Assessment of Remaining Useful Life of Pipelines Using Different Artificial Neural Networks Models
Publication: Journal of Performance of Constructed Facilities
Volume 30, Issue 5
Abstract
Water distribution networks have a significant effect on public health and safety. Recent reports state that the 21st century is estimated to be the end of effective life for most water distribution networks in the United States. It is essential to implement accurate and cost-effective models that can predict deterioration rates along with estimates of remaining useful life (RUL) of the pipelines, to perform necessary intervention plans that can prevent disastrous failures. This study presents a computational model that predicts the RUL of water pipelines using an artificial neural network (ANN) model that has been developed using the Levenberg-Marquardt backpropagation algorithm. The model is implemented, tested, and trained using data collected from the city of Montreal. Results show that pipeline age, condition, length, diameter, material, and breakage rate are the most important factors in the prediction of RUL. Because the model shows robustness and accuracy in estimating the RUL of water pipelines in the case study, it can be used to support the municipality of Montréal, Quebec, Canada, in future planning.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This publication was made possible by NPRP Grant No. 5-165-2-055 from the Qatar National Research Fund. The statements made in this paper are solely the responsibility of the author.
References
Achim, D., Ghotb, F., and McManus, K. J. (2007). “Prediction of water pipe asset life using neural networks.” J. Infrastruct. Syst., 26–30.
Al-Barqawi, H., and Zayed, T. (2006). “Condition rating model for underground infrastructure sustainable water mains.” J. Perform. Constr. Facil., 126–135.
Amaitik, N. M., and Amaitik, S. M. (2008). “Development of PCCP wire breaks prediction model using artificial neural networks.” Proc., Int. Pipelines Conf., ASCE, Atlanta, 1–11.
ASCE. (2013). Report card for America’s infrastructure, 〈www.infrastructurereportcard.org〉 (Sep. 11, 2015).
Boudreau, S., and Brynildsen, D. (2003). “National guide to sustainable municipal infrastructure.” Professional Engineers and Geoscientist of BC-Newsletter of the Municipal Engineers Division, 6(1), 1–2.
Burn, S., Davis, P., and Gould, S. (2009). Risk analysis for pipeline assets: The use of models for failure prediction in plastics pipelines, CSIRO Land and Water, Highett, Australia.
Christodoulou, S., Aslani, P., and Vanrenterghem, A. (2003) “A risk analysis framework for evaluating structural degradation of water mains in urban settings, using neurofuzzy systems and statistical modeling techniques.” World Water & Environmental Resources Congress, ASCE, Philadelphia, 1–9.
Clair, A. M., and Sinha, S. (2012). “State-of-the-technology review on water pipe condition, deterioration and failure rate prediction models.” Urban Water J., 9(2), 85–112.
Davis, P., Burn, S., Cardy, M., Gould, S., Sadler, P., and Tjandraatmadja, G. (2007). Long-term performance prediction for PE pipes, AWWA Research Foundation, Denver.
Davis, P., Burn, S., and Gould, S. (2008a). “Fracture prediction in tough polyethylene pipes using measured craze strength.” Polym. Eng. Sci., 48(5), 843–852.
Davis, P., De Silva, D., Marlow, D., Moglia, M., Gould, S., and Burn, S. (2008b). “Failure prediction and optimal scheduling of replacements in asbestos cement water pipes.” Aqua J. Water Supply, 57(4), 239–252.
Davis, P., and Marlow, D. (2008). “Asset management: Quantifying economic lifetime of large-diameter pipelines.” J. Am. Water Works Assoc., 100(7), 110–119.
Fares, H. A. (2008). “Evaluating the risk of water main failure using a hierarchical fuzzy expert system.” Ph.D. dissertation, Concordia Univ., Montreal.
Farshad, M. (2004). “Two new criteria for the service life prediction of plastics pipes.” Polym. Test., 23(8), 967–972.
Geem, Z., Tseng, C., Kim, J., and Bae, C. (2007) “Trenchless water pipe condition assessment using artificial neural network.” Pipelines, ASCE, Reston, VA, 1–9.
Jia, C., Wei, L., Wang, H., and Yang, J. (2015). “A hybrid model based on wavelet decomposition-reconstruction in track irregularity state forecasting.” Math. Prob. Eng., 2015, 13.
Kettler, A. J., and Goulter, I. C. (1985). “An analysis of pipe breakage in urban water distribution networks.” Can. J. Civ. Eng., 12(2), 286–293.
Lawrence, J. (1994). Introduction to neural networks: Design, theory and applications, California Scientific Software Press, Nevada City, CA.
Lu, J. P., Davis, P., and Burn, L. S. (2003). “Lifetime prediction for ABS pipes subjected to combined pressure and deflection loading.” Polym. Eng. Sci., 43(2), 444–462.
MATLAB [Computer software]. MathWorks, Natick, MA.
Nazari, A., Rajeev, P., and Sanjayan, J. G. (2015a). “Modelling of upheaval buckling of offshore pipeline buried in clay soil using genetic programming.” Eng. Struct., 101, 306–317.
Nazari, A., Rajeev, P., and Sanjayan, J. G. (2015b). “Offshore pipeline performance evaluation by different artificial neural networks approaches.” Measurement, 76, 117–128.
Park, S. W., and Loganathan, G. V. (2002). “Methodology for economically optimal replacement of pipes in water distribution systems: 1: Theory.” KSCE J. Civ. Eng., 6(4), 539–543.
Rajani, B., and Makar, J. (2000). “A methodology to estimate remaining service life of grey cast iron water mains.” Can. J. Civ. Eng., 27(6), 1259–1272.
Rajani, B., and Tesfamariam, S. (2007). “Estimating time to failure of cast-iron water mains.” Proc. ICE-Water Manage., 160(2), 83–88.
Røstum, J. (2000). “Statistical modelling of pipe failures in water networks.” Ph.D. dissertation, Norwegian Univ. of Science and Technology, Trondheim, Norway.
Wang, C. W., Niu, Z. G., Jia, H., and Zhang, H. W. (2010). “An assessment model of water pipe condition using Bayesian inference.” J. Zhejiang Univ. Sci. A, 11(7), 495–504.
Wang, Y., Zayed, T., and Moselhi, O. (2009). “Prediction models for annual break rates of water mains.” J. Perform. Constr. Facil., 47–54.
Zangenehmadar, Z., and Moselhi, O. (2015). “Application of FAHP and shannon entropy in evaluating criteria significance in pipeline deterioration.” Int. Construction Specialty Conf., Vancouver, BC, Canada.
Information & Authors
Information
Published In
Journal of Performance of Constructed Facilities
Volume 30 • Issue 5 • October 2016
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Nov 3, 2015
Accepted: Jan 5, 2016
Published online: Mar 21, 2016
Discussion open until: Aug 21, 2016
Published in print: Oct 1, 2016
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.