Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Concrete Cracks Monitoring using Deep Learning-based Multiresolution Analysis

Version 1 : Received: 7 June 2021 / Approved: 7 June 2021 / Online: 7 June 2021 (15:53:18 CEST)

How to cite: Arbaoui, A.; Ouahabi, A.; Jacques, S.; Hamiane, M. Concrete Cracks Monitoring using Deep Learning-based Multiresolution Analysis. Preprints 2021, 2021060194 (doi: 10.20944/preprints202106.0194.v1). Arbaoui, A.; Ouahabi, A.; Jacques, S.; Hamiane, M. Concrete Cracks Monitoring using Deep Learning-based Multiresolution Analysis. Preprints 2021, 2021060194 (doi: 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.

Subject Areas

cracks; wavelets; multiresolution analysis; ultrasound imaging; deep learning; CNN

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