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Mayakuntla, PK, Ganguli, A and Smyl, D (2023) Gaussian Mixture Model-Based Classification of Corrosion Severity in Concrete Structures Using Ultrasonic Imaging. Journal of Nondestructive Evaluation, 42 (2). ISSN 0195-9298
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Gaussian Mixture Model-Based Classification of Corrosion Severity in Concrete Structures Using Ultrasonic Imaging.pdf - Published Version Available under License Creative Commons Attribution. Download (7MB) | Preview |
Abstract
Corrosion in reinforced concrete (RC) structures is associated with a reduction of the rebar diameter, loss of interfacial bond, cracking, and eventual spalling and probable collapse of the structure. The negative effects of corrosion on structural safety, durability, and longevity imposes significant costs on the national economy. Therefore, planned non-destructive testing (NDT) of concrete structures is essential to enhance the safety and economic sustainability of infrastructure. Previous work by the research group has established the capability of the ultrasonic Synthetic Aperture Focusing Technique (SAFT) as a tool for detection of rebar corrosion. This work extends the previous research towards application of statistical learning for ascertaining the corrosion severity through analysis of SAFT images of the rebar. Using features extracted from images, a Gaussian mixture model (GMM) is implemented to classify the severity of corrosion damage to the rebar. The results from the research positively demonstrate the potential of the proposed technique as an enabler for decisions pertaining to maintenance and timely repair of concrete infrastructural assets.
| Item Type: | Article |
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| Uncontrolled Keywords: | 0912 Materials Engineering; Acoustics |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Engineering |
| Publisher: | Springer |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 22 May 2023 12:47 |
| Last Modified: | 22 May 2023 13:00 |
| DOI or ID number: | 10.1007/s10921-023-00939-9 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/19552 |
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