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Structure and Infrastructure Engineering
Maintenance, Management, Life-Cycle Design and Performance
Volume 15, 2019 - Issue 7
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Original Articles

Simulation-based deterioration patterns of water pipelines

, , , , ORCID Icon &
Pages 965-982 | Received 18 Sep 2018, Accepted 28 Dec 2018, Published online: 12 Apr 2019

References

  • Achim, D., Ghotb, F., & McManus, K. J. (2007). Prediction of water pipe asset life using neural networks. Journal of Infrastructure Systems, 13(1), 26–30. doi: 10.1061/(ASCE)1076-0342(2007)13:1(26)
  • Adams, W. (2001). Creative decisions foundation. Retrieved from http://www.creativedecisions.net/papers/papers_etc/calc-white-paper.pdf (May 2013).
  • Al-Barqawi, H., & Zayed, T. (2008). Infrastructure management: Integrated AHP/ANN model to evaluate municipal water mains’ performance. Journal of Infrastructure Systems, 14(4), 305–318. doi: 10.1061/(ASCE)1076-0342(2008)14:4(305)
  • Al-Barqawi, H., & Zayed, T. (2006). 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)
  • Al-Barqawi, H., & Zayed, T. (2006b). Assessment model of water main conditions. In ASCE, Pipeline Division Specialty Conference, Chicago, IL.
  • American Society of Civil Engineers (2017). Report card for America’s infrastructure. Retrieved from http://www.infrastructurereportcard.org/ (June 2018).
  • Berardi, L., Giustolisi, O., Kapelan, Z., & Savic, D. A. (2008). Development of pipe deterioration models for water distribution systems using EPR. Journal of Hydroinformatics, 10(2), 113–126. doi: 10.2166/hydro.2008.012
  • Canadian Infrastructure Report Card (2016). Potable water. Retrieved from http://canadianinfrastructure.ca/en/drinking-water.html (June 2018).
  • Clair, A. M. S., & Sinha, S. K. (2011). Development and the comparison of a weighted factor and fuzzy inference model for performance prediction of metallic water pipelines. In ASCE, Proceedings of the Pipelines 2011 Conference, Seattle, WA.
  • Dikmen, I., Birgonul, M., & Kiziltas, S. (2005). Prediction of organizational effectiveness in construction companies. Journal of Construction Engineering and Management, 131(2), 252–261. doi: 10.1061/(ASCE)0733-9364(2005)131:2(252)
  • Economou, T., Kapelan, Z., & Bailey, T. C. (2007). An aggregated hierarchical Bayesian model for the prediction of pipe failures. In Proceedings of 9th International Conference on Computing and Control for the Water Industry (CCWI), Leicester, UK.
  • EPA (2013a). Drinking water infrastructure needs survey and assessment. Fifth Report to Congress. Retrieved from https://www.epa.gov/sites/production/files/2015-07/documents/epa816r13006.pdf (December 2013).
  • EPA (2013b). Primer on condition curves for water mains. Final Report. Retrieved from https://nepis.epa.gov/Adobe/PDF/P100H8W0.pdf (December 2018).
  • Etaati, L., Sadi-Nezhad, S., & Moghadam-Abyaneh, P. M. (2011). Fuzzy analytical network process: An overview on methods. American Journal of Scientific Research, 41, 101–114.
  • Fares, H., & Zayed, T. (2010). Hierarchical fuzzy expert system for risk of failure of water mains. ASCE Journal of Pipeline Systems Engineering and Practice, 1(1), 53–62. doi: 10.1061/(ASCE)PS.1949-1204.0000037
  • Federation of Canadian Municipalities and National Research Council (2003). Deterioration and inspection of water distribution systems. National Guide to Sustainable Municipal Infrastructure, Issue No. 1.1, Ottawa, ON, Canada.
  • Francis, R. A., Guikema, S. D., & Henneman, L. (2014). Bayesian belief networks for predicting drinking water distribution system pipe breaks. Reliability Engineering and System Safety, 130, 1–11. doi: 10.1016/j.ress.2014.04.024
  • Geem, Z. W. (2003). Window-based decision support system for the water pipe condition assessment using artificial neural network. In ASCE World Water and Environmental Resources Congress, Philadelphia, PA.
  • Geem, Z. W., Tseng, C. L., Kim, J., & Bae, C. (2007). Trenchless water pipe condition assessment using artificial neural network. ASCE International Conference on Pipeline Engineering and Construction, Boston, MA.
  • Giustolisi, O., Laucelli, D., & Savic, D. A. (2006). Development of rehabilitation plans for water mains replacement considering risk and cost-benefit assessment. Civil Engineering and Environmental Systems, 23(3), 175–190. doi: 10.1080/10286600600789375
  • Huang, J. J. (2012). A mathematical programming model for the fuzzy analytic network process—Applications of international investment. Journal of the Operational Research Society, 63(11), 1534–1544. doi: 10.1057/jors.2011.164
  • Kabir, G., Demissie, G., Sadiq, R., & Tesfamariam, S. (2015). Integrating failure prediction models for water mains: Bayesian belief network based data fusion. Knowledge-Based Systems, 85, 159–169. doi: 10.1016/j.knosys.2015.05.002
  • Kilinç, Y., Özdemir, Ö., Orhan, C., & Firat, M. (2018). Evaluation of technical performance of pipes in water distribution systems by analytic hierarchy process. Sustainable Cities and Society, 42, 13–21. doi: 10.1016/j.scs.2018.06.035
  • Kimutai, E., Betrie, G., Brander, R., Sadiq, R., & Tesfamariam, S. (2015). Comparison of statistical models for predicting pipe failures: Illustrative example with the city of Calgary water main failure. ASCE Journal of Pipeline Systems Engineering and Practice, 6(4), 1–11. doi: 10.1061/(ASCE)PS.1949-1204.0000196
  • Kleiner, Y., & Rajani, B. (2000). Considering time-dependent factors in the statistical prediction of water main breaks. In Proceedings of American Water Works Association Infrastructure Conference, Baltimore, MA.
  • Kleiner, Y., Sadiq, R., & Rajani, B. (2004). Modeling failure risk in buried pipes using fuzzy markov deterioration process. In ASCE International Conference on Pipe Engineering and Construction, San Diego, CA.
  • Lee, Y. H. (2012). A fuzzy analytic network process approach to determining prospective competitive strategy in China: A case study for multinational biotech pharmaceutical enterprises. Journal of Business Economics and Management, 13(1), 5–28. doi: 10.3846/16111699.2011.620165
  • Martins, A., Leitão, J. P., & Amado, C. (2013). Comparative study of three stochastic models for prediction of pipe failures in water supply systems. ASCE Journal of Infrastructure Systems, 19(4), 442–450. doi: 10.1061/(ASCE)IS.1943-555X.0000154
  • Moeinzadeh, P., & Hajfathaliha, A. (2009). A combined fuzzy decision making approach to supply chain risk assessment. World Academy of Science, Engineering and Technology, 60, 519–535.
  • Najafi, M., & Kulandaivel, G. (2005). Pipeline condition predicting using neural network models. In ASCE Pipelines 2005: Optimizing Pipeline Design, Operations, and Maintenance in Today’s Economy, New York, NY.
  • Rajani, B., & Kleiner, Y. (2001). Comprehensive review of structural deterioration of water mains: Physically based models. Urban Water, 3(3), 151–164. doi: 10.1016/S1462-0758(01)00032-2
  • Saaty, T.L. (1990). How to make a decision: The analytic hierarchy process.. European Journal of Operational Research, 48(1), 9–26.
  • Raychaudhuri, S. (2008). Introduction to Monte Carlo simulation. In IEEE, Simulation Conference, Austin, TX.
  • Wang, C. W., Niu, Z. G., Jia, H., & Zhang, H. W. (2010). An assessment model of water pipe condition using Bayesian inference. Journal of Zhejiang University-Science A, 11(7), 495–504. doi: 10.1631/jzus.A0900628
  • Wang, Y., Zayed, T., & Moselhi, O. (2009). Prediction models for annual break rates of water mains. ASCE Journal of Performance of Constructed Facilities, 23(1), 47–54. doi: 10.1061/(ASCE)0887-3828(2009)23:1(47)
  • Watson, T. G., Christian, C. D., Mason, A. J., Smith, M. H., & Meyer, R. (2004). Bayesian-based pipe failure model. Journal of Hydroinformatics, 6(4), 259–264. doi: 10.2166/hydro.2004.0019
  • Winkler, D., Haltmeier, M., Kleidorfer, M., Rauch, W., & Tscheikner-Gratl, F. (2018). Pipe failure modelling for water distribution networks using boosted decision trees. Structure and Infrastructure Engineering, 14(10), 1402–1411. doi: 10.1080/15732479.2018.1443145
  • Xu, Q., Chen, Q., Li, W., & Ma, J. (2011). Pipe break prediction based on evolutionary data-driven methods with brief recorded data. Reliability Engineering and System Safety, 96(8), 942–948. doi: 10.1016/j.ress.2011.03.010
  • Yan, J. M., & Vairavamoorthy, K. (2003). Fuzzy approach for pipe condition assessment. In ASCE Pipeline Engineering and Construction International Conference, Baltimore, MD.
  • Yang, W. Z., Ge, Y. H., He, J. J., & Liu, B. (2010). Designing a group decision support system under uncertainty using group fuzzy analytic network process (ANP). African Journal of Business Management, 4(12), 2571–2585.
  • Zayed, T., & Halpin, D. (2005). Deterministic models for assessing prod. and cost of bored piles. Journal of Construction Management and Economics, 23(5), 531–543. doi: 10.1080/01446190500039911
  • Zhou, Y., Vairavamoorthy, K., & Grimshaw, F. (2009). Development of a fuzzy based pipe condition assessment model using PROMETHEE. In ASCE 29th World Environmental and Water Resources Congress, Kansas City, MI.

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