Deep Learning Algorithms for Identifying Defects in Concrete Structures

Abstract

The presence of cracks is often the first sign of deterioration in concrete pavement. Detectingand addressing these cracks early on is crucial for effective pavement maintenance. Deep learningalgorithms, continuously advancing with improvements in computer hardware, offer a more accurate andautomated approach to crack detection compared to traditional digital image processing methods. Thishas sparked interest in researching concrete pavement crack images, as it promises enhancedresilience. Among deep learning techniques, convolutional neural networks (CNNs) have beenspecifically developed for automatic analysis of concrete surface photographs in crack diagnosisapplications. Despite the high accuracy claimed by deep learning-based systems, it is crucial toconsider an alternative perspective that highlights the neglect of challenges related to obtaining highqualityimages. the limitations of manual inspections, which are time-consuming and subjective, byproposing an alternative approach that streamlines the process. By reducing the reliance on subjectiveexperience, this approach improves efficiency and minimizes the potential for errors in detecting visualindications of stress, wear, and strain, such as cracks and depressions. Such indications, if leftunattended, can gradually lead to failure or collapse, especially when they occur in critical areas likeload-bearing joints.