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Calibrating Markov chain-based deterioration models for predicting future conditions of railway bridge elements

Journal Article


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Abstract


  • Existing nonlinear optimization-based algorithms for estimating Markov transition probability matrix (TPM) in bridge deterioration

    modeling sometimes fail to find optimum TPM values, and hence lead to invalid future condition prediction. In this study, a Metropolis-Hasting

    algorithm (MHA)-based Markov chain Monte Carlo (MCMC) simulation technique is proposed to overcome this limitation and calibrate the

    state-based Markov deterioration models (SBMDM) of railway bridge components. Factors contributing to rail bridge deterioration were identified;

    inspection data for 1,000 Australian railway bridges over 15 years were reviewed and filtered. The TPMs corresponding to a typical

    bridge element were estimated using the proposed MCMC simulation method and two other existing methods, namely, regression-based nonlinear

    optimization (RNO) and Bayesian maximum likelihood (BML). Network-level condition state prediction results obtained from these

    three approaches were validated using statistical hypothesis tests with a test data set, and performance was compared. Results show that the

    MCMC-based deterioration model performs better than the other two methods in terms of network-level condition prediction accuracy and

    capture of model uncertainties.

Publication Date


  • 2015

Citation


  • Walgama Wellalage, N. K., Zhang, T. & Dwight, R. (2015). Calibrating Markov chain-based deterioration models for predicting future conditions of railway bridge elements. Journal of Bridge Engineering, 20 04014060-1-04014060-13.

Scopus Eid


  • 2-s2.0-84921324478

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=5169&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/4148

Start Page


  • 04014060-1

End Page


  • 04014060-13

Volume


  • 20

Abstract


  • Existing nonlinear optimization-based algorithms for estimating Markov transition probability matrix (TPM) in bridge deterioration

    modeling sometimes fail to find optimum TPM values, and hence lead to invalid future condition prediction. In this study, a Metropolis-Hasting

    algorithm (MHA)-based Markov chain Monte Carlo (MCMC) simulation technique is proposed to overcome this limitation and calibrate the

    state-based Markov deterioration models (SBMDM) of railway bridge components. Factors contributing to rail bridge deterioration were identified;

    inspection data for 1,000 Australian railway bridges over 15 years were reviewed and filtered. The TPMs corresponding to a typical

    bridge element were estimated using the proposed MCMC simulation method and two other existing methods, namely, regression-based nonlinear

    optimization (RNO) and Bayesian maximum likelihood (BML). Network-level condition state prediction results obtained from these

    three approaches were validated using statistical hypothesis tests with a test data set, and performance was compared. Results show that the

    MCMC-based deterioration model performs better than the other two methods in terms of network-level condition prediction accuracy and

    capture of model uncertainties.

Publication Date


  • 2015

Citation


  • Walgama Wellalage, N. K., Zhang, T. & Dwight, R. (2015). Calibrating Markov chain-based deterioration models for predicting future conditions of railway bridge elements. Journal of Bridge Engineering, 20 04014060-1-04014060-13.

Scopus Eid


  • 2-s2.0-84921324478

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=5169&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/4148

Start Page


  • 04014060-1

End Page


  • 04014060-13

Volume


  • 20