Crack Detection in Concrete Structures Using Deep Learning

Vaughn Peter Golding, Zahra Gharineiat, Hafiz Suliman Munawar and Fahim Ullah
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Vaughn Peter Golding: School of Surveying and Built Environment, University of Southern Queensland, Springfield Central, QLD 4300, Australia
Zahra Gharineiat: School of Surveying and Built Environment, University of Southern Queensland, Springfield Central, QLD 4300, Australia
Hafiz Suliman Munawar: School of Surveying and Built Environment, University of Southern Queensland, Springfield Central, QLD 4300, Australia
Fahim Ullah: School of Surveying and Built Environment, University of Southern Queensland, Springfield Central, QLD 4300, Australia

Sustainability, 2022, vol. 14, issue 13, 1-25

Abstract: Infrastructure, such as buildings, bridges, pavement, etc., needs to be examined periodically to maintain its reliability and structural health. Visual signs of cracks and depressions indicate stress and wear and tear over time, leading to failure/collapse if these cracks are located at critical locations, such as in load-bearing joints. Manual inspection is carried out by experienced inspectors who require long inspection times and rely on their empirical and subjective knowledge. This lengthy process results in delays that further compromise the infrastructure’s structural integrity. To address this limitation, this study proposes a deep learning (DL)-based autonomous crack detection method using the convolutional neural network (CNN) technique. To improve the CNN classification performance for enhanced pixel segmentation, 40,000 RGB images were processed before training a pretrained VGG16 architecture to create different CNN models. The chosen methods (grayscale, thresholding, and edge detection) have been used in image processing (IP) for crack detection, but not in DL. The study found that the grayscale models (F1 score for 10 epochs: 99.331%, 20 epochs: 99.549%) had a similar performance to the RGB models (F1 score for 10 epochs: 99.432%, 20 epochs: 99.533%), with the performance increasing at a greater rate with more training (grayscale: +2 TP, +11 TN images; RGB: +2 TP, +4 TN images). The thresholding and edge-detection models had reduced performance compared to the RGB models (20-epoch F1 score to RGB: thresholding −0.723%, edge detection −0.402%). This suggests that DL crack detection does not rely on colour. Hence, the model has implications for the automated crack detection of concrete infrastructures and the enhanced reliability of the gathered information.

Keywords: crack detection; convolutional neural network; image processing; deep learning; damage detection (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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