Monitoring and automatic characterization of cracks in strain-hardening cementitious composite (SHCC) through intelligent interpretation of photos

Pengwei Guo, Xiangjun Meng, Weina Meng, Yi Bao

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

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.

Original languageEnglish
Article number110096
JournalComposites Part B: Engineering
Volume242
DOIs
StatePublished - 1 Aug 2022

Keywords

  • Binocular stereo vision
  • Computer vision
  • Crack detection
  • Crack quantification
  • Deep learning
  • Strain-hardening cementitious composites (SHCC)

Fingerprint

Dive into the research topics of 'Monitoring and automatic characterization of cracks in strain-hardening cementitious composite (SHCC) through intelligent interpretation of photos'. Together they form a unique fingerprint.

Cite this