Prediction of Onset of Corrosion in Concrete Bridge Decks Using Neural Networks and Case-Based Reasoning

• Published in: Computer-aided civil and infrastructure engineering Vol. 20; no. 2; pp. 108 - 117
• Main Authors: Morcous, G., Lounis, Z.
• Format: Journal Article
• Language: English
• Published: Boston, USA and Oxford, UK Blackwell Publishing, Inc 01.03.2005; Blackwell
• Subjects: Applied sciences; Bridges; Buildings. Public works; Computation methods. Tables. Charts; Concrete bridges; Corrosion; Durability. Pathology. Repairing. Maintenance; Exact sciences and technology; Structural analysis. Stresses; Canada; North America; America; Case study; Performance evaluation; Corrosion; Chemical corrosion; Chlorides; Mechanism analysis; Concrete construction; Case based reasoning; Neural network; Forecast model; Reinforcing bar; Bridge deck
• DOI: 10.1111/j.1467-8667.2005.00380.x
• ISSN: 1093-9687

:  This article 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.