Deep Learning Aided Crack Identification and Quantification of Microcracks of Ultra-High- Performance Fiber Reinforced Cementitious Composite

Abstract

This research presents an intelligent photo interpretation approach to automatically monitor and characterize dense interconnected microcracks in ultra-high- performance fiber reinforced cementitious composite (UHPFRCC) featuring unique crack patterns in terms of crack number and crack width. The presented approach employs a stereo vision system that integrates binocular and monocular cameras for automatic detection, ranging, and quantification of cracks as well as characterization of crack patterns in real time. This research implemented the presented approach into the evaluation of UHPFRCC in flexural tests and direct tension tests, respectively. Dense microcracks were detected and ranged by the stereo vision system, segmented by an encoder-decoder approach, and quantified by an efficient computer vision approach. Evolution of the cracks was traced throughout the loading process until failure, and a statistical analysis revealed that the crack width was retained while the crack number monotonically increased.