Condition Prediction for Cured-in-Place Pipe Rehabilitation of Sewer Mains
Publication: Journal of Performance of Constructed Facilities
Volume 30, Issue 5
Abstract
Authorities in Quebec, Canada, have been making considerable efforts to improve sewer networks across the province by using up-to-date technologies. To rehabilitate its sewer mains, Quebec has used several rehabilitation methods including slip lining, cement and epoxy lining, and cured-in-place pipe (CIPP).These replacement and rehabilitation techniques have been developed over the last four decades, with many arbitrary declarations made about the efficiency and performance of different rehabilitation techniques. This paper presents condition prediction models for CIPP rehabilitation of sewer mains. Regression analysis technique is used to develop these models, based on gathered and analyzed closed-circuit television (CCTV) inspection reports for Quebec CIPP rehabilitations. The models can predict the structural and operational conditions of CIPP rehabilitation on the basis of basic input such as pipe material, and rehabilitation type and date. They can also generate curves illustrating condition deterioration over time with respect to governing factors. A data set was randomly selected and put aside for validating the developed models. Models validation was based on the value of the coefficient of multiple determination () ranging between 94 and 99%. The developed models are expected to be used by municipalities and contractors to forecast the condition of rehabilitated pipelines, plan inspections, and make informed budget allocation decisions.
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References
Al-Barqawi, H., and Zayed, T. (2006a). “Assessment model of water main conditions.” Proc., Pipeline 2006: Division Specialty Conf., ASCE, Chicago, 1–8.
Al-Barqawi, H., and Zayed, T. (2006b). “Condition rating model for underground infrastructure sustainable water mains.” J. Perform. Constr. Facil., 126–135.
Al-Barqawi, H., and Zayed, T. (2008). “Infrastructure management: Integrated AHP-ANN model to evaluate municipal water mains’ performance.” J. Infrastruct. Syst., 305–318.
Allison, P. (1999). Multiple regression: A primer, Pine Forge Press, Thousand Oaks, CA.
Alzraiee, H., Bakry, I., and Zayed, T. (2014). “Destructive analysis-based testing for cured-in-place pipe.” J. Perform. Constr. Facil., 04014095.
Angkasuwansiri, T., and Sinha, S. K. (2014). “Development of a robust wastewater pipe performance index.” J. Perform. Constr. Facil., 04014042.
Araujo, T., Sabeshan, S., and Yao, B. (2009). “Factors affecting the quality of flexural properties from CIPP field samples.” Proc., Int. No-Dig, NASTT/ISTT Conf., Toronto.
Asnaashari, A., McBean, E. A., Gharabaghi, B., and Tutt, D. (2013). “Forecasting watermain failure using artificial neural network modelling.” Can. Water Resour. J., 38(1), 24–33.
Bartlett, J. E., II, Kotrlik, J. W., Higgins, C. (2001). “Organizational research: Determining appropriate sample size in survey research.” Inf. Technol. Learn. Perform. J., 19(1), 43–50.
Chughtai, F., and Zayed, T. (2007a). “Infrastructure condition prediction models for sustainable sewer pipelines.” J. Perform. Constr. Facil., 333–341.
Chughtai, F., and Zayed, T. (2007b). “Sewer pipeline operational condition prediction using multiple regression.” Proc., ASCE Int. Pipeline Conf., ASCE, Reston, VA.
Chughtai, F., and Zayed, T. (2007c). “Structural condition models for sewer pipeline.” Proc., ASCE Int. Pipeline Conf., ASCE, Reston, VA.
El-Abbasy, M. S., 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., 04014013.
Félio, G., et al. (2012). “The Canadian infrastructure report card.” CCA, CPWA, CSCE, FCM, 〈http://www.canadainfrastructure.ca/en/index.html〉 (Mar. 2013).
Islam, M. M., Ali, A., and Purtell, J. (2009). “Enhanced condition assessment methodologies of buried infrastructure.” Proc., Pipelines 2009: Infrastructure’s Hidden Assets, ASCE, Reston, VA.
Kleiner, Y., and Rajani, B. (2001). “Comprehensive review of structural deterioration of water mains: Statistical models.” J. Urban Water, 3(3), 131–150.
Najafi, M., and Kulandaivel, G. (2005). “Pipeline condition prediction using neural network models.” Proc., ASCE Int. Pipeline Conf., ASCE, Houston.
Nishiyama, M., and Filion, Y. (2013). “Review of statistical water main break prediction models.” Can. J. Civ. Eng., 40(10), 972–979.
Osman, H., and Bainbridge, K. (2010). “Comparison of statistical deterioration models for water distribution networks.” J. Perform. Constr. Facil., 259–266.
Shelton, J. (2012). “Cured in place liner defects—Three studies of installed liner performance quality.” Proc., 30th Int. No-Dig Conf., International Society for Trenchless Technology (ISTT), London.
SPSS version 19.0 [Computer software]. IBM, Armonk, NY.
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Published In
Journal of Performance of Constructed Facilities
Volume 30 • Issue 5 • October 2016
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Apr 22, 2015
Accepted: Nov 16, 2015
Published online: Feb 1, 2016
Discussion open until: Jul 1, 2016
Published in print: Oct 1, 2016
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