Automatic corrosion classification and quantification of steel reinforcing bars within concrete using image data generated by an inductive sensor

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Bibliographical metadata

Original languageEnglish
Title of host publicationReview of Progress in Quantitative Nondestructive Evaluation: Volume 25B
Pages1313-1320
Number of pages8
Volume820 II
DOIs
Publication statusPublished - 6 Mar 2006
EventReview of Progress in Quantitative Nondestructive - Brunswick, ME, United States
Event duration: 31 Jul 20055 Aug 2005

Conference

ConferenceReview of Progress in Quantitative Nondestructive
CountryUnited States
CityBrunswick, ME
Period31/07/055/08/05