Integration of Artificial Intelligence and Machine Learning for Predicting the Behaviour of Fibre-Reinforced Concrete Under Complex Loads

Abstract

Fibre-reinforced concrete (FRC) is a widely used construction material, brought on by its improved tensile strength, ductility, and toughness relative to plain concrete. Knowledge of how FRC behaves under complex loading is crucial for delivering mechanical competence and durability in constructions. AI and ML techniques have been extensively applied in predicting the behavior of different materials, including FRC. This article aims to combine AI and ML methods to predict how FRC will behave on complex loads. The advancement in construction materials has gained the rapid acceptance of fibre-reinforced concrete (FRC) due to its better mechanical properties under complicated loading arrangements. Nonetheless, the heterogeneous composition and nonlinear properties of FRC make it a challenging material to accurately predict using a standard relationship through loads. Utilizing supervised, unsupervised, and hybrid ML techniques; the study presents in the civil engineering domain, how AI-based approaches can improve efficiency and innovation.