We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Concrete Cracks Detection and Monitoring Using Deep Learning-Based Multiresolution Analysis.
- Authors
Arbaoui, Ahcene; Ouahabi, Abdeldjalil; Jacques, Sébastien; Hamiane, Madina
- 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.
- Subjects
CRACKING of concrete; CONVOLUTIONAL neural networks; CRACK propagation (Fracture mechanics)
- Publication
Electronics (2079-9292), 2021, Vol 10, Issue 15, p1772
- ISSN
2079-9292
- Publication type
Article
- DOI
10.3390/electronics10151772