Advertisement

Journal of Zhejiang University-SCIENCE A

, Volume 11, Issue 7, pp 495–504 | Cite as

An assessment model of water pipe condition using Bayesian inference

  • Chen-wan Wang
  • Zhi-guang Niu
  • Hui Jia
  • Hong-wei Zhang
Article

Abstract

An accurate understanding of the condition of a pipe is important for maintaining acceptable levels of service and providing appropriate strategies for maintenance and rehabilitation in water supply systems. Many factors contribute to pipe deterioration. To consolidate information on these factors to assess the condition of water pipes, this study employed a new approach based on Bayesian configuration against pipe condition to generate factor weights. Ten pipe factors from three pipe materials (cast iron, ductile cast iron and steel) were used in this study. The factors included size, age, inner coating, outer coating, soil condition, bedding condition, trench depth, electrical recharge, the number of road lanes, material, and operational pressure. To address identification problems that arise when switching from pipe factor information to actual pipe condition, informative prior factor weight distribution based on the literature and previous knowledge of water pipe assessment was used. The influence of each factor on the results of pipe assessment was estimated. Results suggested that factors that with smaller weight values or with weights having relative stable posterior means and narrow uncertainty bounds, would have less influence on pipe conditions. The model was the most sensitive to variations of pipe age. Using numerical experiments of different factor combinations, a simplified model, excluding factors such as trench depth, electrical recharge, and the number of road lanes, is provided. The proposed Bayesian inference approach provides a more reliable assessment of pipe deterioration.

Key words

Bayesian inference Condition assessment Pipe factor Water distribution system Weight 

CLC number

TU991 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Al-Barqawi, H., Zayed, T., 2006a. Condition rating model for underground infrastructure sustainable water mains. Journal of Performance of Constructed Facilities, 20(2):126–135. [doi:10.1061/(ASCE)0887-3828(2006)20:2(126)]CrossRefGoogle Scholar
  2. Al-Barqawi, H., Zayed, T., 2006b. Assessment Model of Water Main Conditions. The Pipeline Division Specialty Conference, Chicago, USA. [doi:10.1061/40854(211)27]Google Scholar
  3. Alegre, H., Hirner, W., Baptista, J.M., Parena, R., 2000. Indicators for Water Supply Services. Manual of Best Practice, IWA Publishing, Alliance House, London, UK.Google Scholar
  4. Alegre, H., Baptista, J.M., Cabrera, E.Jr., Cubllo, F., Duarte, P., Hirner, W., Merkel, W., Parena, R., 2006. Performance Indicators for Water Supply Services (2nd Ed.). Manual of Best Practice, IWA Publishing, Alliance House, London, UK.Google Scholar
  5. Alegre, H., Cabrera, E.Jr., Merkel, W., 2009. Performance assessment of urban utilities: the case of water supply, wastewater and solid waste. Journal of Water Supply: Research and Technology, 58(5):305–315. [doi:10.2166/aqua.2009.041]CrossRefGoogle Scholar
  6. Arun, K.D., Yakir, J.H., 1995. Distribution System Performance Evaluation. Research Foundation and American Water Works Association, Denver, USA.Google Scholar
  7. American Society of Civil Engineers, 2009. American’s Infrastructure Report Card. Available from http://www.infrastructurereportcard.org [Accessed on July. 23, 2009].
  8. Bates, J., Gregory, A., 1994. Development of a Pipe Evaluation Model for the Louisville Water Company. Process of AWWA Computer Conference, Denver.Google Scholar
  9. Dingus, M., Haven, J., Austin, R., 2002. Nondestructive Assessment of Underground Pipelines. Research Foundation and American Water Works Association, Denver, USA.Google Scholar
  10. Dodrill, D.M., Edwards, M., 1995. Corrosion control on the basis of utility experience. Journal of American Water Works Association, 87(3):74–85.Google Scholar
  11. Ellison, A.M., 2004. Bayesian inference in ecology. Ecology Letters, 7(6):509–520. [doi:10.1111/j.1461-0248.2004.00603]CrossRefGoogle Scholar
  12. Enrique, C.Jr., Miguel, A.P., 2008. Performance Assessment of Urban Infrastructure Services. IWA Publishing, Alliance House, London, UK.Google Scholar
  13. Exeritt, B.S., 2003. The Cambridge Dictionary of Statistics. Cambridge University Press, UK.Google Scholar
  14. Federation of Canadian Municipalities and National Research Council, 2003. Deterioration and Inspection of Water Distribution Systems. Issue No. 1.1, Ottawa. Available from http://www.sustainablecommunities.fcm.ca/files/infraguide/potable_water/deterior_inspect_water_distrib_syst.pdf
  15. Geem, Z.W., Tseng, C., Kim, J., Bae, C., 2007. Trenchless Water Pipe Condition Assessment Using Artificial Neural Network. The ASCE International Conference on Pipeline Engineering and Construction, Boston, USA. [doi:10.1061/40934(252)26]Google Scholar
  16. Gelman, A., Hill, J., 2007. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press, New York.Google Scholar
  17. Grigg, N.S., 2004. Assessment and Renewal of Water Distribution System. Research Foundation and American Water Works Association, Denver, USA.Google Scholar
  18. Grigg, N.S., 2005. Assessment and renewal of water distribution system. Journal of American Water Works Association, 97(2):58–70.Google Scholar
  19. Grigg, N.S., 2006. Condition assessment of water distribution pipes. Journal of Infrastructure Systems, 12(3):147–153. [doi:10.1061/(ASCE)1076-0342(2006)12:3(147)]MathSciNetCrossRefGoogle Scholar
  20. Hudson, W.R., Haas, R., Uddin, W., 1997. Infrastructure Management: Design, Construction, Maintenance, Rehabilitation, and Renovation. McGraw-Hill, New York.Google Scholar
  21. Kettler, A.J., Goulter, I.C., 1985. Analysis of pipe breakage in urban water distribution networks. Canadian Journal of Civil Engineering, 12(2):286–293. [doi:10.1139/l85-030]CrossRefGoogle Scholar
  22. Kim, J., Bae, C., Woo, H., 2007. Assessment of Residual Tensile Strength on Cast Iron Pipes. The ASCE International Conference on Pipeline Engineering and Construction, Boston, USA. [doi:10.1061/40934(252)62]Google Scholar
  23. Kirmeyer, G.J., Richards, W., Smith, C.D., 1994. An Assessment of Water Distribution Systems and Associated Research Needs. Research Foundation and American Water Works Association, Denver, USA.Google Scholar
  24. Kleiner, Y., Adams, B.J., Rogers, J.S., 2001. Water distribution network renewal planning. Journal of Computing in Civil Engineering, 15(1):15–26. [doi:10.1061/(ASCE)0887-3801(2001)15:1(15)]CrossRefGoogle Scholar
  25. Koo, D.H., Ariaratnam, S.T., 2006. Innovative method for assessment of underground sewer pipe condition. Automation in Construction, 15(4):479–488. [doi:10.1016/j.autcon.2005.06.007]CrossRefGoogle Scholar
  26. Makar, J.M., Kleiner, Y., 2000. Maintaining Water Pipeline Integrity. AWWA Infrastructure Conference and Exhibition, Baltimore, USA.Google Scholar
  27. Male, J.W., Walski, T.M., 1990. Water Distribution Systems: A Troubleshooting Manual. Michigan Lewis Publishers, USA.Google Scholar
  28. Malve, O., Qian, S.S., 2006. Estimating nutrients and chlorophyll a relationships in Finnish lakes. Environmental Science & Technology, 40(24):7848–7853. [doi:10.1021/es061359b]CrossRefGoogle Scholar
  29. O’Day, D.K., 1982. Organizing and analyzing leak and break data for making main replacement decision. Journal of the American Water Works Association, 74(11):589–594.Google Scholar
  30. Reckhow, K.H., 1994. Importance of scientific uncertainty in decision-making. Environmental Management, 18(2): 161–166. [doi:10.1007/BF02393758]CrossRefGoogle Scholar
  31. Rogers, P.D., Grigg, N.S., 2009. Failure assessment modeling to prioritize water pipe renewal: two case studies. Journal of Infrastructure Systems, 15(3):162–171. [doi:10.1061/(ASCE)1076-0342(2009)15:3(162)]CrossRefGoogle Scholar
  32. Spiegelhalte, D.J., Best, N.G., Carlin, B.P., van der Linde, A., 2002. A Bayesian measures of model complexity and fit. Journal of Royal Statistical Society (Series B), 64(4):583–639. [doi:10.1111/1467-9868.00353]MathSciNetCrossRefMATHGoogle Scholar
  33. Stow, C.A., Scavia, D., 2009. Modeling hypoxia in the Chesapeake Bay: ensemble estimation using a Bayesian hierarchical model. Journal of Marine Systems, 76(1–2): 244–250. [doi:10.1016/j.jmarsys.2008.05.008]CrossRefGoogle Scholar
  34. Watson, T.G., Christian, C.D., Mason, A.J., Smith, M.H., Myers, R., 2004. Baysian-based pipe failure model. Journal of Hydroinformatics, 06(4):259–264.Google Scholar
  35. Yamini, H., Lence, B.J., 2006. Probability Failure Analysis Due to Internal Corrosion in Cast Iron Pipes. 8th Annual Water Distribution Systems Analysis Symposium, Ohio, USA, p.27–37. [doi:10.1061/40941(247)27]Google Scholar
  36. Yan, J.M., Vairavamoorthy, K., 2003. Fuzzy Approach for Pipe Condition Assessment. Proceedings of the ASCE International Conference on Pipeline Engineering and Construction, Baltimore, USA, p.466–476. [doi:10.1061/40690(2003)11]Google Scholar

Copyright information

© Zhejiang University and Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Chen-wan Wang
    • 1
  • Zhi-guang Niu
    • 1
  • Hui Jia
    • 2
  • Hong-wei Zhang
    • 1
  1. 1.School of Environment Science and TechnologyTianjin UniversityTianjinChina
  2. 2.School of Environment and Chemical EngineeringTianjin Polytechnic UniversityTianjinChina

Personalised recommendations