally, the fracture morphology under various hydrogen charging parameters was analyzed and discussed. The results demonstrate that the mechanical properties of the material degrade with increasing hydrogen charging parameters, with tensile strength and yield stress decreasing by approximately 2–4%, and elongation after fracture reducing by about 20%. The findings also reveal that macroscopic fractures exhibit significant necking in uncharged conditions. As hydrogen charging parameters increase, macroscopic necking gradually diminishes, the number of microscopic dimples decreases, and the material ultimately transitions to a fully brittle fracture.