Understanding the impact of creep on the long-term mechanical features of concrete is crucial, and constructing an accurate prediction model is the key to exploring the development of concrete creep under long-term loads. Therefore, in this study, three machine learning (ML) models, a Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting Machine (XGBoost), are constructed, and the Hybrid Snake Optimization Algorithm (HSOA) is proposed, which can reduce the risk of the ML model falling into the local optimum while improving its prediction performance. Simultaneously, the contributions of the input features are ranked, and the optimal model’s prediction outcomes are explained through SHapley Additive exPlanations (SHAP). The research results show that the optimized SVM, RF, and XGBoost models increase their accuracies on the test set by 9.927%, 9.58%, and 14.1%, respectively, and the XGBoost has the highest precision in forecasting the concrete creep. The verification results of four scenarios confirm that the optimized model can precisely capture the compliance changes in long-term creep, meeting the requirements for forecasting the nature of concrete creep.

Rubber concrete has been applied to a certain extent in fatigue-resistant structures due to its good durability. Based on a cohesive model of rubber composed of a five-phase material containing mortar, aggregate, rubber, aggregate-mortar interfacial transition zone (ITZ), and rubber-mortar ITZ, this paper studies the influence of the cohesive parameters in the rubber-mortar ITZ on the fatigue problem of rubber concrete on the mesoscopic scale. As the weak part of cement-based composite materials, the ITZ has a great influence on the mechanical properties and durability of concrete, but the performance of the ITZ is difficult to test in macro experiments, resulting in difficulties in determining its simulation parameters. Based on the cohesive model with a rubber content of 5%, this study uses Monofactor analysis and the Plackett-Burman test to quickly and effectively determine the primary and secondary influences of the cohesive model parameters in the rubber-mortar ITZ; further, the response surface method is used to optimize the cohesive parameters in the rubber-mortar ITZ, and the numerical simulation results after optimizing the cohesive parameters are compared and analyzed with the simulation results before optimization. The results show that, under the setting of the optimized parameters, the simulation results of each item of the optimal cohesive model parameters in the rubber-mortar ITZ are in line with the reality and closer to the experimental data, and they are also applicable to rubber concrete models with different rubber dosing.

Rubber concrete (RC) has been used in fatigue-resistant components due to its durability, yet the numerical simulation of its fatigue properties remains in its early stages. This study proposes a cohesive zone model (CZM) that accounts for the accumulation of fatigue damage at the mesoscale to investigate the fatigue performance of RC. The model integrates static and fatigue damage in the CZM, effectively capturing damage caused by fatigue loading. Validation was conducted using experimental data from the existing literature. Based on this validation, four concrete beams with varying rubber replacement rates (0%, 5%, 10%, and 15%) were tested. The CZM was employed to describe the mechanical behavior of the interface transition zones (ITZs) and the mortar interior, which were simulated and analyzed under different stress levels. The results demonstrate that the model accurately simulates crack propagation paths, interface damage evolution, and the fatigue life of RC beams under fatigue loading. A functional relationship between fatigue life and stress level was established for various rubber replacement rates. This study provides a reference model for numerical simulations of RC under fatigue loading conditions and introduces new approaches for analyzing the fatigue performance of other materials.