Development of an Integrated Method for Probabilistic Bridge-Deterioration Modeling
Publication: Journal of Performance of Constructed Facilities
Volume 28, Issue 2
Abstract
Probabilistic deterioration models such as state-based and time-based models are only capable of predicting future bridge-condition ratings when a sufficient amount of condition data and reasonable data distribution are available. However, such are usually difficult to acquire from limited bridge-inspection records. As a result, these probabilistic models cannot guarantee reliable long-term prediction for each of the bridge elements concerned. To minimize this shortcoming, this paper proposes an advanced integrated method to construct workable transition probabilities for predicting long-term bridge performance. A selection process within this method automatically chooses a suitable prediction procedure for a given situation in terms of available inspection data. The backward prediction model (BPM) is also incorporated to effectively predict the bridge performance when sufficient inspection data are unavailable. Four different situations in regard to the available inspection data are predefined in this study to demonstrate the capabilities of the proposed integrated method. The outcomes show that the method can help develop an effective prediction model for various situations in terms of the quantity and distribution of available condition-rating data.
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Acknowledgments
The authors are grateful for the financial support provided by the Australian Research Council through an ARC Linkage Project Grant (LP0883807). The authors also wish to express their sincere thanks to the Industry Partners, Queensland Department of Transport and Main Roads and the Gold Coast City Council, for their generous financial and in-kind support.
References
Agrawal, A. K., Kawaguchi, A., and Chen, Z. (2010). “Deterioration rates of typical bridge elements in New York.” J. Bridge Eng., 419–429.
Butt, A. A., Shahin, M. Y., Feighan, K. J., and Carpenter, S. H. (1987). “Pavement performance prediction model using the Markov process.” Transportation Research Record 1123, Transportation Research Board, Washington, DC, 12–19.
DeStefano, P. D., and Grivas, D. A. (1998). “Method for estimating transition probability in bridge deterioration models.” J. Infrastruct. Syst., 56–62.
Devaraj, D. (2009). “Application of non-homogeneous Markov chains in bridge management systems.” Ph.D. thesis, Wayne State Univ., Detroit.
Jiang, Y. (1990). “The development of performance prediction and optimization models for bridge management systems.” Ph.D. thesis, Purdue Univ., West Lafayette, IN.
Jiang, Y., Saito, M., and Sinha, K. C. (1988). “Bridge performance prediction model using the Markov chain.” Transportation Research Record 1180, Transportation Research Board, Washington, DC, 25–32.
Jiang, Y., and Sinha, K. C. (1989). “Bridge service life prediction model using the Markov chain.” Transportation Research Record 1223, Transportation Research Board, Washington, DC, 24–30.
Lee, J. H., Sanmugarasa, K., Loo, Y. C., and Blumenstein, M. (2008). “Improving the reliability of a bridge management system (BMS) using an ANN-based backward prediction model (BPM).” Autom. Constr., 17(6), 758–772.
Madanat, S., and Ibrahim, W. H. W. (1995). “Poisson regression models of infrastructure transition probabilities.” J. Transp. Eng., 267–272.
Madanat, S., Karlaftis, M. G., and McCarthy, P. S. (1997). “Probabilistic infrastructure deterioration models with panel data.” J. Infrastruct. Syst., 4–9.
Madanat, S., Mishalani, R., and Ibrahim, W. H. W. (1995). “Estimation of infrastructure transition probabilities from condition rating data.” J. Infrastruct. Syst., 120–125.
Mauch, M., and Madanat, S. M. (2001). “Semiparametric hazard rate models of reinforced concrete bridge deck deterioration.” J. Infrastruct. Syst., 49–57.
Morcous, G. (2006). “Performance prediction of bridge deck systems using Markov chains.” J. Perform. Constr. Facil., 146–155.
Morcous, G., and Akhnoukh, A. (2006). “Stochastic modelling of infrastructure deterioration: An application to concrete bridge decks.” Proc., Int. Conf. on Computing and Decision Making in Civil and Building Engineering, International Society for Computing in Civil and Building Engineering, Taipei, Taiwan.
Morcous, G., Lounis, Z., and Cho, Y. (2010). “An integrated system for bridge management using probabilistic and mechanistic deterioration models: application to bridge decks.” KSCE J. Civ. Eng., 14(4), 527–537.
Prozzi, J., and Madanat, S. M. (2000). “Analysis of experimental pavement failure data using stochastic duration models.” Transportation Research Record 1699, Transportation Research Board, Washington, DC, 87–94.
Queensland Dept. of Transport and Main Roads (QTMR). (2004). Bridge inspection manual.” Registration No. 80.640, Transport Technology Division, Brisbane, QLD, Australia.
Ravirala, V., and Grivas, D. A. (1995). “State increment method of life-cycle cost analysis for highway management.” J. Infrastruct. Syst., 151–159.
Thompson, P. D., and Shepard, R. W. (2000). “AASHTO commonly-recognized bridge elements-successful applications and lessons learned.” Proc., National Workshop on National Commonly Recognized Measures for Maintenance, Booz-Allen and Hamilton, McLean, VA.
Information & Authors
Information
Published In
Journal of Performance of Constructed Facilities
Volume 28 • Issue 2 • April 2014
Pages: 330 - 340
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Jan 17, 2012
Accepted: Nov 13, 2012
Published online: Nov 15, 2012
Published in print: Apr 1, 2014
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