Prediction of onset of corrosion in concrete bridge decks using neural networks and case-based reasoning

Download
  1. (PDF, 467 KB)
  2. Get@NRC: Prediction of onset of corrosion in concrete bridge decks using neural networks and case-based reasoning (Opens in a new window)
DOIResolve DOI: http://doi.org/10.1111/j.1467-8667.2005.00380.x
AuthorSearch for: ; Search for:
TypeArticle
Journal titleComputer-Aided Civil and Infrastructure Engineering
Volume20
IssueJanuary 1
Pages108117; # of pages: 10
SubjectCorrosion (of reinforced concrete); Corrosion/cracking
AbstractThis paper proposes a methodology for predicting the time to onset of corrosion of reinforcing steel in concrete bridge decks while incorporating parameter uncertainty. It is based on the integration of artificial neural network (ANN), case-based reasoning (CBR), mechanistic model, and Monte Carlo simulation (MCS). A probabilistic mechanistic model is used to generate the distribution of the time to corrosion initiation based on statistical models of the governing parameters obtained from field data. The proposed ANN and CBR models act as universal functional mapping tools to approximate the relationship between the input and output of the mechanistic model. These tools are integrated with the MCS technique to generate the distribution of the corrosion initiation time using the distributions of the governing parameters. The proposed methodology is applied to predict the time to corrosion initiation of the top reinforcing steel in the concrete deck of the Dickson Bridge in Montreal. This study demonstrates the feasibility, adequate reliability and computational efficiency of the proposed integrated ANN-MCS and CBR-MCS approaches for preliminary project?level and also network-level analyses.
Publication date
LanguageEnglish
AffiliationNRC Institute for Research in Construction; National Research Council Canada
Peer reviewedYes
NRC number46313
15565
NPARC number20386400
Export citationExport as RIS
Report a correctionReport a correction
Record identifiere271c2ec-27ca-40ef-a87a-9859520f1b33
Record created2012-07-25
Record modified2016-05-09
Bookmark and share
  • Share this page with Facebook (Opens in a new window)
  • Share this page with Twitter (Opens in a new window)
  • Share this page with Google+ (Opens in a new window)
  • Share this page with Delicious (Opens in a new window)