Effect on AI-driven Ant Lion Optimization framework using fibre reinforced concrete with dual-stage building crack for structural applications

Abstract

The growing demand for sustainable, durable, and crack-resistant construction materials has accelerated research into hybrid Fibre Reinforced Concrete (FRC) systems incorporating Polyvinyl Alcohol (PVA), poly-ethylene (PE), and steel fibres. Challenges persist in achieving early crack detection and designing optimal FRC mixtures with balanced mechanical and durability properties. This study introduces an Explainable AI-Driven Ant Lion Optimization (ALO) framework that integrates deep learning–based crack detection with intelligent FRC mix design optimization. In the first stage, a Deep Convolutional Neural Network (DCNN) combined with Augmented Gradient-weighted Class Activation Mapping (AugX-Grad-CAM) is employed for precise crack localization and interpretability, achieving an average detection accuracy of 92.4%, with a 28% improvement in detection reliability compared to existing CNN models. In the second stage, the ALO algorithm optimizes the proportions of PVA, PE, and steel fibres to enhance tensile strength (+22%), flexural toughness (+25%), and crack resistance (+27%) relative to standard FRC formulations. The optimized FRC microstructure, analyzed through Scanning Electron Microscopy (SEM), confirms improved fibre–matrix bonding and reduced micro-crack propagation. The proposed framework establishes a closed-loop AI–materials integration, linking real-time crack diagnostics with adaptive material optimization. This synergy between explainable AI and nature-inspired optimization presents a scalable pathway toward intelligent, self-improving, and resilient concrete.