Identification of Concrete Cracks Using Deep Learning Models: A Systematic Review

Abstract

Deep learning (DL) has grown in popularity in civil inspection, notably for crack diagnosis, as a means of guaranteeing the long-term stability and security of concrete structures. It is critical to identify cracks to conduct inspections and assessments while preserving the existing concrete frameworks. This article reviews and analyses existing literature on identification of cracks on concrete structures using DL, to enhance the clarity and understanding of the ongoing research efforts in this domain. A systematic review found 97 linked research papers from 2018 to the beginning of 2023, using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Statement review process as a guide. The articles are categorised into several methods in identifying cracks, which include classification, segmentation, detection, and hybrid methods. Various issues in implementing DL in all the methods are discussed and several limitations, challenges and proposed solutions are presented. Finally, possible research directions are also discussed.

Keywords

• convolution neural network
• crack classification
• crack detection
• crack segmentation
• deep learning
• review
• structural health monitoring