Automatic Corrosion Classification and Quantification of Steel Reinforcing Bars Within Concrete Using Image Data Generated by an Inductive Sensor

Journal Article · Mon Mar 06 00:00:00 EST 2006 · AIP Conference Proceedings

DOI:https://doi.org/10.1063/1.2184676· OSTI ID:20798225

Zaid, M; El-Madaani, F; Gaydecki, P; Miller, G ^[1]

---

1. School of Electrical and Electronic Engineering, the University of Manchester, PO Box 88, Manchester M60 1QD (United Kingdom)

This paper presents a methodology to automatically distinguish and quantify the corrosion of reinforcing bars within concrete using images generated by an inductive sensor. The methodology comprises three stages; image generation using the inductive sensor, image segmentation and feature extraction and neural network object classification. Preliminary results have shown that the methodology has correctly classified all the corroded parts on the testing samples while estimated the corrosion rate correctly on 80% of the testing samples.

• OSTI ID: 20798225
• Journal Information: AIP Conference Proceedings, Vol. 820, Issue 1; Conference: Conference on review of progress in quantitative nondestructive evaluation, Brunswick, ME (United States), 31 Jul - 5 Aug 2005; Other Information: DOI: 10.1063/1.2184676; (c) 2006 American Institute of Physics; Country of input: International Atomic Energy Agency (IAEA); ISSN 0094-243X
• Country of Publication: United States
• Language: English

Related Subjects

36 MATERIALS SCIENCE
37 INORGANIC
ORGANIC
PHYSICAL AND ANALYTICAL CHEMISTRY
CONCRETES
CORROSION
IMAGES
NEURAL NETWORKS
NONDESTRUCTIVE TESTING
STEELS