Detection of flaws in concrete using ultrasonic tomography and convolutional neural networks
Authors:
- Marek Słoński,
- Krzysztof Schabowicz,
- Ewa Krawczyk
Abstract
Non-destructive testing of concrete for defects detection, using acoustic techniques, is currently performed mainly by human inspection of recorded images. The images consist of the inside of the examined elements obtained from testing devices such as the ultrasonic tomograph. However, such an automatic inspection is time-consuming, expensive, and prone to errors. To address some of these problems, this paper aims to evaluate a convolutional neural network (CNN) toward an automated detection of flaws in concrete elements using ultrasonic tomography. There are two main stages in the proposed methodology. In the first stage, an image of the inside of the examined structure is obtained and recorded by performing ultrasonic tomography-based testing. In the second stage, a convolutional neural network model is used for automatic detection of defects and flaws in the recorded image. In this work, a large and pre-trained CNN is used. It was fine-tuned on a small set of images collected during laboratory tests. Lastly, the prepared model was applied for detecting flaws. The obtained model has proven to be able to accurately detect defects in examined concrete elements. The presented approach for automatic detection of flaws is being developed with the potential to not only detect defects of one type but also to classify various types of defects in concrete elements.
- Record ID
- CUT7e175dbb6bc644e3b65f6e585e542325
- Publication categories
- ;
- Author
- Journal series
- Materials, ISSN , e-ISSN 1996-1944, Biweekly
- Issue year
- 2020
- Vol
- 13
- No
- 7
- Pages
- [1-16]
- Article number
- 1557
- Other elements of collation
- il. (w tym kolor.); Bibliografia (na s.) - 14-16; Bibliografia (liczba pozycji) - 42; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 13, Iss. 7, Spec. Iss.
- Substantive notes
- Special Issue: Testing of Materials and Elements in Civil Engineering
- Keywords in English
- concrete, non-destructive testing, ultrasounds, ultrasonic tomography, acoustic methods, defects, diagnostic, detection, convolutional neural networks, transfer learning
- DOI
- DOI:10.3390/ma13071557 Opening in a new tab
- URL
- https://www.mdpi.com/1996-1944/13/7/1557/htm Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 140
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT7e175dbb6bc644e3b65f6e585e542325/
- URN
urn:pkr-prod:CUT7e175dbb6bc644e3b65f6e585e542325
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.