- Title
- Algorithm development for the non-destructive testing of structural damage
- Creator
- Noori Hoshyar, Azadeh; Rashidi, Maria; Liyanapathirana, Ranjith; Samali, Bijan
- Date
- 2019
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/187729
- Identifier
- vital:17120
- Identifier
-
https://doi.org/10.3390/app9142810
- Identifier
- ISSN:2076-3417
- Abstract
- Monitoring of structures to identify types of damages that occur under loading is essential in practical applications of civil infrastructure. In this paper, we detect and visualize damage based on several non-destructive testing (NDT) methods. A machine learning (ML) approach based on the Support Vector Machine (SVM) method is developed to prevent misdirection of the event interpretation of what is happening in the material. The objective is to identify cracks in the early stages, to reduce the risk of failure in structures. Theoretical and experimental analyses are derived by computing the performance indicators on the smart aggregate (SA)-based sensor data for concrete and reinforced-concrete (RC) beams. Validity assessment of the proposed indices was addressed through a comparative analysis with traditional SVM. The developed ML algorithms are shown to recognize cracks with a higher accuracy than the traditional SVM. Additionally, we propose different algorithms for microwave- or millimeter-wave imaging of steel plates, composite materials, and metal plates, to identify and visualize cracks. The proposed algorithm for steel plates is based on the gradient magnitude in four directions of an image, and is followed by the edge detection technique. Three algorithms were proposed for each of composite materials and metal plates, and are based on 2D fast Fourier transform (FFT) and hybrid fuzzy c-mean techniques, respectively. The proposed algorithms were able to recognize and visualize the cracking incurred in the structure more efficiently than the traditional techniques. The reported results are expected to be beneficial for NDT-based applications, particularly in civil engineering.
- Publisher
- MDPI AG
- Relation
- Applied sciences Vol. 9, no. 14 (2019), p. 2810
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- http://creativecommons.org/licenses/by/4.0/
- Rights
- © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license
- Rights
- Open Access
- Subject
- Algorithms; Artificial intelligence; Civil engineering; Comparative analysis; Composite materials; Concrete; Cracking (fracturing); Damage detection; Data analysis; Destructive testing; Discriminant analysis; Edge detection; Failure analysis; Fast fourier transformations; Fourier analysis; Fourier transforms; Image processing; Learning algorithms; Machine learning; Metal plates; Microwave or millimeter wave imaging; Millimeter waves; Non-destructive testing; Reinforced concrete; Reinforcing steels; Risk reduction; Steel plates; Structural damage; Support vector machines; MD Multidisciplinary
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