IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v35y2021i11d10.1007_s11269-021-02911-6.html
   My bibliography  Save this article

Pipe Break Rate Assessment While Considering Physical and Operational Factors: A Methodology based on Global Positioning System and Data-Driven Techniques

Author

Listed:
  • Yaser Amiri-Ardakani

    (Graduate University of Advanced Technology)

  • Mohammad Najafzadeh

    (Graduate University of Advanced Technology)

Abstract

An accurate prediction of pipes failure rate plays a substantial role in the management of Water Distribution Networks (WDNs). In this study, a field study was conducted to register pipes break and relevant causes in the WDN of Yazd City, Iran. In this way, 851 water pipes were incepted and localized by the Global Positioning System (GPS) apparatus. Then, 1033 failure cases were reported in the eight zones of understudy WDN during March-December 2014. Pipes break rate (BRP) was calculated using the depth of pipe installation (hP), number of failures (NP), the pressure of water pipes in operation (P), and age of pipe (AP). After completing a pipe break database, robust Artificial Intelligence models, namely Multivariate Adaptive Regression Spline (MARS), Gene-Expression Programming (GEP), and M5 Model Tree were employed to extract precise formulation for the pipes break rate estimation. Results of the proposed relationships demonstrated that the MARS model with Coefficient of Correlation (R) of 0.981 and Root Mean Square Error (RMSE) of 0.544 provided more satisfying efficiency than the M5 model (R = 0.888 and RMSE = 1.096). Furthermore, statistical results indicated that MARS and GEP models had comparatively at the same accuracy level. Explicit equations by Artificial Intelligence (AI) models were satisfactorily comparable with those obtained by literature review in terms of various conditions: physical, operational, and environmental factors and complexity of AI models. Through a probabilistic framework for the pipes break rate, the results of first-order reliability analysis indicated that the MARS technique had a highly satisfying performance when MARS-extracted-equation was assigned as a limit state function.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:11:d:10.1007_s11269-021-02911-6
    DOI: 10.1007/s11269-021-02911-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-021-02911-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-021-02911-6?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. 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).
    2. Akbar Shirzad & Massoud Tabesh & Behzad Atayikia, 2017. "Multiobjective Optimization of Pressure Dependent Dynamic Design for Water Distribution Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(9), pages 2561-2578, July.
    3. 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.
    4. 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.
    5. Keshtegar, Behrooz & Kisi, Ozgur, 2018. "RM5Tree: Radial basis M5 model tree for accurate structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 49-61.
    6. L. Berardi & O. Giustolisi, 2021. "Calibration of Design Models for Leakage 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. 35(8), pages 2537-2551, June.
    7. S. Seifollahi-Aghmiuni & Omid Bozorg Haddad & M. Omid & M. Mariño, 2013. "Effects of Pipe Roughness Uncertainty on Water Distribution Network Performance During its Operational Period," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(5), pages 1581-1599, March.
    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. Ram Kailash Prasad, 2021. "Identification of Critical Pipes for Water Distribution Network Rehabilitation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5187-5204, December.
    2. Icen Yoosefdoost & Abbas Khashei-Siuki & Hossein Tabari & Omolbani Mohammadrezapour, 2022. "Runoff Simulation Under Future Climate Change Conditions: Performance Comparison of Data-Mining Algorithms and Conceptual Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1191-1215, March.

    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. 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).
    2. Jara-Arriagada, Carlos & Stoianov, Ivan, 2021. "Pipe breaks and estimating the impact of pressure control in water supply networks," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    3. 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).
    4. 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.
    5. 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).
    6. Xiao, Mi & Zhang, Jinhao & Gao, Liang, 2021. "A Kriging-assisted sampling method for reliability analysis of structures with hybrid uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    7. Roy, Atin & Chakraborty, Subrata, 2022. "Reliability analysis of structures by a three-stage sequential sampling based adaptive support vector regression model," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    8. Wu, Jason & Baker, Jack W., 2020. "Statistical learning techniques for the estimation of lifeline network performance and retrofit selection," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    9. Soheila Beygi & Massoud Tabesh & Shuming Liu, 2019. "Multi-Objective Optimization Model for Design and Operation of Water Transmission Systems Using a Power Resilience Index for Assessing Hydraulic Reliability," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(10), pages 3433-3447, August.
    10. 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).
    11. Alicia Robles-Velasco & Cristóbal Ramos-Salgado & Jesús Muñuzuri & Pablo Cortés, 2021. "Artificial Neural Networks to Forecast Failures in Water Supply Pipes," Sustainability, MDPI, vol. 13(15), pages 1-10, July.
    12. S. Seifollahi-Aghmiuni & O. Bozorg Haddad & M. Mariño, 2013. "Water Distribution Network Risk Analysis Under Simultaneous Consumption and Roughness Uncertainties," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2595-2610, May.
    13. Habib Akbari-Alashti & Omid Bozorg Haddad & Miguel Mariño, 2015. "Evaluation of a Developed Discrete Time-Series Method in Flow Forecasting Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(9), pages 3211-3225, July.
    14. Tornyeviadzi, Hoese Michel & Owusu-Ansah, Emmanuel & Mohammed, Hadi & Seidu, Razak, 2022. "A systematic framework for dynamic nodal vulnerability assessment of water distribution networks based on multilayer networks," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    15. 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).
    16. Zhang, Jinhao & Gao, Liang & Xiao, Mi, 2020. "A composite-projection-outline-based approximation method for system reliability analysis with hybrid uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    17. Dariusz Andraka & Wojciech Kruszyński & Jacek Tyniec & Joanna Gwoździej-Mazur & Bartosz Kaźmierczak, 2023. "Practical Aspects of the Energy Efficiency Evaluation of a Water Distribution Network Using Hydrodynamic Modeling—A Case Study," Energies, MDPI, vol. 16(8), pages 1-17, April.
    18. 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.
    19. 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).
    20. 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).

    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:spr:waterr:v:35:y:2021:i:11:d:10.1007_s11269-021-02911-6. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.