Computers and Concrete

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Concrete bridge deck deterioration model using belief networks

  • Njardardottir, Hrodny (Department of Civil Engineering, University of Toronto) ;
  • McCabe, Brenda (Department of Civil Engineering, University of Toronto) ;
  • Thomas, Michael D.A. (Civil Engineering, University of New Brunswick)
  • Published : 2005.12.25
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Abstract

When deterioration of concrete is observed in a structure, it is highly desirable to determine the cause of such deterioration. Only by understanding the cause can an appropriate repair strategy be implemented to address both the cause and the symptom. In colder climates, bridge deck deterioration is often caused by chlorides from de-icing salts, which penetrate the concrete and depassivate the embedded reinforcement, causing corrosion. Bridge decks can also suffer from other deterioration mechanisms, such as alkali-silica reaction, freeze-thaw, and shrinkage. There is a need for a comprehensive and integrative system to help with the inspection and evaluation of concrete bridge deck deterioration before decisions are made on the best way to repair it. The purpose of this research was to develop a model to help with the diagnosis of concrete bridge deck deterioration that integrates the symptoms observed during an inspection, various deterioration mechanisms, and the probability of their occurrence given the available data. The model displays the diagnosis result as the probability that one of four deterioration mechanisms, namely shrinkage, corrosion of reinforcement, freeze-thaw and alkali-silica reaction, is at fault. Sensitivity analysis was performed to determine which probabilities in the model require refinement. Two case studies are included in this investigation.

Keywords

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  2. Prediction of the remaining service life of existing concrete bridges in infrastructural networks based on carbonation and chloride ingress vol.21, pp.3, 2005, https://doi.org/10.12989/sss.2018.21.3.305
  3. Performance of US Concrete Highway Bridge Decks Characterized by Random Parameters Binary Logistic Regression vol.6, pp.1, 2005, https://doi.org/10.1061/ajrua6.0001031