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

Abstract

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.

Authors:
; ; ;  [1]
  1. School of Electrical and Electronic Engineering, the University of Manchester, PO Box 88, Manchester M60 1QD (United Kingdom)
Publication Date:
OSTI Identifier:
20798225
Resource Type:
Journal Article
Resource Relation:
Journal Name: AIP Conference Proceedings; Journal Volume: 820; Journal 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)
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; 37 INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL CHEMISTRY; CONCRETES; CORROSION; IMAGES; NEURAL NETWORKS; NONDESTRUCTIVE TESTING; STEELS

Citation Formats

Zaid, M., El-Madaani, F., Gaydecki, P., and Miller, G. Automatic Corrosion Classification and Quantification of Steel Reinforcing Bars Within Concrete Using Image Data Generated by an Inductive Sensor. United States: N. p., 2006. Web. doi:10.1063/1.2184676.
Zaid, M., El-Madaani, F., Gaydecki, P., & Miller, G. Automatic Corrosion Classification and Quantification of Steel Reinforcing Bars Within Concrete Using Image Data Generated by an Inductive Sensor. United States. doi:10.1063/1.2184676.
Zaid, M., El-Madaani, F., Gaydecki, P., and Miller, G. Mon . "Automatic Corrosion Classification and Quantification of Steel Reinforcing Bars Within Concrete Using Image Data Generated by an Inductive Sensor". United States. doi:10.1063/1.2184676.
@article{osti_20798225,
title = {Automatic Corrosion Classification and Quantification of Steel Reinforcing Bars Within Concrete Using Image Data Generated by an Inductive Sensor},
author = {Zaid, M. and El-Madaani, F. and Gaydecki, P. and Miller, G.},
abstractNote = {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.},
doi = {10.1063/1.2184676},
journal = {AIP Conference Proceedings},
number = 1,
volume = 820,
place = {United States},
year = {Mon Mar 06 00:00:00 EST 2006},
month = {Mon Mar 06 00:00:00 EST 2006}
}