Deterioration Assessment Models for Water Pipelines
The aging and deterioration of water pipelines in cities worldwide result in more frequent water main breaks, water service disruptions, and flooding damage. Therefore, there is an urgent need for undertaking proper maintenance procedures to avoid breaks and disastrous failures. However, due to budget limitations, the maintenance of water pipeline networks needs to be prioritized through efficient deterioration assessment models. Previous studies focused on the development of structural or physical deterioration assessment models, which require expensive inspection data. But, this paper aims at developing deterioration assessment models for water pipelines using statistical techniques. Several deterioration models were developed based on pipeline size, material type, and soil type using linear regression analysis. The categorical nature of some variables affecting pipeline deterioration was considered through developing several categorical models. The developed models were validated with an average validity percentage greater than 95%. Moreover, sensitivity analysis was carried out against different classifications and it displayed higher importance of age of pipes compared to other factors. The developed models will be helpful for the water municipalities and asset managers to assess the condition of their pipes and prioritize them for maintenance and inspection purposes.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1132579Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 806
 American Society of Civil Engineers (2017). “Report Card for America’s Infrastructure”,
 American Water Works Association (AWWA) (2012). “Buried No Longer: Confronting America’s Water Infrastructure Challenge.”,
 Canadian Infrastructure Report Card (2016). “Informing the Future”,
 El Chanati, H., El-Abbasy, M.S., Mosleh F., Senouci, A., Abouhamad, M., Gkountis, I., Zayed, T., and Al-Derham, H. (2015). “Multi-Criteria Decision Making Models for Water Pipelines”, ASCE, Journal of Performance of Constructed Facilities, 30(4), 04015090.
 Giustolisi, O., Laucelli, D., & Dragan, A. S. (2006). "Development of rehabilitation plans for water mains replacement considering risk and cost-benefit assessment." J. Civil Engineering and Environmental Systems, 23(3), 175-190.
 Kleiner, Y., & Rajani, B. (2000). "Considering time-dependent factors in the statistical prediction of water main breaks." Proc. Infrastructure Conference, AWWA, 1-12.
 NRC. (2003). "Deterioration and Inspection of Water Distribution Systems" National guide to sustainable municipal infrastructure, Issue No. 1.1, Ottawa, Ontario, Canada.
 Kleiner, Y., & Rajani, B. (2001). "Comprehensive review of structural deterioration of water mains: Physical models." J. Urban Water, 3(3), 151-164.
 Yan, J.M., and Vairavamoorthy, K. (2003). “Fuzzy Approach for Pipe Condition Assessment”, ASCE, Pipeline Engineering and Construction International Conference, Baltimore, Maryland, USA.
 Geem, Z.W. (2003). “Window-Based Decision Support System for the Water Pipe Condition Assessment using Artificial Neural Network”, ASCE, World Water and Environmental Resources Congress, Philadelphia, Pennsylvania, USA.
 Al-Barqawi, H., and Zayed, T. (2006). “Condition Rating Model for Underground Infrastructure Sustainable Water Mains”, ASCE, Journal of Performance of Constructed Facilities, Vol. 20, No. 2, pp. 126-135.
 Al-Barqawi, H., and Zayed, T. (2006). “Assessment Model of Water Main Conditions”, ASCE, Pipeline Division Specialty Conference, Chicago, Illinois, USA.
 Geem, Z. W., Tseng, C.L., Kim, J., and Bae, C. (2007). “Trenchless Water Pipe Condition Assessment using Artificial Neural Network”, ASCE, International Conference on Pipeline Engineering and Construction, Boston, Massachusetts, USA.
 Al-Barqawi, H., and Zayed, T. (2008). “Infrastructure Management: Integrated AHP/ANN Model to Evaluate Municipal Water Mains’ Performance”, ASCE, Journal of Infrastructure Systems, Vol. 14, No. 4, pp. 305-318.
 Wang, Y., Zayed, T., and Moselhi, O. (2009). “Prediction Models for Annual Break Rates of Water Mains”, ASCE, Journal of Performance of Constructed Facilities, Vol. 23, No. 1, pp. 47-54.
 Zhou, Y., Vairavamoorthy, K., and Grimshaw, F. (2009). “Development of a Fuzzy Based Pipe Condition Assessment Model using PROMETHEE”, ASCE, The 29th World Environmental and Water Resources Congress, Kansas City, Missouri, USA.
 Fares, H., and Zayed, T. (2010). “Hierarchical Fuzzy Expert System for Risk of Failure of Water Mains”, ASCE, Journal of Pipeline Systems Engineering and Practice, Vol. 1, No. 1, pp. 53-62.
 Wang, C.W., Niu, Z.G., Jia, H., and Zhang, H.W. (2010). “An Assessment Model of Water Pipe Condition using Bayesian Inference”, Journal of Zhejiang University Science A, Vol. 11, No. 7, pp. 495-504.
 Clair, A.M.S., and Sinha, S.K. (2011). “Development and the Comparison of a Weighted Factor and Fuzzy Inference Model for Performance Prediction of Metallic Water Pipelines”, ASCE, Proceedings of the Pipelines 2011 Conference, Seattle, Washington, USA.
 Elhag, T. and Wang, Y. (2007). “Risk Assessment for Bridge Maintenance Projects: Neural Networks versus Regression Techniques”, Journal of Computing in Civil Engineering, Volume 21, pp. 402-409.
 Chughtai, F., & Zayed, T. (2009). “Infrastructure Condition Prediction Models for Sustainable Sewer Pipelines.” Journal of performnce of constructed facilities, 333-341.
 El-Abbasy, M., Senouci, A., Zayed, T., Mirahadi, F., and Parvizsedghy, L. (2014). ”Condition Prediction Models for Oil and Gas Pipelines Using Regression Analysis.” J. Constr. Eng. Manage., 140(6), 04014013.
 Levine, D., Stephanm, D., Krehbiel, T., Berenson, M., and Bliss, J. (2002). Statistics for Managers - Using Microsoft Excel, 3rd Edition, Prentice Hall, Upper Saddle River, NJ.
 Kutner, M. H., Nachtsheim, C.J., Neter, J., & Li, W. (2004). Applied Linear Statistical Models. 5th Edition, McGraw-Hill, New York, NY.