Consequence of Failure Modeling for Water Pipeline Infrastructure Using a Hierarchical Ensemble Fuzzy Inference System
Publication: Journal of Infrastructure Systems
Volume 29, Issue 1
Abstract
Risk-based asset management of water pipes is important to support pipe renewal prioritization decisions. Risk is a function of the likelihood and consequence of failure. Certain gaps were observed from the literature and a practice review of the consequences of failure modeling related to lack of data used, methodologies used, and the model testing quality. This study proposes a novel fuzzy inference system (FIS)–based consequence of failure model to assess the comprehensive failure impacts of a water pipe based on economic impacts, social impacts, environmental impacts, operational characteristics, and renewal complexity and ranks pipes into a 0 = insignificant to 5 = catastrophic scale. The 20 input parameters categorized into five modules and the 381 fuzzy rules are based on published literature, secondary data, and interviews with 25 experts from large water utilities, consultancies, and pipe associations. The model results can also be visualized on a geographic information system (GIS) for each pipe segment in the water distribution and transmission network. The applicability of the proposed model was evaluated based on data from a large water utility in the US, and the sensitive parameters were also identified. The results from model validation indicated that the proposed fuzzy-based methodology was useful for accurately modeling the consequences of failure of water pipes achieving a high root mean square error (RMSE) of 0.96.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some data used during this study are confidential in nature and may only be provided with restrictions. The list of data that are confidential has been provided. Some of these are direct parameters from the model (like cost of property damage, historical cost of legal issues, time to shutdown, and so on), whereas others are used as indicators to derive the remaining parameters (like geospatial locations of pipes and breaks):
•
Geospatial data of pipe location,
•
Geospatial data of historical breaks,
•
Historical cost of legal issues due to flooding from water main breaks,
•
Historical boil water advisories due to water main breaks,
•
Historical fines for environmental damage,
•
Cost of property damage, and
•
Time to shutdown.
Acknowledgments
The authors would like to thank the United States Bureau of Reclamation (USBR) and Sustainable Water Infrastructure Management (SWIM) Center at Virginia Tech for providing funding and support for this research.
References
ASCE. 2017. “ASCE’s 2017 infrastructure report card.” Accessed November 1, 2020. https://infrastructurereportcard.org/tag/2017-infrastructure-report-card/.
Blaha, F. J., P. E. Gaewski, P. R. McCary, and G. T. Coates. 2014. “Revisiting the topic of liability for damage due to water main breaks or leaks.” In Proc., Pipelines 2014: From Underground to the Forefront of Innovation and Sustainability, 1649–1662. Reston, VA: ASCE. https://doi.org/10.1061/9780784413692.150.
Bruaset, S., S. Sægrov, and R. Ugarelli. 2018. “Performance-based modelling of long-term deterioration to support rehabilitation and investment decisions in drinking water distribution systems.” Urban Water J. 15 (1): 46–52. https://doi.org/10.1080/1573062X.2017.1395894.
Bubtiena, A. M., A. H. Elshafie, and O. Jafaar. 2011. “Application of artificial neural networks in modeling water networks.” In Proc., 2011 IEEE 7th Int. Colloquium on Signal Processing and Its Applications, 50–57. New York: IEEE. https://doi.org/10.1109/cspa.2011.5759841.
Chen, C., G. Flintsch, and I. Al-Qadi. 2004. “Fuzzy logic-based life-cycle costs analysis model for pavement and asset management.” Accessed November 1, 2020. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.652.2084&rep=rep1&type=pdf.
Christodoulou, S., A. Agathokleous, B. Charalambous, and A. Adamou. 2010. “Proactive risk-based integrity assessment of water distribution networks.” Water Resour. Manage. 24 (13): 3715–3730. https://doi.org/10.1007/s11269-010-9629-5.
Cromwell, J. E. 2002. Costs of infrastructure failure. Denver: American Water Works Association.
Damodaran, N., J. Pratt, J. Cromwell, E. David, and J. Lazo. 2005. Customer acceptance of water main structural reliability. Denver: American Water Works Association.
Dawood, T., E. Elwakil, H. M. Novoa, and J. F. G. Delgado. 2020. “Artificial intelligence for the modeling of water pipes deterioration mechanisms.” Autom. Constr. 120 (Dec): 103398. https://doi.org/10.1016/j.autcon.2020.103398.
Dawood, T., E. Elwakil, H. M. Novoa, and J. F. G. Delgado. 2021. “Toward urban sustainability and clean potable water: Prediction of water quality via artificial neural networks.” J. Cleaner Prod. 291 (Apr): 125266. https://doi.org/10.1016/j.jclepro.2020.125266.
Evans, B., A. S. Chen, A. Prior, S. Djordjevic, D. A. Savic, D. Butler, P. Goodey, J. R. Stevens, and G. Colclough. 2018. “Mapping urban infrastructure interdependencies and fuzzy risks.” Procedia Eng. 212 (Jan): 816–823. https://doi.org/10.1016/j.proeng.2018.01.105.
Fayaz, M., Q. B. Pham, N. T. T. Linh, P. T. T. Nhi, D. N. Khoi, M. S. Qureshi, A. S. Shah, and S. Khalid. 2020. “A water supply pipeline risk analysis methodology based on DIY and hierarchical fuzzy inference.” Symmetry 12 (1): 44. https://doi.org/10.3390/sym12010044.
Gaewski, P. E., and F. J. Blaha. 2007. “Analysis of total cost of large diameter pipe failures.” Proc., AWWA Research Symp. Distribution Systems: The Next Frontier. Denver: American Water Works Association.
Gan, Y., Q. Duan, W. Gong, C. Tong, Y. Sun, W. Chu, A. Ye, C. Miao, and Z. Di. 2014. “A comprehensive evaluation of various sensitivity analysis methods: A case study with a hydrological model.” Environ. Modell. Software 51 (Jan): 269–285. https://doi.org/10.1016/j.envsoft.2013.09.031.
Grigg, N. 2007. Main break prediction, prevention, and control. Denver: AWWA Research Foundation, American Water Works Association.
Higgins, M. S., A. Stroebele, and S. Zahidi. 2012. “Numbers don’t lie: PCCP performance and deterioration based on a statistical review of a decade of condition assessment data.” In Proc., Pipelines 2012, 298–306. Reston, VA: ASCE. https://doi.org/10.1061/9780784412480.027.
Homma, T., and A. Saltelli. 1996. “Importance measures in global sensitivity analysis of nonlinear models.” Reliab. Eng. Syst. Saf. 52 (1): 1–17. https://doi.org/10.1016/0951-8320(96)00002-6.
Kutyłowska, M. 2015. “Neural network approach for failure rate prediction.” Eng. Fail. Anal. 47 (Jan): 41–48. https://doi.org/10.1016/j.engfailanal.2014.10.007.
Mamdani, E. H., and S. Assilian. 1975. “An experiment in linguistic synthesis with a fuzzy logic controller.” Int. J. Man Mach. Stud. 7 (1): 1–13. https://doi.org/10.1016/S0020-7373(75)80002-2.
Marzouk, M., and A. Osama. 2017. “Fuzzy-based methodology for integrated infrastructure asset management.” Int. J. Comput. Intell. Syst. 10 (1): 745. https://doi.org/10.2991/ijcis.2017.10.1.50.
NRC (National Academies Press). 2006. Drinking water distribution systems: Assessing and reducing risks. Washington, DC: NRC.
Parvizsedghy, L., and T. Zayed. 2015. “Consequence of failure: Neurofuzzy-based prediction model for gas pipelines.” J. Perform. Constr. Facil. 30 (4): 04015073. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000817.
Raucher, R. S. 2017. Managing infrastructure risk: The consequence of failure for buried assets. Denver: Water Research Foundation.
Robles-Velasco, A., P. Cortés, J. Muñuzuri, and L. Onieva. 2020. “Prediction of pipe failures in water supply networks using logistic regression and support vector classification.” Reliab. Eng. Syst. Saf. 196 (Apr): 106754. https://doi.org/10.1016/j.ress.2019.106754.
Sadiq, R., Y. Kleiner, and B. Rajani. 2007. “Water quality failures in distribution networks—Risk analysis using fuzzy logic and evidential reasoning.” Risk Anal. 27 (5): 1381–1394. https://doi.org/10.1111/j.1539-6924.2007.00972.x.
Saltelli, A. 2002. “Sensitivity analysis for importance assessment.” Risk Anal. 22 (3): 579–590. https://doi.org/10.1111/0272-4332.00040.
Shin, H., C. Joo, and J. Koo. 2016. “Optimal rehabilitation model for water pipeline systems with genetic algorithm.” Procedia Eng. 154 (Jan): 384–390. https://doi.org/10.1016/j.proeng.2016.07.497.
Singh, J., and S. K. Sinha. 2016. “Nationwide survey on the pipe performance for drinking water and force main pressure pipe applications.” In Proc., Pipelines 2016. Reston, VA: ASCE. https://doi.org/10.1061/9780784479957.044.
Sobol, I. M. 1990. “On sensitivity estimation for nonlinear mathematical models.” Matematicheskoe Modelirovanie 2 (1): 112–118.
Sobol, I. M. 2001. “Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates.” Math. Comput. Simul. 55 (1–3): 271–280. https://doi.org/10.1016/S0378-4754(00)00270-6.
St. Clair, A. M., and S. K. Sinha. 2014. “Development of a fuzzy inference performance index for ferrous drinking water pipelines.” J. Pipeline Syst. Eng. Pract. 5 (3): 04014003. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000168.
Tabesh, M., M. Jamasb, and R. Moeini. 2011. “Calibration of water distribution hydraulic models: A comparison between pressure dependent and demand driven analyses.” Urban Water J. 8 (2): 93–102. https://doi.org/10.1080/1573062X.2010.548525.
Tabesh, M., J. Soltani, R. Farmani, and D. Savic. 2009. “Assessing pipe failure rate and mechanical reliability of water distribution networks using data-driven modeling.” J. Hydroinf. 11 (1): 1–17. https://doi.org/10.2166/hydro.2009.008.
Valis, D., K. Hasilova, M. Forbelska, and K. Pietrucha-Urbanik. 2017. “Modelling water distribution network failures and deterioration.” In Proc., 2017 IEEE Int. Conf. on Industrial Engineering and Engineering Management (IEEM), 924–928. New York: IEEE. https://doi.org/10.1109/IEEM.2017.8290027.
Vishwakarma, A., and S. K. Sinha. 2020. “Development of a consequence of failure model and risk matrix for water pipelines infrastructure systems.” In Proc., Pipelines 2020, 169–177. Reston, VA: ASCE. https://doi.org/10.1061/9780784483213.019.
Vladeanu, G. J., and J. C. Matthews. 2019. “Consequence-of-failure model for risk-based asset management of wastewater pipes using AHP.” J. Pipeline Syst. Eng. Pract. 10 (2): 04019005. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000370.
Yannopoulos, S., and M. Spiliotis. 2013. “Water distribution system reliability based on minimum cut–set approach and the hydraulic availability.” Water Resour. Manage. 27 (6): 1821–1836. https://doi.org/10.1007/s11269-012-0163-5.
Yariyan, P., H. Zabihi, I. D. Wolf, M. Karami, and S. Amiriyan. 2020. “Earthquake risk assessment using an integrated Fuzzy Analytic Hierarchy Process with Artificial Neural Networks based on GIS: A case study of Sanandaj in Iran.” Int. J. Disaster Risk Reduct. 50 (Nov): 101705. https://doi.org/10.1016/j.ijdrr.2020.101705.
Zadeh, L. A. 1965. “Fuzzy sets.” Inf. Control 8 (3): 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X.
Zadeh, L. A. 1997. “The roles of fuzzy logic and soft computing in the conception, design and deployment of intelligent systems.” In Software agents and soft computing towards enhancing machine intelligence, 181–190. New York: Springer. https://doi.org/10.1007/3-540-62560-7_45.
Zangenehmadar, Z., and O. Moselhi. 2016. “Application of neural networks in predicting the remaining useful life of water pipelines.” In Proc., Pipelines 2016, 292–308. Reston, VA: ASCE.
Zarghamee, M. S., P. G. Cranston, R. Fongemie, and D. Wittas. 2011. “Statistical analysis of condition assessment data and prediction of future performance of PCCP.” In Proc., Pipelines 2011, 160–169. Reston, VA: ASCE. https://doi.org/10.1061/41187(420)16.
Zhang, X.-Y., M. N. Trame, L. J. Lesko, and S. Schmidt. 2015. “Sobol sensitivity analysis: A tool to guide the development and evaluation of systems pharmacology models.” CPT: Pharmacometrics Syst. Pharmacol. 4 (2): 69–79. https://doi.org/10.1002/psp4.6.
Information & Authors
Information
Published In
Journal of Infrastructure Systems
Volume 29 • Issue 1 • March 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Mar 14, 2021
Accepted: Aug 20, 2022
Published online: Oct 20, 2022
Published in print: Mar 1, 2023
Discussion open until: Mar 20, 2023
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.