Monitoring and automatic characterization of cracks in strain-hardening cementitious composite (SHCC) through intelligent interpretation of photos. (1st August 2022)

• Record Type: Journal Article
• Authors: Guo, Pengwei; Meng, Xiangjun; Meng, Weina; Bao, Yi
• Is Part Of: Composites. Number 242(2022)
• Publication Date: 2022-08-01
• Subjects: Binocular stereo vision -- Computer vision -- Crack detection -- Crack quantification -- Deep learning -- Strain-hardening cementitious composites (SHCC); Composite materials -- Periodicals; Materials science -- Periodicals; Composite materials; Periodicals; Electronic journals; 620.118
• DOI: 10.1016/j.compositesb.2022.110096
• Languages: English
• ISSNs: 1359-8368

Abstract

Abstract: This paper presents an intelligent photo interpretation approach to automatically monitor and characterize dense interconnected microcracks in strain-hardening cementitious composite (SHCC) 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. The presented approach was implemented into evaluation of SHCC in flexural tests and direct tension tests. 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. The interpretation time was shorter than 0.4 s for each photo, making the approach promising for monitoring of SHCC. The proposed system can be deployed for automated assessment of cementitious composites with complex crack patterns in material research and engineering structures. Highlights: Complex cracks in strain-hardening cementitious composites are intelligently characterized. Ranging is incorporated to enable automatic photo interpretation without human intervention. Real-time assessment of crack width and crack number is achieved. The proposed approach showed high accuracy in crack detection and quantification.