Arbaoui, A.; Ouahabi, A.; Jacques, S.; Hamiane, M. Concrete Cracks Monitoring using Deep Learning-based Multiresolution Analysis. Preprints2021, 2021060194. https://doi.org/10.20944/preprints202106.0194.v1
APA Style
Arbaoui, A., Ouahabi, A., Jacques, S., & Hamiane, M. (2021). Concrete Cracks Monitoring using Deep Learning-based Multiresolution Analysis. Preprints. https://doi.org/10.20944/preprints202106.0194.v1
Chicago/Turabian Style
Arbaoui, A., Sébastien Jacques and Madina Hamiane. 2021 "Concrete Cracks Monitoring using Deep Learning-based Multiresolution Analysis" Preprints. https://doi.org/10.20944/preprints202106.0194.v1
Abstract
In this paper, we propose a new methodology for crack monitoring in concrete structures. This approach is based on a n this paper, we propose a new methodology for monitoring cracks in concrete structures. This approach is based on a multi-resolution analysis of a sample or a specimen of the studied material subjected to several types of solicitation. The image obtained by ultrasonic investigation and processing by a dedicated wavelet will be analyzed according to several scales in order to detect internal cracks and crack initiation. The ultimate goal of this work is to propose an automatic crack type identification scheme based on convolutional neural networks (CNN). In this context, crack propagation can be monitored without access to the concrete surface and the goal is to detect cracks before they are visible on the concrete surface. The key idea allowing such a performance is the combination of two major data analysis tools which are wavelets and Deep Learning. This original procedure allows to reach a high accuracy close to 0.90. In this work, we have also implemented another approach for automatic detection of external cracks by deep learning from publicly available datasets.
Keywords
cracks; wavelets; multiresolution analysis; ultrasound imaging; deep learning; CNN
Subject
Engineering, Automotive Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
