A Novel Method of Self-Healing Concrete to Improve Durability and Extend the Service Life of Civil Infrastructure

Abstract

Concrete civil infrastructure often suffers severe damage to its internal structure due to insufficient durability in high and cold complex environments, affecting the service life of the infrastructure. Therefore, a novel method based on nanofillers for self-healing concrete is proposed to optimize the durability mix proportion of high-performance concrete in complex high and cold environments, which improves the strength recovery rate of concrete. Moreover, a concrete durability prediction model based on particle swarm optimization-least squares support vector machine (PSO-LSSVM) and improved NSGA-II (nondominated sorting genetic algorithm II) algorithm was proposed to quickly and accurately determine the optimization scheme of self-healing concrete mix proportion. First, the model employs PSO-LSSVM to achieve highly accurate predictions of relative dynamic elastic modulus and chloride ion permeability coefficient, which are key indicators of the concrete durability. Subsequently, the predicted regression functions for concrete durability are utilized as fitness functions, and the improved NSGA-II algorithm is employed to obtain the optimal mix ratio for durable concrete. Finally, the Pareto frontier solution set is processed using the ideal point method selection approach to determine the optimal concrete mix ratio scheme. To assess the self-healing ability of the proposed concrete, a concrete durability test study is conducted. Experimental research shows that the durability of the proposed self-healing concrete has been significantly improved. The optimized PSO-LSSVM model demonstrates excellent generalization capability. The coefficient of determination between the predicted and actual values in the test dataset is 0.9357, and the root-mean-square error is 0.10267. Building upon this, the enhanced concrete durability prediction model based on the NSGA-II algorithm proves to be highly effective in predicting the optimal concrete mix proportion scheme. The predicted values of chloride ion permeability coefficient and relative dynamic elastic modulus of the proposed model differ from the actual experimental values by 1.29% and 0.59%, significantly better than the other prediction models.