Development of a Bridge Deterioration Model in a Data-Constrained Environment
Publication: Journal of Performance of Constructed Facilities
Volume 31, Issue 5
Abstract
A stochastic time-based deterioration model for use with New Zealand bridges is presented, comprising two parts and being based on the condition management process that is used to assess the extent and severity of a defect, or defects. The first part is an expert-based severity deterioration model, which can be used to simulate the deterioration of timber, concrete, and pretensioned and steel load-bearing elements. The second part is the data-derived extent model, which uses a novel approach, not previously used, to simulate the growth of defects with time. By creating these extent and severity models, the general absence of deterioration models in the Australian and New Zealand region is addressed. Furthermore, the development of both the extent and severity models was achieved in a data-constrained environment, which led to validation and development challenges. How these challenges were dealt with, and the novel methods that were used to solve them are also covered.
Get full access to this article
View all available purchase options and get full access to this article.
References
Aboura, K., Samali, B., Crews, K., and Li, J. (2008). “Stochastic processes for modeling bridge deterioration.” Proc., 20th Australasian Conf. on the Mechanics of Structures and Materials, Taylor & Francis Group, Abingdon, U.K., 533–538.
Abramowitz, M., and Stegun, I. A. (1972). Handbook of mathematical functions with formulas, graphs, and mathematical tables, Courier Corporation, Dover, NY.
Agrawal, A. K., Kawaguchi, A., and Chen, Z. (2010). “Deterioration rates of typical bridge elements in New York.” J. Bridge Eng., 419–429.
BehaviorSearch [Computer software]. Northwestern Univ., Evanston, IL.
Bevc, L., Mahut, B., and Grefstad, K. (1999). “Deliverable D2 review of current practice for assessment of structural condition and classification of defects, bridge management in Europe (BRIME).”, Transportation Research Laboratory, Woking, U.K.
Black, M., Brint, A. T., and Brailsford, J. R. (2005). “A semi-Markov approach for modelling asset deterioration.” J. Oper. Res. Soc., 56(11), 1241–1249.
Bu, G. P., Lee, J. H., Guan, H., and Loo, Y. C. (2012). “An integrated method for probabilistic bridge deterioration modeling.” The Australasian Structural Engineering Conf.: Past, Present and Future of Structural Engineering, Engineers Australia, Barton, Australia.
Bush, S. J. W., Omenzetter, P., Henning, T., and McCarten, P. (2012). “Data collection and monitoring strategies for asset management of New Zealand road bridges.”, NZ Transport Agency, Wellington, New Zealand, 1–160.
Clemen, R. T., and Winkler, R. L. (2007). “Aggregating probability distributions.” Advances in decision analysis: From foundations to applications, E. Ward, F. M. Ralph, and D. von Winterfeldt, eds., Cambridge University Press, Cambridge, U.K., 154–176.
Davis, R. (2008). “Teaching note—Teaching project simulation in excel using PERT-beta distributions.” INFORMS Trans. Educ., 8(3), 139–148.
Epstein, J. M. (2008). “Why model?” J. Artif. Soc. Soc. Simul., 11(4), 12.
Golabi, K., and Shepard, R. (1997). “Pontis: A system for maintenance optimization and improvement of U.S. bridge networks.” Interfaces, 27(1), 71–88.
Goossens, L. H. J., and Cooke, R. M. (2005). “Expert judgement-calibration and combination.” The Workshop on Expert Judgment, Aix En Provence, CEA, Cadarache, France.
Heath, B., Hill, R., and Ciarallo, F. (2009). “A survey of agent-based modeling practices (January 1998 to July 2008).” J. Artif. Soc. Soc. Simul., 12(4), 9.
Kotze, R., Ngo, H., and Seskis, J. (2015). Improved bridge deterioration models, predictive tools and costs, Austroads, Sydney, NSW, Australia, 1–124.
Kuhn, K. D., and Madanat, S. M. (2005). “Model uncertainty and the management of a system of infrastructure facilities.” Transp. Res. Part C: Emerging Technol., 13(5–6), 391–404.
Lake, N., and Seskis, J. (2013). Bridge management using performance models, Austroads, Sydney, NSW, Australia, 1–163.
Landry, M., Malouin, J.-L., and Oral, M. (1983). “Model validation in operations research.” Eur. J. Oper. Res., 14(3), 207–220.
Lee, J., Guan, H., Loo, Y.-C., Blumenstein, M., and Xin-ping, W. (2011). “Modelling long-term bridge deterioration at structural member level using artificial intelligence techniques.” Applied mechanics and materials, Trans Tech Publications, Zurich, Switzerland, 444–453.
NetLogo [Computer software]. Northwestern Univ., Evanston, IL.
Noortwijk, J. M., and Kallen, M.-J. (2014). Stochastic deterioration, Wiley, New York, 1–8.
NZTA (New Zealand Transport Agency). (2011a). Bridges and other significant highways structures inspection policy, Wellington, New Zealand, 1–20.
NZTA (New Zealand Transport Agency). (2011b). State highway asset management plan 2012–2015, Wellington, New Zealand, 1–106.
NZTA (New Zealand Transport Agency). (2014). Bridge manual: SP/M/022, 3rd Ed., Wellington, New Zealand.
NZTA (New Zealand Transport Agency). (2015). Bridges and other significant highways structures inspection policy, Wellington, New Zealand, 1–27.
Reynolds, J., and Rooke, A. (2009). “A basic bridge information system.” The 7th Austroads Bridge Conf., Bridges of the Future, Australian Roads Research Board, Melbourne, Australia.
Rogers, R. A., Al-Ani, M., and Ingham, J. M. (2013). Assessing pre-tensioned reinforcement corrosion within the New Zealand concrete bridge stock, New Zealand Transport Agency, Wellington, New Zealand, 1–488.
Skulmoski, G., Hartman, F., and Krahn, J. (2007). “The Delphi method for graduate research.” J. Inf. Technol. Educ. Res., 6(1), 1–21.
Thomas, O., and Sobanjo, J. (2013). “Comparison of Markov chain and semi-Markov models for crack deterioration on flexible pavements.” J. Infrastruct. Syst., 186–195.
UKHA (United Kingdom Highways Agency). (2007). Inspection manual for highway structures. Volume 1: Reference manual, The Stationery Office, London.
Wang, R., Ma, L., Yan, C., and Mathew, J. (2012). “Condition deterioration prediction of bridge elements using dynamic Bayesian networks (DBNs).” Int. Conf. on Quality, Reliability, Risk, Maintenance, and Safety Engineering, IEEE, Piscataway, NJ, 566–571.
Welton, N. J., and Ades, A. E. (2005). “Estimation of Markov chain transition probabilities and rates from fully and partially observed data: Uncertainty propagation, evidence synthesis, and model calibration.” Med. Decis. Making, 25(6), 633–645.
Information & Authors
Information
Published In
Journal of Performance of Constructed Facilities
Volume 31 • Issue 5 • October 2017
Copyright
©2017 American Society of Civil Engineers.
History
Received: Oct 27, 2016
Accepted: Mar 21, 2017
Published online: Jun 14, 2017
Published in print: Oct 1, 2017
Discussion open until: Nov 14, 2017
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.