Advanced imaging techniques for damage characterization of concrete

Abstract

Reinforced concrete is widely used for the construction of bridges, buildings, highways, dams, power plants and many other infrastructures. Monitoring the structural health of these infrastructures is essential for their uninterrupted functioning and assessing their deterioration due to loading and environmental factors. The monitoring also helps in the estimation of its load-carrying capacity, serviceability and need for repair. Cracking in concrete structures is one of the critical parameters representing the health of the structure. Concrete cracking occurs due to many reasons like shrinkage, heaving, premature drying, and excessive loading which leads to a reduction in the strength of structures. Trained personnel monitor the development of cracks and their progression at the critical locations of the structures through a physical vision at regular intervals of time. newlineStructures like bridges, buildings, roads, tunnels, historical monuments and many others are inspected at regular intervals to estimate their deterioration and to prevent further accidents which may directly affect human life. Different methods like acoustic, ultrasonic and image processing-based inspection methods have been deployed to carry out an assessment of such concrete structures. Physical inspection of structures for health monitoring is time-consuming, costly and risky. Automatic detection of cracks in concrete structures of various shapes and scales holds worthy importance in the area of structural health monitoring. Advances in image acquisition, processing techniques, and computational resources have made vision systems, a cost-effective and accurate technique for structural health assessment. newlineThe present work is aimed to address the concrete crack detection problem by developing a novel system using machine vision and deep learning. The work covers concrete crack monitoring by identifying the location of the crack, the number of cracks, the length of the cracks, and the area of the cracks. The methodology is applied for crack monito