Similar articles

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.

[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.

[2]Al-Barqawi, H., Zayed, T., 2006b. Assessment Model of Water Main Conditions. The Pipeline Division Specialty Conference, Chicago, USA.

[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.

[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.

[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.

[6]Arun, K.D., Yakir, J.H., 1995. Distribution System Performance Evaluation. Research Foundation and American Water Works Association, Denver, USA.

[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.

[9]Dingus, M., Haven, J., Austin, R., 2002. Nondestructive Assessment of Underground Pipelines. Research Foundation and American Water Works Association, Denver, USA.

[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.

[11]Ellison, A.M., 2004. Bayesian inference in ecology. Ecology Letters, 7(6):509-520.

[12]Enrique, C.Jr., Miguel, A.P., 2008. Performance Assessment of Urban Infrastructure Services. IWA Publishing, Alliance House, London, UK.

[13]Exeritt, B.S., 2003. The Cambridge Dictionary of Statistics. Cambridge University Press, UK.

[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.

[16]Gelman, A., Hill, J., 2007. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press, New York.

[17]Grigg, N.S., 2004. Assessment and Renewal of Water Distribution System. Research Foundation and American Water Works Association, Denver, USA.

[18]Grigg, N.S., 2005. Assessment and renewal of water distribution system. Journal of American Water Works Association, 97(2):58-70.

[19]Grigg, N.S., 2006. Condition assessment of water distribution pipes. Journal of Infrastructure Systems, 12(3):147-153.

[20]Hudson, W.R., Haas, R., Uddin, W., 1997. Infrastructure Management: Design, Construction, Maintenance, Rehabilitation, and Renovation. McGraw-Hill, New York.

[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.

[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.

[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.

[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.

[25]Koo, D.H., Ariaratnam, S.T., 2006. Innovative method for assessment of underground sewer pipe condition. Automation in Construction, 15(4):479-488.

[26]Makar, J.M., Kleiner, Y., 2000. Maintaining Water Pipeline Integrity. AWWA Infrastructure Conference and Exhibition, Baltimore, USA.

[27]Male, J.W., Walski, T.M., 1990. Water Distribution Systems: A Troubleshooting Manual. Michigan Lewis Publishers, USA.

[28]Malve, O., Qian, S.S., 2006. Estimating nutrients and chlorophyll a relationships in Finnish lakes. Environmental Science & Technology, 40(24):7848-7853.

[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.

[30]Reckhow, K.H., 1994. Importance of scientific uncertainty in decision-making. Environmental Management, 18(2): 161-166.

[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.

[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.

[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]

[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.

[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.

[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.