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Concrete Cracks Detection and Monitoring Using Deep Learning-Based Multiresolution Analysis

Abstract : In this paper, we propose a new methodology for crack detection and monitoring in concrete structures. This approach is based on a multiresolution analysis of a sample or a specimen of concrete material subjected to several types of solicitation. The image obtained by ultrasonic investigation and processed by a customized wavelet is analyzed at various scales in order to detect internal cracks and crack initiation. The ultimate objective 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. This is achieved through the combination of two major data analysis tools which are wavelets and deep learning. This original procedure is shown to yield a high accuracy close to 90%. In order to evaluate the performance of the proposed CNN architectures, we also used an open access database, SDNET2018, for the automatic detection of external cracks.
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https://hal-univ-tours.archives-ouvertes.fr/hal-03300399
Contributor : Sebastien Jacques <>
Submitted on : Tuesday, July 27, 2021 - 10:13:46 AM
Last modification on : Friday, July 30, 2021 - 10:22:16 AM

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Ahcene Arbaoui, Abdeldjalil Ouahabi, Sebastien Jacques, Madina Hamiane. Concrete Cracks Detection and Monitoring Using Deep Learning-Based Multiresolution Analysis. Electronics, Penton Publishing Inc., 2021, 10, ⟨10.3390/electronics10151772⟩. ⟨hal-03300399⟩

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