Crack and Noncrack Classification from Concrete Surface Images Using Machine Learning. (May 2019)

• Record Type: Journal Article
• Authors: Kim, Hyunjun; Ahn, Eunjong; Shin, Myoungsu; Sim, Sung-Han
• Is Part Of: Structural health monitoring. Volume 18:Number 3(2019)
• Page Start: 725
• Page End: 738
• Publication Date: 2019-05
• Subjects: Concrete crack identification -- convolutional neural network -- digital image processing -- machine learning -- speeded-up robust features; Structural health monitoring -- Periodicals; Structural stability -- Periodicals; Strength of materials -- Periodicals; Nondestructive testing -- Periodicals; Constructions -- Stabilité -- Périodiques; Résistance des matériaux -- Périodiques; Contrôle non destructif -- Périodiques; Electronic journals; 624.17
• DOI: 10.1177/1475921718768747
• Languages: English
• ISSNs: 1475-9217

Abstract

In concrete structures, surface cracks are important indicators of structural durability and serviceability. Generally, concrete cracks are visually monitored by inspectors who record crack information such as the existence, location, and width. Manual visual inspection is often considered ineffective in terms of cost, safety, assessment accuracy, and reliability. Digital image processing has been introduced to more accurately obtain crack information from images. A critical challenge is to automatically identify cracks from an image containing actual cracks and crack-like noise patterns (e.g. dark shadows, stains, lumps, and holes), which are often seen in concrete structures. This article presents a methodology for identifying concrete cracks using machine learning. The method helps in determining the existence and location of cracks from surface images. The proposed approach is particularly designed for classifying cracks and noncrack noise patterns that are otherwise difficult to distinguish using existing image processing algorithms. In the training stage of the proposed approach, image binarization is used to extract crack candidate regions; subsequently, classification models are constructed based on speeded-up robust features and convolutional neural network. The obtained crack identification methods are quantitatively and qualitatively compared using new concrete surface images containing cracks and noncracks.