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Investigating the Impact of Cumulative Pressure-Induced Stress on Machine Learning Models for Pipe Breaks

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  • Charalampos Konstantinou

    (University of Cyprus)

  • Carlos Jara-Arriagada

    (Imperial College London)

  • Ivan Stoianov

    (Imperial College London)

Abstract

Significant financial resources are needed for the maintenance and rehabilitation of water supply networks (WSNs) to prevent pipe breaks. The causes and mechanisms for pipe breaks vary between different WSNs. However, it is commonly acknowledged that the operational management and water pressure influence significantly the frequency of pipe breaks. Pipe breaks occur when the water pressure exceeds the tensile strength of a pipe, or due to repetitive pressure cycles that result in fatigue-related failures. Considering these pipe failure modes, a new metric known as cumulative pressure-induced stress has been introduced. This metric takes into account both static and dynamic pressure components that contribute to pipe breaks, including mean pressure and the magnitude and frequency of pressure fluctuations, respectively. The impact of CPIS on pipe breaks has not been extensively investigated. Consequently, this study investigates and evaluates the impact of this metric when incorporated as an explanatory variable in Random Forest (RF) models that analyse the key causes of pipe breaks in two WSNs. Different RF models were developed both with and without incorporating pressure components. Subsequently, the performance of these models and the significance of each input variable were assessed. The results of this study suggest that CPIS is an important variable, especially in cases where pressure-related factors play a significant role in pipe breaks. Consequently, incorporating CPIS has shown a notable improvement in the accuracy of pipe break models.

Suggested Citation

  • Charalampos Konstantinou & Carlos Jara-Arriagada & Ivan Stoianov, 2024. "Investigating the Impact of Cumulative Pressure-Induced Stress on Machine Learning Models for Pipe Breaks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(2), pages 603-619, January.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:2:d:10.1007_s11269-023-03687-7
    DOI: 10.1007/s11269-023-03687-7
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    References listed on IDEAS

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    1. Zahra Pouri & Morteza Heidarimozaffar, 2022. "Spatial Analysis and Failure Management in Water Distribution Networks Using Fuzzy Inference System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 1783-1797, April.
    2. 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.
    3. Yuan Huang & Feifei Zheng & Huan-Feng Duan & Qingzhou Zhang, 2020. "Multi-Objective Optimal Design of Water Distribution Networks Accounting for Transient Impacts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(4), pages 1517-1534, March.
    4. 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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    Cited by:

    1. Amira Rjaibi & Sophie Duchesne, 2024. "Impact of Pressure on the Deterioration of Drinking Water Distribution Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(12), pages 4867-4882, September.

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