Using a Negative Binomial Regression Model with a Bayesian Tuner to Estimate Failure Probability for Sewerage Infrastructure
Publication: Journal of Infrastructure Systems
Volume 20, Issue 1
Abstract
The replacement and maintenance of subsurface assets, such as water and wastewater pipes, is of great interest to water utilities because these infrastructure networks require large amounts of investment over time. Each asset requires investment relatively rarely, but the number of assets is so enormous that the flow of money is large. Therefore the accurate estimation of the deterioration and aging process of these assets is critical to the efficient and sustainable allocation of investment spend. The development of failure models is difficult for various reasons: short spans of data (very little longitudinal data), very sparse failure rates, inaccuracy of observational data, and accuracy and availability of potential predictor data. Technical difficulties also arise such as variability and noise, censoring effects, overdispersion, and, throughout the exercise, the large volume of data usually involved. In this paper, a new regression approach is formulated that maintains a rigorous statistical approach while still being practical and easy to apply. In addition, the formulation involved permits the individual pipe history to be used in an elegantly simple Bayesian update. The example provided refers to work done for Yorkshire Water Services in estimating probability of blockage failure for all their sewerage assets.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
We are indebted to Yorkshire Water Services for their permission to publish this work.
References
Arthur, S., Crow, H., and Pedezert, L. (2008). “Understanding blockage formation in combined sewer networks.” Proc. Inst. Civ. Eng. Water Manage., 161(WM4), 215–221.
Ascher, H. (1991). “Basic probabilistic and statistical concepts for maintenance of parts and systems.” IMA J. Manage. Math., 3(3), 153–167.
Baik, H., Jeong, H. S., and Abraham, D. M. (2006). “Estimating transition probabilities in Markov chain-based deterioration models for management of wastewater systems.” J. Water Resour. Plann. Manage., 15–24.
Baur, R., and Herz, R. (2002). “Selective inspection planning with ageing forecast for sewer types.” Water Sci. Technol., 46(6–7), 389–396.
Berardi, L., Giustolisi, O., Savic, D. A., and Kapelan, Z. (2009). “An effective multi-objective approach to prioritization of sewer pipe inspection.” Water Sci. Technol., 60(4), 841–850.
Fenner, R. A., McFarland, G., and Thorne, O. (2007). “Case-based reasoning approach for managing sewerage assets.” Proc. Inst. Civ. Eng. Water Manage., 160, 15–24.
Fenner, R. A., and Sweeting, L. (1999). “A decision support model for the rehabilitation of non-critical sewers.” Water Sci. Technol., 39(9), 193–200.
Jonkergouw, P. M. R., Watson, T. G., Erskine, A. D., and Petrie, M. (2009). “Infrastructure risk management: A probabilistic approach to asset management.” Computing and Control in the Water Industry: ‘Integrating Water Systems’ Conf.
Koo, D., and Ariaratnam, S. T. (2006). “Innovative method for assessment of underground sewer pipe condition.” Autom. Construct., 15, 479–488.
Korving, H., and van Noortwijk, J. M. (2006). “Bayesian updating of a prediction model for sewer degradation.” 2nd Int. IWA Conf. on Sewer Operation and Maintenance.
Mavin, K. (1996). “Predicting the failure performance of individual water mains.” Research Rep. 114, Urban Water Research Association of Australia.
McCullagh, P., and Nelder, J. (1989). Generalized linear models, 2nd Ed., Chapman and Hall/CRC, Boca Raton, FL.
Micevski, T., Kuczera, G., and Coombes, P. (2002). “Markov model for storm water pipe deterioration.” J. Infrastruct. Syst., 49–56.
Shamir, U., and Howard, C. D. D. (1979). “An analytic approach to scheduling pipe replacement.” J. Am. Water Works Assoc., 71(5), 248–258.
Stein, R., and Trujillo Alvarez, R. (2004). “Evaluation methods of survival capacity oriented investment strategies of drain and sewer systems by the use of damage-specific aging models.” ASCE Conf. Proc., ASCE, Reston, VA, 1–12.
Tran, D. H., Perera, B. J. C., and Ng, A. W. M. (2007). “Neural network based prediction models for structural deterioration of urban drainage pipes.” MODSIM 2007 Int. Congress on Modelling and Simulation, L. Oxley and D. Kulasiri, eds., Modelling and Simulation Society of Australia and New Zealand, 2264–2270.
UKWIR. (2007). “Distribution: Development of national deterioration models.” Rep. Reference 07/RG/05/20.
Watson, T. G., Christian, C. D., Mason, A. J., Smith, M. H., and Meyer, R. (2004). “Bayesian-based pipe failure model.” J. Hydroinf., 6(4), 259–264.
Information & Authors
Information
Published In
Journal of Infrastructure Systems
Volume 20 • Issue 1 • March 2014
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Oct 28, 2011
Accepted: Jun 24, 2013
Published online: Jun 26, 2013
Published in print: Mar 1, 2014
Discussion open until: May 13, 2014
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.