Consequence-of-Failure Model for Risk-Based Asset Management of Wastewater Pipes Using AHP
Publication: Journal of Pipeline Systems Engineering and Practice
Volume 10, Issue 2
Abstract
Risk-based asset management focuses on prioritizing the most critical assets by evaluating their risk of failure. A typical way to determine the risk of failure is by assessing the asset’s likelihood of failure and its consequence of failure. The goal of this paper is to present a consequence-of-failure model using a weighted-sum multicriteria decision-making method to evaluate the economic, social, and environmental consequences of wastewater pipe failure. The model incorporates 14 factors consisting of pipe characteristics and demographic parameters arranged into a hierarchical model to determine one final consequence-of-failure score. The analytic hierarchy process (AHP) was used to determine the relative importance weights of all factors and criteria. The model was applied to a northeastern Louisiana sewer network. The results show that economic cost factors are the most important to evaluate when determining an asset’s consequence of failure. Integrating the derived model with wastewater pipe deterioration models can help utility managers prioritize critical investment needs in a more efficient manner.
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References
Anbari, M. J., M. Tabesh, and A. Roozbahani. 2017. “Risk assessment model to prioritize sewer pipes inspection in wastewater collection networks.” J. Environ. Manage. 190: 91–101. https://doi.org/10.1016/j.jenvman.2016.12.052.
Anderson, S., and C. Hyer. 2014. Innovative methods to assess sewer pipe risk and improve replacement planning decisions. Bath, MI: Michigan Water Environment Association.
ASCE. 2017. “2017 infrastructure report card.” Accessed December 6, 2017. https://www.infrastructurereportcard.org.
Baik, H., H. Jeong, and D. Abraham. 2006. “Estimating transition probabilities in Markov chain-based deterioration models for management of wastewater systems.” J. Water Resour. Plann. Manage. 132 (1): 15–24. https://doi.org/10.1061/(ASCE)0733-9496(2006)132:1(15).
Baur, R., and R. Herz. 2002. “Selective inspection planning with ageing forecast for sewer types.” Water Sci. Technol. 46 (6–7): 389–396. https://doi.org/10.2166/wst.2002.0704.
Berardi, L., O. Giustolisi, Z. Kapelan, and D. A. Savic. 2008. “Development of pipe deterioration models for water distribution systems using EPR.” J. Hydroinf. 10 (2): 113–126. https://doi.org/10.2166/hydro.2008.012.
Chugtai, F., and T. Zayed. 2008. “Infrastructure condition prediction models for sustainable sewer pipelines.” J. Perform. Constr. Facil. 22 (5): 333–341. https://doi.org/10.1061/(ASCE)0887-3828(2008)22:5(333).
Cromwell, J., H. Reynolds, and K. Young. 2002. Costs of infrastructure failure.. Denver: American Water Works Association Research Foundation and American Water Works Association.
Damodaran, N., J. Pratt, J. Cromwell, J. Lazo, E. David, R. Raucher, C. Herrick, E. Rambo, A. Deb, and J. Snyder. 2005. Customer acceptance of water main structural reliability. Denver: American Water Works Association Research Foundation.
Ennaouri, I., and M. Fuamba. 2011. “New integrated condition-assessment model for combined storm-sewer systems.” J. Water Resour. Plann. Manage. 139 (1): 53–64. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000217.
Forman, E., and K. Peniwati. 1998. “Aggregating individual judgments and priorities with the analytic hierarchy process.” Eur. J. Oper. Res. 108 (1): 165–169. https://doi.org/10.1016/S0377-2217(97)00244-0.
Gaewski, D., and F. Blaha. 2007. “Analysis of total cost of large diameter pipe failures.” In Proc., AWWA Distribution System Research Symp. Reno, NV: American Water Works Association.
Grigg, N. 2007. Main break prediction, prevention, and control. Denver: American Water Works Association Research Foundation.
Halfawy, M. R., L. Dridi, and S. Baker. 2008. “Integrated decision support system for optimal renewal planning of sewer networks.” J. Comput. Civ. Eng. 22 (6): 360–372. https://doi.org/10.1061/(ASCE)0887-3801(2008)22:6(360).
Hess, J. 2015. “Assessing the condition and consequence of failure of pipes crossing major transportation corridors.” In Proc., Pipelines 2015, 1750–1761. Reston, VA: ASCE.
Kleiner, Y., and B. Rajani. 2001a. “Comprehensive review of structural deterioration of water mains: Physically based models.” Urban Water 3 (3): 131–150. https://doi.org/10.1016/S1462-0758(01)00033-4.
Kleiner, Y., and B. Rajani. 2001b. “Comprehensive review of structural deterioration of water mains: Statistical models.” Urban Water. 3 (3): 131–150. https://doi.org/10.1016/S1462-0758(01)00033-4.
Kleiner, Y., and B. Rajani. 2004. “Quantifying effectiveness of cathodic protection in watermains: Theory.” J. Infrastruct. Syst. 10 (2): 43–51. https://doi.org/10.1061/(ASCE)1076-0342(2004)10:2(43).
Leonelli, R. C. 2012. “Enhancing a decision support tool with sensitivity analysis.” M.Sc. dissertation, Faculty of Engineering and Physical Sciences, Univ. of Manchester.
Liberator, T. 2008. “Assessing pipe condition and risk in Portland, Oregon’s distribution system.” In Proc., Pipelines 2008: Pipeline Asset Management: Maximizing Performance of Our Pipeline Infrastructure, 1–10. Reston, VA: ASCE.
Liu, Z., Y. Kleiner, B. Rajani, L. Wang, and W. Condit. 2012. Condition assessment technologies for water transmission and distribution systems, 108. Washington, DC: USEPA.
Mann, E., and J. Frey. 2011. “Optimized pipe renewal programs ensure cost-effective asset management.” In Proc., Pipelines 2011: A Sound Conduit for Sharing Solutions, 44–54. Reston, VA: ASCE.
Matthews, J., A. Selvakumar, R. Sterling, and W. Condit. 2012. “Analysis of wastewater and water system renewal decision-making tools and approaches.” J. Pipeline Syst. Eng. Pract. 3 (4): 99–105. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000114.
McDonald, S., and J. Zhao. 2001. “Condition assessment and rehabilitation of large sewers.” In Proc., Int. Conf. on Underground Infrastructure Research, 361–369. Waterloo, ON, Canada: Univ. of Waterloo.
Micevski, T., G. Kuczera, and P. Coombes. 2002. “Markov model for storm water pipe deterioration.” J. Infrastruct. Syst. 8 (2): 49–56. https://doi.org/10.1061/(ASCE)1076-0342(2002)8:2(49).
Najafi, M., and G. Kulandaivel. 2005. “Pipeline condition prediction using neural network models.” In Proc., Pipelines 5005: Optimizing Pipeline Design, Operations, and Maintenance in Today’s Economy, 767–781. Reston, VA: ASCE.
NASSCO (National Association of Sewer Service Companies). 2001. NASSCO’s pipeline assessment and certification program manual. Marriotsville, MD: NASSCO.
Nishiyama, M., and Y. Filion. 2013. “Review of statistical water main break prediction models.” Can. J. Civ. Eng. 40 (10): 972–979. https://doi.org/10.1139/cjce-2012-0424.
Park, S., and G. V. Loganathan. 2002. “Optimal pipe replacement analysis with a new pipe break prediction model.” J. Korean Soc. Water Wastewater 16 (6): 710–716.
Petersen, M. D., et al. 2017. “2017 one-year seismic-hazard forecast for the central and eastern United States from induced and natural earthquakes.” Seismol. Res. Lett. 88 (3): 772–783. https://doi.org/10.1785/0220170005.
Pietig, S. 2015. “The condition assessment of a 30-inch ductile iron water line by Waterone of Johnson County, Kansas.” In Proc., Pipelines 2015, 887–899. Reston, VA: ASCE.
Ramanathan, R., and L. S. Ganesh. 1994. “Group preference aggregation methods employed in AHP: An evaluation and an intrinsic process for deriving members’ weight ages.” Eur. J. Oper. Res. 79 (2): 249–265. https://doi.org/10.1016/0377-2217(94)90356-5.
Raucher, R., J. Henderson, M. Duckworth, D. Hughes, T. Helgeson, K. Campanella, J. Oxenford, J. Plattsmier, and M. Akhoondan. 2017. Managing infrastructure risk: The consequence of failure for buried assets. Denver: Water Research Foundation.
Saaty, T. L. 1980. The analytic hierarchy process. New York: McGraw-Hill.
Salem, O., B. Salman, and M. Najafi. 2012. “Culvert asset management practices and deterioration modeling.” Transp. Res. Rec. 2285: 1–7. https://doi.org/10.3141/2285-01.
Salman, B., and O. Salem. 2012. “Risk assessment of wastewater collection lines using failure models and criticality ratings.” J. Pipeline Syst. Eng. Pract. 3 (3): 68–76. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000100.
Shanmuganathan, M., and R. Karthikeyan. 2016. “Limitations of AHP as applied to requirements prioritization.” Artif. Intell. Syst. Mach. Learn. 8 (4): 137–140.
Srdevic, Z., B. Blagojevic, and B. Srdevic. 2011. “AHP based group decision making in ranking loan applicants for purchasing irrigation equipment: A case study.” Bulg. J. Agric. Sci. 17 (4): 531–543.
St. Clair, A. M., and S. Sinha. 2012. “State-of-the-technology review on water pipe condition, deterioration and failure rate prediction models!” Urban Water J. 9 (2): 85–112. https://doi.org/10.1080/1573062X.2011.644566.
Toy, L. P. 2016. “Risk-based asset inspection prioritisation programme.” Accessed February 5, 2018. https://www.waternz.org.nz/Attachment?Action=Download&Attachment_id=449.
USEPA. 2015. “Report to congress on impacts and control of combined sewer overflows and sanitary sewer overflows.” Accessed January 9, 2018. https://www.epa.gov/sites/production/files/2015-10/documents/csossortc2004_full.pdf.
Vladeanu, G. J., and D. D. Koo. 2015. “A comparison study of water pipe failure prediction models using Weibull distribution and binary logistic regression.” In Proc., Pipelines 2015, 1590–1601. Reston, VA: ASCE.
Vladeanu, G. J., and J. C. Matthews. 2018a. “Analysis of risk management methods used in trenchless renewal decision making.” Tunnelling Underground Space Technol. 72: 272–280. https://doi.org/10.1016/j.tust.2017.11.025.
Vladeanu, G. J., and J. C. Matthews. 2018b. “Consequence of failure of sewers (COFS) model for risk-based asset management using analytic hierarchy process.” In Proc., Pipelines 2018. Reston, VA: ASCE.
Wirahadikusumah, R., D. Abraham, and D. T. Iseley. 2001. “Challenging issues in modeling deterioration of combined sewers.” J. Infrastruct. Syst. 7 (2): 77–84. https://doi.org/10.1061/(ASCE)1076-0342(2001)7:2(77).
Zhang, S., and H. S. Wong. 2010. “ARImp: A generalized adjusted rand index for cluster ensembles.” In Proc., 20th Int. Conf. on Pattern Recognition, 778–781. New York: IEEE.
Information & Authors
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Published In
Journal of Pipeline Systems Engineering and Practice
Volume 10 • Issue 2 • May 2019
Copyright
©2019 American Society of Civil Engineers.
History
Received: May 23, 2018
Accepted: Sep 26, 2018
Published online: Jan 31, 2019
Published in print: May 1, 2019
Discussion open until: Jun 30, 2019
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