AI-Based Structural Damage Detection in Civil Infrastructure

Abstract

Structural Health Monitoring (SHM) has become increasingly important in civil engineering due to the aging of infrastructure such as bridges, buildings, tunnels, and dams. Traditional inspection methods rely on manual visual assessments, which are time-consuming, labor-intensive, and often unable to detect early-stage damage. Recent advances in Artificial Intelligence (AI) have enabled the development of intelligent damage detection systems that improve the accuracy and efficiency of structural assessments. AI techniques, including Machine Learning, Deep Learning, Computer Vision, and Pattern Recognition, analyze sensor data, vibration signals, and images to identify defects and predict structural failures. This study presents a comprehensive survey of AI-based damage detection methods for civil infrastructure. The proposed framework integrates sensor data acquisition, feature extraction, machine learning classification, and automated damage assessment. Various AI algorithms such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), Decision Trees (DT), and Convolutional Neural Networks (CNN) are evaluated for detecting structural deterioration. Experimental results demonstrate that AI-based approaches, particularly deep learning models, achieve higher damage detection accuracy than conventional inspection techniques. Furthermore, AI-powered SHM systems enable real-time monitoring, reduce maintenance costs, and support proactive infrastructure management, highlighting their potential to transform modern civil infrastructure maintenance and safety.