ISI Journals

Concrete crack detection and structural health monitoring are critical aspects of ensuring the safety and longevity of infrastructure. Recent advancements in Artificial Intelligence (AI) have significantly improved the accuracy and efficiency of identifying and analyzing structural defects. This paper explores the integration of AI, specifically image segmentation and deep neural networks, into the domain of structural health monitoring. The research outlines the methodologies employed, evaluates the results, and discusses the implications of AI-driven systems on infrastructure management. Through a comprehensive literature review and empirical analysis, the study establishes the superiority of AI approaches over traditional manual inspection methods. Artificial Intelligence (AI) has revolutionized concrete crack detection and structural health monitoring by offering automated, accurate, and efficient solutions for maintaining infrastructure integrity. Leveraging machine learning algorithms and computer vision techniques, AI systems can rapidly analyze images and sensor data to identify, classify, and quantify cracks, even in complex environments. This reduces human error, minimizes inspection time, and enables predictive maintenance, ultimately enhancing the safety and longevity of structures. As AI continues to evolve, its integration with Internet of Things (IoT) devices and advanced data analytics promises a more comprehensive and real-time approach to structural health monitoring, paving the way for smarter and more resilient infrastructure systems.