Skip to main content
SpringerLink
Log in

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

  • Published:
Water Resources Management Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data Availability

The data are not publicly available due to restrictions such their containing information that.

References

  • Aydogdu M, Firat M (2015) Estimation of Failure Rate in Water Distribution Network Using Fuzzy Clustering and LS-SVM Methods. Water Resour Manage 29:1575–1590

    Article  Google Scholar 

  • Berardi L, Kapelan Z, Giustolisi O, Savic DA (2008) Development of pipe deterioration models for water distribution systems using EPR. J Hydroinf 10(2):113–126

    Article  Google Scholar 

  • Berardi L, Giustolisi O (2021) Calibration of Design Models for Leakage Management of Water Distribution Networks. Water Resour Manage 35:2537–2551

    Article  Google Scholar 

  • Clair AMS, Sinha S (2012) State-of-the-technology review on water pipe condition, deterioration and failure rate prediction models! Urban Water Journal 9(2):85–112

    Article  Google Scholar 

  • Clark RM, Stafford CL, Goodrich JA (1982) Water distribution systems: A spatial and cost evaluation. J Water Resour Plng Mgmt Div 108(3):243–256

  • Friedman JH (1991) Multivariate Adaptive Regression Splines. Ann Stat 19(1):1–67

    Google Scholar 

  • Ferreira C (2006) Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence. Springer

    Book  Google Scholar 

  • Homaei F, Najafzadeh MA (2020) Reliability-based probabilistic evaluation of the wave-induced scour depth around marine structure piles. Ocean Eng 19:106818

  • Jowitt PW, Xu C (1993) Predicting Pipe Failure Effects in Water Distribution Networks. J Water Resour Plann Manage 119(1):18–31

    Article  Google Scholar 

  • Keshtkar B, Kisi O (2018) RM5Tree: Radial basis M5 model tree for accurate structural reliability analysis. Reliab Eng Syst Saf 180:49–61

    Article  Google Scholar 

  • Kettler AJ, Goulter IC (1985) An analysis of pipe breakage in urban water distribution networks. Can J Civ Eng 12:286–293

  • Moslehi I, Jalili-Ghazizadeh M (2020) Pressure-Pipe Breaks Relationship in Water Distribution Networks: A Statistical Analysis. Water Resour Manage 34:2851–2868

    Article  Google Scholar 

  • Quinlan JR (1992) Learning with Continuous Classes In Proceedings of AI’92 (Adams & Sterling, Eds), Singapore. World Sci 343–348

  • Rezaie-Balf M, Fani-Nowbandegani S, Samadi SZ, Fallah H, Alaghmand S (2019) An Ensemble Decomposition-Based Artificial Intelligence Approach for Daily Streamflow Prediction. Water 11:709

    Article  Google Scholar 

  • Robles-Velasco, Cortes P, Munuzuri J, Oniev L (2020) Prediction of pipe failures in water supply networks using logistic regression and support vector classification. Reliab Eng Syst Saf 196:106754

  • Rezaei H, Ryanb B, Stoianovc I (2015) Pipe failure analysis and impact of dynamic hydraulic conditions in water supply networks. 13th Com Control Water Industry Confer, CCWI 2015. Leicester, UK

  • Silinis PG, Franks SW (2007) Understanding failure rates in cast iron pipes using temporal stratification. Urban Water Journal 4(1):1–7

    Article  Google Scholar 

  • Shirzad A, Tabesh M, Atayikia B (2017) Multiobjective Optimization of Pressure Dependent Dynamic Design for Water Distribution Networks. Water Resour Manage 31:2561–2578

    Article  Google Scholar 

  • Savic DA, Giustolisi O, Berardi L, Shepherd W, Djordjevic S, Saul A (2006) Modelling sewers failure using evolutionary computing. Pro ICE Wat Manage 159(2):111–118

  • Seifollahi-Aghmiuni S, Bozorg Haddad O, Omid MH, Marino MA (2013) Effects of Pipe Roughness Uncertainty on Water Distribution Network Performance During its Operational Period. Water Resour Manage 27:1581–1599

    Article  Google Scholar 

  • Shi WZ, Zhang AS, Ho OK (2013) Spatial analysis of water mains failure clusters and factors: a Hong Kong case study. Ann GIS 19(2):89–97

    Article  Google Scholar 

  • Shende S, Chau W-W (2019) Design of water distribution systems using an intelligent simple benchmarking algorithm with respect to cost optimization and computational efficiency. Water Supply 19(7):1892–1898

    Article  Google Scholar 

  • Stephens M, Gong J, Zhang C, Marchi A, Dix L, Lambert MF (2020) Leak-Before-Break Main Failure Prevention for Water Distribution Pipes Using Acoustic Smart Water Technologies: Case Study in Adelaide. J Water Resour Plann Manage 146(10):05020020

    Article  Google Scholar 

  • Sattari MT, Falsafian K, Irvem A, Qasem SSSN (2020) Potential of kernel and tree-based machine-learning models for estimating missing data of rainfall. Eng Appl Comput Fluid Mech 14(1):1078–1094

    Google Scholar 

  • Shamshirband S, Hashemi S, Salimi H, Samadianfard S, Asadi E, Shadkani S, Kargar K, Mosavi A, Nabipour N, Chau K-W (2020) Predicting Standardized Streamflow Index for Hydrological Drought Using Machine Learning Models. Eng Appl Comput Fluid Mech 14(1):339–350

    Google Scholar 

  • Sarıdemir M (2010) Genetic programming approach for prediction of compressive strength of concretes containing rice husk ash. Constr Build Mater 24:1911–1919

    Article  Google Scholar 

  • Tang K, Parsons DJ, Jude S (2019) Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system. Reliab Eng Syst Saf 186:24–36

    Article  Google Scholar 

  • Taormina R, Chau W-W (2015) ANN-based interval forecasting of streamflow discharges using the LUBE method and MOFIPS. Eng Appl Artif Intell 45:429–440

    Article  Google Scholar 

  • Wang Y, Zayed T, Moselhi O (2009) Prediction models for annual break rates of water mains. J Perform Constr Facil 23(1):47–54

    Article  Google Scholar 

  • Wols B, Moerman A, Horst P, Laarhoven KV (2018) Prediction of Pipe Failure in Drinking Water Distribution Networks by Comsima. Proceedings 2(11):589. https://doi.org/10.3390/proceedings2110589

  • Xu Q, Chen Q, Li W (2011a) Application of genetic programming to modeling pipe failures in water distribution systems. J Hydroinf 13(3):419–428

    Article  Google Scholar 

  • Xu Q, Chen Q, Li W, Ma J (2011b) Pipe break prediction based on evolutionary data-driven methods with brief recorded data. Reliab Eng Syst Saf 96(8):942–948

    Article  Google Scholar 

  • Xu Q, Chen Q, Ma J, Blanckaert K (2013) Optimal pipe replacement strategy based on break rate prediction through genetic programming for water distribution network. J Hydro-Environ Res 7(2):134–140

    Article  Google Scholar 

  • Yazdekhasti S, Vladeanu G, Daly C (2020) Evaluation of artificial intelligence tool performance for predicting water pipe failures. Pipelines Conference, August 9–12, San Antonio, Texas

  • Yilmaz B, Egemen Aras E, Nacar S, Kankal M (2018) Estimating suspended sediment load with multivariate adaptive regression spline, teaching-learning based optimization, and artificial bee colony models. Sci Total Environ 639:826–840

    Article  Google Scholar 

  • Zampieri P, Zanini MA, Faleschini F (2016) Influence of damage on the seismic failure analysis of masonry arches. Constr Build Mater 119:343–355

    Article  Google Scholar 

Download references

Funding

No funds, grants, or other supports were received.

Author information

Authors and Affiliations

  1. Department of Water Engineering, Faculty of Civil and Surveying Engineering, Graduate University of Advanced Technology, ​P.O.Box 7631885356, Kerman, Iran

    Yaser Amiri-Ardakani & Mohammad Najafzadeh

Authors
  1. Yaser Amiri-Ardakani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Najafzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Yaser Amiri-Ardakani; Performing the field study and collecting the data; Mohammad. Najafzadeh; Formal analysis and investigation, Writing—original draft preparation, Writing -review and editing, Resources, Supervision.

Corresponding author

Correspondence to Mohammad Najafzadeh.

Ethics declarations

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964. Helsinki declaration and its later amendments or comparable ethical standards.

Consent to Participate

Informed consent was obtained from all individual participants included in the study.

Consent to Publish

All the authors give the Publisher the permission of the authors to publish the research work.

Competing Interests

There is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 1676 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Amiri-Ardakani, Y., Najafzadeh, M. Pipe Break Rate Assessment While Considering Physical and Operational Factors: A Methodology based on Global Positioning System and Data-Driven Techniques. Water Resour Manage 35, 3703–3720 (2021). https://doi.org/10.1007/s11269-021-02911-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-021-02911-6

Keywords

Search

Navigation