Advanced AI Techniques for Autonomous Crack Detection and Failure Prediction in Concrete Structures

Abstract

The durability and safety of concrete structures are crucial in civil engineering, requiring regular inspection and maintenance to prevent catastrophic failures. Traditional crack detection methods rely on manual visual inspections, which are time-consuming, labor-intensive, and susceptible to human errors. To overcome these limitations, this study presents an AI-driven autonomous crack detection and failure prediction system based on Convolutional Neural Networks (CNNs). The proposed deep learning model is trained on a dataset comprising four distinct categories: Without Crack, Longitudinal Crack, Oblique Crack, and Transverse Crack. By leveraging CNN-based feature extraction and classification, the system accurately identifies different crack types and provides predictive insights into structural health. The experimental results demonstrate that the model achieves high precision and recall, making it a reliable tool for real-time monitoring and preventive maintenance of concrete infrastructure. This research contributes to the advancement of structural health monitoring (SHM) by integrating artificial intelligence (AI) with civil engineering practices, thereby reducing human dependency, enhancing inspection efficiency, and ensuring long-term structural safety.