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Deterioration Prediction of Timber Bridge Elements Using the Markov Chain

Ranith, Shriganghi, Setunge, Sujeeva, Gravina, Rebecca and Venkatesan, Srikanth (2011) Deterioration Prediction of Timber Bridge Elements Using the Markov Chain. Journal of Performance of Constructed Facilities. ISSN 0887-3828 (print) 1943-5509 (online)

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Timber bridges require high accumulated maintenance costs which can be many times greater than their initial cost. Infrastructure managers need deterioration models to assist with appropriate decisions on repair strategies and program maintenance schedules by accurately predicting the future condition of timber bridge elements. Markov chain based models have been used extensively in modelling the deterioration of infrastructure facilities. These models can predict the condition of bridge elements as a probabilistic estimate. This paper presents the prediction of future condition of timber bridge elements using a stochastic Markov chain model. Condition data obtained from VicRoads (Roads Corporation of Victoria, Australia) was used to develop transition probabilities. The percentage prediction method, regressionbased optimisation method and non-linear optimisation technique were applied to predict transition matrices and transient probabilities from condition data. The most suitable deterioration model for timber bridge elements was selected by evaluating the model performances using the Goodness-of-fit and reliability tests. It was concluded that the Markov chain developed for deterioration prediction of timber bridges using the non-linear optimisation technique was mathematically acceptable and predicts the deterioration progression with a reasonable accuracy.

Item Type: Article
Uncontrolled Keywords: ResPubID24573, Timber Bridge, deterioration prediction, Markov models, Goodness-of-fit test, reliability, condition data
Subjects: Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
Current > FOR Classification > 0905 Civil Engineering
Depositing User: VUIR
Date Deposited: 02 Oct 2012 05:25
Last Modified: 04 Jun 2020 23:19
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Citations in Scopus: 37 - View on Scopus

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