Prediction of crack repair percentage in self-healing concrete using machine learning.

ABSTRACT: Concrete is the most widely used construction material, yet it is highly susceptible to micro-crack formation, which can critically reduce its durability and structural performance. This challenge has drawn significant research interest toward the self-healing capability of concrete as a sustainable solution. However, the effectiveness of self-healing in concrete is still mainly evaluated through time-consuming and costly experimental procedures, with no universally accepted standard methods, limiting its practical implementation. To address this problem and harness the potential of artificial intelligence for modeling complex nonlinear behaviors, this study develops three hybrid predictive models: artificial neural network (ANN) optimized with genetic algorithm (GA), particle swarm optimi

SUBMITTER: Khosravi H 

PROVIDER: S-EPMC12775066 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature