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Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system

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  • Tang, Kayu
  • Parsons, David J.
  • Jude, Simon

Abstract

The reliability of the water distribution system is critical to maintaining a secure supply for the population, industry and agriculture, so there is a need for proactive maintenance to help reduce water loss and down times. Bayesian networks are one approach to modelling the complexity of water mains, to assist water utility companies in planning maintenance. This paper compares and analyses how accurately the Bayesian network structure can be derived given a large and highly variable dataset. Method one involved using automated learning algorithms to build the Bayesian network, while method two involved a guided method using a combination of historic failure data, prior knowledge and pre-modelling data exploration of the water mains. By understanding common failure types (circumferential, longitudinal, pinhole and joint), the guided learning Bayesian Network was able to capture the interactions of the surrounding soil environment with the physical properties of pipes. The Bayesian network built using data exploration and literature was able to achieve an overall accuracy of 81.2% when predicting the specific type of water mains failure compared to the 84.4% for the automated method. The slightly greater accuracy from the automated method was traded for a sparser Bayes net where the interpretation of the interactions between the variables was clearer and more meaningful.

Suggested Citation

  • Tang, Kayu & Parsons, David J. & Jude, Simon, 2019. "Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 24-36.
    • Handle: RePEc:eee:reensy:v:186:y:2019:i:c:p:24-36
    DOI: 10.1016/j.ress.2019.02.001
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    2. Wu, Jiansong & Bai, Yiping & Fang, Weipeng & Zhou, Rui & Reniers, Genserik & Khakzad, Nima, 2021. "An Integrated Quantitative Risk Assessment Method for Urban Underground Utility Tunnels," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    3. 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).
    4. Zhang, Qiongfang & Xu, Nan & Ersoy, Daniel & Liu, Yongming, 2022. "Manifold-based Conditional Bayesian network for aging pipe yield strength estimation with non-destructive measurements," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    5. Rifaai, Talha M. & Abokifa, Ahmed A. & Sela, Lina, 2022. "Integrated approach for pipe failure prediction and condition scoring in water infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    6. Wei Liu & Binhao Wang & Zhaoyang Song, 2022. "Failure Prediction of Municipal Water Pipes Using Machine Learning Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1271-1285, March.
    7. Li, Xiaoyue & Yang, Yinfei & Li, Liang & Zhao, Guolong & He, Ning, 2020. "Uncertainty quantification in machining deformation based on Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    8. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    9. Wang, Run-Zi & Gu, Hang-Hang & Zhu, Shun-Peng & Li, Kai-Shang & Wang, Ji & Wang, Xiao-Wei & Hideo, Miura & Zhang, Xian-Cheng & Tu, Shan-Tung, 2022. "A data-driven roadmap for creep-fatigue reliability assessment and its implementation in low-pressure turbine disk at elevated temperatures," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    10. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    11. Yaser Amiri-Ardakani & Mohammad Najafzadeh, 2021. "Pipe Break Rate Assessment While Considering Physical and Operational Factors: A Methodology based on Global Positioning System and Data-Driven Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(11), pages 3703-3720, September.
    12. Yaxin Shi & Suning Liu & Haiyun Shi, 2022. "Analysis of the Water-Food-Energy Nexus and Water Competition Based on a Bayesian Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 3349-3366, July.
    13. Fan, Xudong & Wang, Xiaowei & Zhang, Xijin & ASCE Xiong (Bill) Yu, P.E.F., 2022. "Machine learning based water pipe failure prediction: The effects of engineering, geology, climate and socio-economic factors," Reliability Engineering and System Safety, Elsevier, vol. 219(C).

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