Implementation of Computer Vision technique for Crack Monitoring in Concrete Structure
Abstract
Assessment of structural health is essential for safe and efficient functioning of built environment. Physical inspection of structures for its health monitoring is time consuming, costly and risky. Advances in image capturing and processing techniques as well as numerical simulation tools have made computer vision a cost effective and accurate alternative for structural health assessment. Evolution of convolution neural network (CNN) has reduced human effort and made it easy to develop algorithms for identification of structure defects. One of the primary defects in concrete is crack. Concrete cracking occurs due to many reasons like shrinkage, heaving, premature drying, excessive loading etc. and it leads to reduction in strength of structures. This paper presents a computer vision system developed for crack monitoring of concrete cubes subjected to compressive loading. Camera is used to capture real time images when concrete cubes are subjected to loading. Images are further processed using CNN to obtain various features of cracks like numbers, location, length, area etc. Present Computer vision system is developed using LabVIEW and implemented using tensor processing unit (TPU) for better computational efficiency. The outcome of present system demonstrated better and accurate real time monitoring of cracking when concrete is subjected to loading. Proposed computer vision system can be implemented for structural health monitors of real-life civil engineering structures like buildings and bridges.
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