IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v240y2015i1p220-234.html
   My bibliography  Save this article

Evaluating risk of water mains failure using a Bayesian belief network model

Author

Listed:
  • Kabir, Golam
  • Tesfamariam, Solomon
  • Francisque, Alex
  • Sadiq, Rehan

Abstract

It has been reported that since year 2000, there have been an average 700 water main breaks per day only in Canada and the USA costing more than CAD 10 billions/year. Moreover, water main leaks affect other neighboring infrastructure that may lead to catastrophic failures. For this, municipality authorities or stakeholders are more concerned about preventive actions rather reacting to failure events. This paper presents a Bayesian Belief Network (BBN) model to evaluate the risk of failure of metallic water mains using structural integrity, hydraulic capacity, water quality, and consequence factors. BBN is a probabilistic graphical model that represents a set of variables and their probabilistic relationships, which also captures historical information about these dependencies. The proposed model is capable of ranking water mains within distribution network that can identify vulnerable and sensitive pipes to justify proper decision action for maintenance/rehabilitation/replacement (M/R/R). To demonstrate the application of proposed model, water distribution network of City of Kelowna has been studied. Result indicates that almost 9% of the total 259 metallic pipes are at high risk in both summer and winter.

Suggested Citation

  • Kabir, Golam & Tesfamariam, Solomon & Francisque, Alex & Sadiq, Rehan, 2015. "Evaluating risk of water mains failure using a Bayesian belief network model," European Journal of Operational Research, Elsevier, vol. 240(1), pages 220-234.
  • Handle: RePEc:eee:ejores:v:240:y:2015:i:1:p:220-234
    DOI: 10.1016/j.ejor.2014.06.033
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221714005360
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ejor.2014.06.033?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Nadkarni, Sucheta & Shenoy, Prakash P., 2001. "A Bayesian network approach to making inferences in causal maps," European Journal of Operational Research, Elsevier, vol. 128(3), pages 479-498, February.
    2. Matos, Manuel A., 2007. "Decision under risk as a multicriteria problem," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1516-1529, September.
    3. Sun, Lili & Shenoy, Prakash P., 2007. "Using Bayesian networks for bankruptcy prediction: Some methodological issues," European Journal of Operational Research, Elsevier, vol. 180(2), pages 738-753, July.
    4. Poropudas, Jirka & Virtanen, Kai, 2011. "Simulation metamodeling with dynamic Bayesian networks," European Journal of Operational Research, Elsevier, vol. 214(3), pages 644-655, November.
    5. Bennett, Joanna C. & Bohoris, George A. & Aspinwall, Elaine M. & Hall, Richard C., 1996. "Risk analysis techniques and their application to software development," European Journal of Operational Research, Elsevier, vol. 95(3), pages 467-475, December.
    6. He, Ying & Huang, Rui-Hua, 2008. "Risk attributes theory: Decision making under risk," European Journal of Operational Research, Elsevier, vol. 186(1), pages 243-260, April.
    7. Lauria, Eitel J.M. & Duchessi, Peter J., 2007. "A methodology for developing Bayesian networks: An application to information technology (IT) implementation," European Journal of Operational Research, Elsevier, vol. 179(1), pages 234-252, May.
    8. Marlow, David R. & Beale, David J. & Mashford, John S., 2012. "Risk-based prioritization and its application to inspection of valves in the water sector," Reliability Engineering and System Safety, Elsevier, vol. 100(C), pages 67-74.
    9. Häger, David & Andersen, Lasse B., 2010. "A knowledge based approach to loss severity assessment in financial institutions using Bayesian networks and loss determinants," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1635-1644, December.
    10. Sadiq, Rehan & Tesfamariam, Solomon, 2007. "Probability density functions based weights for ordered weighted averaging (OWA) operators: An example of water quality indices," European Journal of Operational Research, Elsevier, vol. 182(3), pages 1350-1368, November.
    11. Janssens, Davy & Wets, Geert & Brijs, Tom & Vanhoof, Koen & Arentze, Theo & Timmermans, Harry, 2006. "Integrating Bayesian networks and decision trees in a sequential rule-based transportation model," European Journal of Operational Research, Elsevier, vol. 175(1), pages 16-34, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kabir, Golam & Balek, Ngandu Balekelayi Celestin & Tesfamariam, Solomon, 2018. "Consequence-based framework for buried infrastructure systems: A Bayesian belief network model," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 290-301.
    2. Kabir, Golam & Tesfamariam, Solomon & Sadiq, Rehan, 2015. "Predicting water main failures using Bayesian model averaging and survival modelling approach," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 498-514.
    3. Albara M. Mustafa & Abbas Barabadi, 2021. "Resilience Assessment of Wind Farms in the Arctic with the Application of Bayesian Networks," Energies, MDPI, vol. 14(15), pages 1-15, July.
    4. Deng, Yong, 2016. "Deng entropy," Chaos, Solitons & Fractals, Elsevier, vol. 91(C), pages 549-553.
    5. Rahimi-Golkhandan, Armin & Aslani, Babak & Mohebbi, Shima, 2022. "Predictive resilience of interdependent water and transportation infrastructures: A sociotechnical approach," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    6. De Iuliis, Melissa & Kammouh, Omar & Cimellaro, Gian Paolo & Tesfamariam, Solomon, 2021. "Quantifying restoration time of power and telecommunication lifelines after earthquakes using Bayesian belief network model," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    7. Dawid Szpak, 2020. "Method for Determining the Probability of a Lack of Water Supply to Consumers," Energies, MDPI, vol. 13(20), pages 1-16, October.
    8. Peyman Zandi & Mohammad Rahmani & Mojtaba Khanian & Amir Mosavi, 2020. "Agricultural Risk Management Using Fuzzy TOPSIS Analytical Hierarchy Process (AHP) and Failure Mode and Effects Analysis (FMEA)," Agriculture, MDPI, vol. 10(11), pages 1-27, October.
    9. Robles-Velasco, Alicia & Cortés, Pablo & Muñuzuri, Jesús & Onieva, Luis, 2020. "Prediction of pipe failures in water supply networks using logistic regression and support vector classification," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    10. Quintanar-Gago, David A. & Nelson, Pamela F. & Díaz-Sánchez, à ngeles & Boldrick, Michael S., 2021. "Assessment of steam turbine blade failure and damage mechanisms using a Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    11. Jiansong Wu & Zhuqiang Hu & Jinyue Chen & Zheng Li, 2018. "Risk Assessment of Underground Subway Stations to Fire Disasters Using Bayesian Network," Sustainability, MDPI, vol. 10(10), pages 1-21, October.
    12. Lina Han & Qing Ma & Feng Zhang & Yichen Zhang & Jiquan Zhang & Yongbin Bao & Jing Zhao, 2019. "Risk Assessment of An Earthquake-Collapse-Landslide Disaster Chain by Bayesian Network and Newmark Models," IJERPH, MDPI, vol. 16(18), pages 1-17, September.
    13. Gema Sakti Raspati & Stian Bruaset & Camillo Bosco & Lars Mushom & Birgitte Johannessen & Rita Ugarelli, 2022. "A Risk-Based Approach in Rehabilitation of Water Distribution Networks," IJERPH, MDPI, vol. 19(3), pages 1-24, January.
    14. Mancuso, A. & Compare, M. & Salo, A. & Zio, E., 2019. "Portfolio optimization of safety measures for the prevention of time-dependent accident scenarios," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
    15. Ramos-Salgado, Cristóbal & Muñuzuri, Jesús & Aparicio-Ruiz, Pablo & Onieva, Luis, 2022. "A comprehensive framework to efficiently plan short and long-term investments in water supply and sewer networks," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    16. Iman Moslehi & Mohammadreza Jalili_Ghazizadeh, 2020. "Pressure-Pipe Breaks Relationship in Water Distribution Networks: A Statistical Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 2851-2868, July.
    17. Mrinal Kanti Sen & Subhrajit Dutta & Golam Kabir, 2021. "Flood Resilience of Housing Infrastructure Modeling and Quantification Using a Bayesian Belief Network," Sustainability, MDPI, vol. 13(3), pages 1-24, January.
    18. Vafadarnikjoo, Amin & Chalvatzis, Konstantinos & Botelho, Tiago & Bamford, David, 2023. "A stratified decision-making model for long-term planning: Application in flood risk management in Scotland," Omega, Elsevier, vol. 116(C).
    19. Kammouh, Omar & Gardoni, Paolo & Cimellaro, Gian Paolo, 2020. "Probabilistic framework to evaluate the resilience of engineering systems using Bayesian and dynamic Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    20. Bai, Xiwen & Cheng, Liangqi & Iris, Çağatay, 2022. "Data-driven financial and operational risk management: Empirical evidence from the global tramp shipping industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    21. Zhang, Mingming & Zhou, Simei & Wang, Qunwei & Liu, Liyun & Zhou, Dequn, 2023. "Will the carbon neutrality target impact China's energy security? A dynamic Bayesian network model," Energy Economics, Elsevier, vol. 125(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ülengin, Füsun & Önsel, Şule & Aktas, Emel & Kabak, Özgür & Özaydın, Özay, 2014. "A decision support methodology to enhance the competitiveness of the Turkish automotive industry," European Journal of Operational Research, Elsevier, vol. 234(3), pages 789-801.
    2. Garvey, Myles D. & Carnovale, Steven & Yeniyurt, Sengun, 2015. "An analytical framework for supply network risk propagation: A Bayesian network approach," European Journal of Operational Research, Elsevier, vol. 243(2), pages 618-627.
    3. Wu, Wei-Wen & Lan, Lawrence W. & Lee, Yu-Ting, 2012. "Exploring the critical pillars and causal relations within the NRI: An innovative approach," European Journal of Operational Research, Elsevier, vol. 218(1), pages 230-238.
    4. D. B. Matellini & A. D. Wall & I. D. Jenkinson & J. Wang & R. Pritchard, 2018. "A Three‐Part Bayesian Network for Modeling Dwelling Fires and Their Impact upon People and Property," Risk Analysis, John Wiley & Sons, vol. 38(10), pages 2087-2104, October.
    5. Önsel Ekici, Şule & Kabak, Özgür & Ülengin, Füsun, 2019. "Improving logistics performance by reforming the pillars of Global Competitiveness Index," Transport Policy, Elsevier, vol. 81(C), pages 197-207.
    6. Michail Tsagris, 2021. "A New Scalable Bayesian Network Learning Algorithm with Applications to Economics," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 341-367, January.
    7. De Iuliis, Melissa & Kammouh, Omar & Cimellaro, Gian Paolo & Tesfamariam, Solomon, 2021. "Quantifying restoration time of power and telecommunication lifelines after earthquakes using Bayesian belief network model," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    8. Rogerson, Ellen C. & Lambert, James H., 2012. "Prioritizing risks via several expert perspectives with application to runway safety," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 22-34.
    9. Gunasekaran, Angappa & Subramanian, Nachiappan & Papadopoulos, Thanos, 2017. "Information technology for competitive advantage within logistics and supply chains: A review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 99(C), pages 14-33.
    10. Chiuling Lu & Ann Yang & Jui-Feng Huang, 2015. "Bankruptcy predictions for U.S. air carrier operations: a study of financial data," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 39(3), pages 574-589, July.
    11. Alberti, Alexandre R. & Cavalcante, Cristiano A.V. & Scarf, Philip & Silva, André L.O., 2018. "Modelling inspection and replacement quality for a protection system," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 145-153.
    12. Carlos Llopis-Albert & José M. Merigó & Huchang Liao & Yejun Xu & Juan Grima-Olmedo & Carlos Grima-Olmedo, 2018. "Water Policies and Conflict Resolution of Public Participation Decision-Making Processes Using Prioritized Ordered Weighted Averaging (OWA) Operators," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(2), pages 497-510, January.
    13. Løken, Espen & Botterud, Audun & Holen, Arne T., 2009. "Use of the equivalent attribute technique in multi-criteria planning of local energy systems," European Journal of Operational Research, Elsevier, vol. 197(3), pages 1075-1083, September.
    14. Verda Kocabas & Suzana Dragicevic, 2013. "Bayesian networks and agent-based modeling approach for urban land-use and population density change: a BNAS model," Journal of Geographical Systems, Springer, vol. 15(4), pages 403-426, October.
    15. Desai, Vikram & Bucaro, Anthony C. & Kim, Joung W. & Srivastava, Rajendra & Desai, Renu, 2023. "Toward a better expert system for auditor going concern opinions using Bayesian network inflation factors," International Journal of Accounting Information Systems, Elsevier, vol. 49(C).
    16. Feuerriegel, Stefan & Gordon, Julius, 2019. "News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions," European Journal of Operational Research, Elsevier, vol. 272(1), pages 162-175.
    17. Mandhani, Jyoti & Nayak, Jogendra Kumar & Parida, Manoranjan, 2020. "Interrelationships among service quality factors of Metro Rail Transit System: An integrated Bayesian networks and PLS-SEM approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 140(C), pages 320-336.
    18. Beatriz Molina Serrano & Nicoleta González-Cancelas & Francisco Soler-Flores & Samir Awad-Nuñez & Alberto Camarero Orive, 2018. "Use of Bayesian Networks to Analyze Port Variables in Order to Make Sustainable Planning and Management Decision," Logistics, MDPI, vol. 2(1), pages 1-16, January.
    19. Ligang Zhou & Kin Keung Lai, 2017. "AdaBoost Models for Corporate Bankruptcy Prediction with Missing Data," Computational Economics, Springer;Society for Computational Economics, vol. 50(1), pages 69-94, June.
    20. Amrin, Andas & Zarikas, Vasileios & Spitas, Christos, 2018. "Reliability analysis and functional design using Bayesian networks generated automatically by an “Idea Algebra†framework," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 211-225.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:240:y:2015:i:1:p:220-234. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.