Automatic identification and quantification of dense microcracks in high-performance fiber-reinforced cementitious composites through deep learning-based computer vision

Pengwei Guo, Weina Meng, Yi Bao

Research output: Contribution to journalArticlepeer-review

58 Scopus citations

Abstract

High-performance fiber-reinforced cementitious composites (HPFRCCs) feature high mechanical strengths, crack resistance, and durability. Under excessive loading, HPFRCCs demonstrate dense microcracks that are difficult to identify using existing methods. This study presents a computer vision method for identification, quantification, and visualization of microcracks in HPFRCCs based on deep learning. The presented method integrates multiple deep learning models and computer vision techniques in a hierarchical architecture. The crack pattern (e.g., number, width, and spacing of cracks) are automatically determined from pictures without human intervention. This study shows that the presented method achieves an accuracy of 0.992 for crack detection and an accuracy finer than 50 μm (R2 > 0.984) for quantification of crack width when deep learning models are trained using only 200 pictures of HPFRCCs and 200 pictures of conventional concrete with incorporation of data augmentation. The presented method is expected to be also applicable to other materials featuring complex cracks.

Original languageEnglish
Article number106532
JournalCement and Concrete Research
Volume148
DOIs
StatePublished - Oct 2021

Keywords

  • Computer vision
  • Crack detection
  • Crack quantification
  • Deep learning
  • High-performance fiber reinforced cementitious composites (HPFRCC)
  • Microcrack

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