The addition of rubber to concrete improves resistance to chloride ion attacks. Therefore, rapidly determining the chloride permeability coefficient (D_CI) of rubber concrete (RC) can contribute to promotion in coastal areas. Most current methods for determining D_CI of RC are traditional, which cannot account for multi-factorial effects and suffer from low prediction accuracy. Machine learning (ML) techniques have good non-linear learning capabilities and can consider the effects of multiple factors compared with traditional methods. However, ML models easily fall into the local optimum due to their parameters’ influence. Therefore, a mixed whale optimization algorithm (MWOA) was developed in this paper to optimize ML models. The main strategies are to introduce Tent mapping to expand the search range of the algorithm, to use an adaptive t-distribution dimension-by-dimensional variation strategy to perturb the optimal fitness individual to thereby improve the algorithm’s ability to jump out of the local optimum, and to introduce adaptive weights and adaptive probability threshold values to enhance the adaptive capacity of the algorithm. For this purpose, data were collected from the published literature. Three machine learning models, Extreme Learning Machine (ELM), Random Forest (RF), and Elman Neural Network (ELMAN), were built to predict the D_CI of RC, and the three models were optimized using MWOA. The calculations show that the MWOA is effective with the optimized ELM, RF, and ELMAN models improving the prediction accuracy by 54.4%, 62.9%, and 36.4% compared with the initial model. The MWOA-ELM model was found to be the optimal model after a comparative analysis. The accuracy of the multiple linear regression model (MRL) and the traditional mathematical model is calculated to be 87.15% and 85.03%, which is lower than that of the MWOA-ELM model. This indicates that the ML model that is optimized using the improved whale optimization algorithm has better predictive ability than traditional models, providing a new option for predicting the D_CI of RC.

Rubber concrete (RC) exhibits high durability due to the rubber admixture. It is widely used in a large number of fatigue-resistant structures. Mesoscale studies are used to study the composition of polymers, but there is no method for fatigue simulation of RC. Therefore, this paper presents a finite element modeling approach to study the fatigue problem of RC on the mesoscale, which includes the random generation of the main components of the RC mesoscale structure. We also model the interfacial transition zone (ITZ) of aggregate mortar and the ITZ of rubber mortar. This paper combines the theory of concrete damage to plastic with the method of zero-thickness cohesive elements in the ITZ, and it is a new numerical approach. The results show that the model can simulate reasonably well the random damage pattern after RC beam load damage. The damage occurred in the middle of the beam span and tended to follow the ITZ. The model can predict the fatigue life of RC under various loads.

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.

In practical engineering applications, the use of low-carbon concrete materials is in line with the principles of sustainable development and helps to reduce the impact on the environment. Creep effects are particularly critical in the research on such materials. However, traditional characterization methods are time-consuming and often fail to account for the interactions of multiple factors. This study constructs a time-series database capturing the behavioral characteristics of low-carbon concrete materials over time. Three temporal prediction models—Artificial Neural Network (ANN), Random Forest (RF), and Long Short-Term Memory (LSTM) networks—were retrained for creep prediction. To address limitations in model architecture and algorithmic frameworks, an enhanced Adaptive Crowned Porcupine Optimization algorithm (ACCPO) was implemented. The improved performance of the ACCPO was validated using four diverse benchmark test functions. Post-optimization results showed remarkable improvements. For ANN, RF, and LSTM, single-metric accuracies increased by 20%, 19%, and 6%, reaching final values of 95.9%, 93.9%, and 97.8%, respectively. Comprehensive evaluation metrics revealed error reductions of 22.6%, 7.9%, and 8% across the respective models. These results confirm the rationality of the proposed temporal modeling framework and the effectiveness of the ACCPO algorithm. Among them, the ACCPO-LSTM time series model is the best choice.

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.

Non-uniform corrosion cracking in reinforced concrete buildings constitutes a fundamental difficulty resulting in durability failure. This work develops a microscopic-scale multi-species electrochemical phase field model to tackle this issue. The model comprehensively examines the spatiotemporal coupling mechanisms of the full “corrosion-rust swelling-cracking” process by integrating electrochemical reaction kinetics, multi-ion transport processes, and a unified phase field fracture theory. The model uses local corrosion current density as the primary variable to accurately measure the dynamic interactions among electrochemical processes, ion transport, and rust product precipitation. It incorporates phase field method simulations of fracture initiation and propagation in concrete, establishing a bidirectional link between rust swelling stress and crack development. Experimental validation confirms that the model’s predictions about cracking duration, crack shape, and ion concentration distribution align well with empirical data, substantiating the efficacy of local corrosion current density as an indicator of electrochemical reaction rate. Parametric studies were performed to examine the effects of interface transition zone strength, oxygen diffusion coefficient, protective layer thickness, reinforcing bar diameter, and reinforcing bar configuration on cracking patterns. This model’s multi-physics field coupling framework, influenced by dynamic corrosion current density, facilitates cross-field interactions, offering sophisticated theoretical tools and technical support for the quantitative analysis, durability evaluation, and protective design of corrosion-induced cracking in reinforced concrete structures.