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Pipe Networks Risk Assessment Based on Survival Analysis

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  • Stavroula Tsitsifli
  • Vasilis Kanakoudis
  • Ioannis Bakouros

Abstract

Integrated management of pipe networks should include methods for monitoring, repairing and replacing deteriorating components (usually pipes), but also methods and everyday operating practices towards a proactive risk assessment approach in order to give a solid answer to the unavoidable “repair or replace” dilemma. The present paper attempts to check whether the Discriminant Analysis and Classification (DAC) method can be used to achieve the above mentioned goals and predict the future behaviour of network pipes. Three pipe networks carrying different types of fluids (oil; gas; and water) are used as case studies. For each case study network, the DAC method is used to classify the pipes into two groups (failures/successes), based on simple variables (pipe/network characteristics) and dimensionless joint ones. Several scenarios are being analysed for each case. The results for the two cases of oil and gas networks are very satisfying. The implementation of the DAC method to water pipe networks needs to overcome serious problems related to the quality, reliability and compatibility of the data records provided by the Water Utilities. In this paper, these shortcomings are faced combining field data with theoretical one. Also the distinction between what “failure” and “success” actually mean in a water pipe network has to be determined. The present study uses the total water volume being lost as a definition criterion. Copyright Springer Science+Business Media B.V. 2011

Suggested Citation

  • Stavroula Tsitsifli & Vasilis Kanakoudis & Ioannis Bakouros, 2011. "Pipe Networks Risk Assessment Based on Survival Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(14), pages 3729-3746, November.
    • Handle: RePEc:spr:waterr:v:25:y:2011:i:14:p:3729-3746
    DOI: 10.1007/s11269-011-9881-3
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    References listed on IDEAS

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    1. Symeon Christodoulou & Alexandra Deligianni, 2010. "A Neurofuzzy Decision Framework for the Management of Water Distribution Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(1), pages 139-156, January.
    2. Ayse Muhammetoglu & Habib Muhammetoglu & Sedat Oktas & Levent Ozgokcen & Selcuk Soyupak, 2005. "Impact Assessment of Different Management Scenarios on Water Quality of Porsuk River and Dam System – Turkey," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 19(2), pages 199-210, April.
    3. S. Park & H. Jun & B. Kim & G. Im, 2008. "Modeling of Water Main Failure Rates Using the Log-linear ROCOF and the Power Law Process," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(9), pages 1311-1324, September.
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    Cited by:

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    2. Chen, Thomas Ying-Jeh & Guikema, Seth David, 2020. "Prediction of water main failures with the spatial clustering of breaks," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    3. Symeon Christodoulou & Anastasis Gagatsis & Savvas Xanthos & Sofia Kranioti & Agathoklis Agathokleous & Michalis Fragiadakis, 2013. "Entropy-Based Sensor Placement Optimization for Waterloss Detection in Water Distribution Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(13), pages 4443-4468, October.
    4. Qiang Xu & Qiuwen Chen & Jinfeng Ma & Koen Blanckaert & Zhonghua Wan, 2014. "Water Saving and Energy Reduction through Pressure Management in Urban Water Distribution Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(11), pages 3715-3726, September.
    5. Young Hwan Choi & Donghwi Jung, 2020. "Development of Cross-Domain Artificial Neural Network to Predict High-Temporal Resolution Pressure Data," Sustainability, MDPI, vol. 12(9), pages 1-17, May.
    6. Xiao, Rui & Zayed, Tarek & Meguid, Mohamed A. & Sushama, Laxmi, 2024. "Improving failure modeling for gas transmission pipelines: A survival analysis and machine learning integrated approach," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    7. Ahmed M. A. Sattar & B. Gharabaghi & Edward A. McBean, 2016. "Prediction of Timing of Watermain Failure Using Gene Expression Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1635-1651, March.
    8. Ahmed Sattar & B. Gharabaghi & Edward McBean, 2016. "Prediction of Timing of Watermain Failure Using Gene Expression Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1635-1651, March.
    9. I. Karadirek & S. Kara & G. Yilmaz & A. Muhammetoglu & H. Muhammetoglu, 2012. "Implementation of Hydraulic Modelling for Water-Loss Reduction Through Pressure Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(9), pages 2555-2568, July.

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