Measurement system design for leak detection in hydraulic pressurized networks

References

• Adam, B., & Smith, I. F. C. (2007). Tensegrity active control: Multiobjective approach. Journal of Computing in Civil Engineering, 21, 3–10.10.1061/(ASCE)0887-3801(2007)21:1(3)
   Web of Science ®Google Scholar
• Behzadian, M., Kazemzadeh, R. B., Albadvi, A., & Aghdasi, M. (2010). PROMETHEE: A comprehensive literature review on methodologies and applications. European Journal of Operational Research, 200, 198–215.10.1016/j.ejor.2009.01.021
   Web of Science ®Google Scholar
• Brans, J.-P. (1982). L’ingénierie de la decision: Elaboration d’instruments d’aide à la decision. La méthode PROMETHEE [The engineering of decision: Development of support tools for decision. The PROMETHEE method]. In R. Nadeau & M. Landry (Eds.), Laide a la decision: Nature, instruments et perspectives davenir, (pp. 183–213). Québec: Presses de l’Universite Laval.
   Google Scholar
• Brans, J.-P., & Mareschal, B. (2005). PROMETHEE methods. In S. Greco (Ed.), Multiple criteria decision analysis: State of the art surveys (pp. 163–186). New York, NY: Springer.
   Google Scholar
• Brans, J.-P., Vincke, P., & Mareschal, B. (1986). How to select and how to rank projects: The Promethee method. European Journal of Operational Research, 24, 228–238.10.1016/0377-2217(86)90044-5
   Web of Science ®Google Scholar
• Chang, S.-P., Kim, S., Lee, J., & Bae, I. (2008). Health monitoring system of a self-anchored suspension bridge (planning, design and installation/operation). Structure and Infrastructure Engineering, 4, 193–205.10.1080/15732470500254790
   Web of Science ®Google Scholar
• Cressie, N. (1992). Statistics for spatial data. Terra Nova, 4, 613–617.
   Google Scholar
• Dorvash, S., Pakzad, S., Naito, C., & Hodgson, I. (2014). Application of state-of-the-art in measurement and data analysis techniques for vibration evaluation of a tall building. Structure and Infrastructure Engineering, 10, 654–669.10.1080/15732479.2012.757795
   Web of Science ®Google Scholar
• Farley, B., Mounce, S., & Boxall, J. (2010). Field testing of an optimal sensor placement methodology for event detection in an urban water distribution network. Urban Water Journal, 7, 345–356.10.1080/1573062X.2010.526230
   Web of Science ®Google Scholar
• Goulet, J., & Smith, I. F. C. (2012). Performance-driven measurement system design for structural identification. Journal of Computing in Civil Engineering, 27, 427–436.
   Web of Science ®Google Scholar
• Grierson, D. E. (2008). Pareto multi-criteria decision making. Advanced Engineering Informatics, 22, 371–384.10.1016/j.aei.2008.03.001
   Web of Science ®Google Scholar
• Guestrin, C., Krause, A., & Singh, A. P. (2005). Near-optimal sensor placements in Gaussian processes. Presented in the 22nd international conference on Machine learning, Bonn, Germany.
   Google Scholar
• Hutton, C. J., Vamvakeridou-Lyroudia, L. S., Kapelan, Z., & Savic, D. A. (2004). Uncertainty quantification and reduction in urban water systems (UWS) modelling: Evaluation. Environment, 33(2), 1–15.
   Google Scholar
• Kang, D., Pasha, M., & Lansey, K. (2009). Approximate methods for uncertainty analysis of water distribution systems. Urban Water Journal, 6, 233–249.10.1080/15730620802566844
   Web of Science ®Google Scholar
• Kripakaran, P., & Smith, I. F. C. (2009). Configuring and enhancing measurement systems for damage identification. Advanced Engineering Informatics, 23, 424–432.10.1016/j.aei.2009.06.002
   Web of Science ®Google Scholar
• Laory, I., Hadj Ali, N., Trinh, T., & Smith, I. F. C. (2012). Measurement system configuration for damage identification of continuously monitored structures. Journal of Bridge Engineering, 17, 857–866.10.1061/(ASCE)BE.1943-5592.0000386
   Web of Science ®Google Scholar
• Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., & Glance, N. (2007). Cost-effective outbreak detection in networks (pp. 420–429). Proceedings of International conference on knowledge discovery and data mining, New York, NY.
   Google Scholar
• Moser, G., German Paal, S., & Smith, I. F. C. (2015). Performance comparison of reduced models for leak detection in water distribution networks. Advanced Engineering Informatics, 29, 714–726.10.1016/j.aei.2015.07.003
   Web of Science ®Google Scholar
• Papadopoulou, M., Raphael, B., Smith, I. F. C., & Sekhar, C. (2014). Hierarchical sensor placement using joint entropy and the effect of modeling error. Entropy, 16, 5078–5101.10.3390/e16095078
   Web of Science ®Google Scholar
• Papadopoulou, M., Raphael, B., Smith, I. F. C., & Sekhar, C. (2016). Evaluating predictive performance of sensor configurations in wind studies around buildings. Advanced Engineering Informatics, 30, 127–142.10.1016/j.aei.2016.02.004
   Web of Science ®Google Scholar
• Preis, A., Whittle, A., & Ostfeld, A. (2009). Multi-objective sensor network placement model for integrated monitoring of hydraulic and water quality parameters. Paper presented at the meeting of World City Water Forum, Incheon, Korea.
   Google Scholar
• Prodon, A., DeNegre, S., & Liebling, T. M. (2010). Locating leak detecting sensors in a water distribution network by solving prize-collecting Steiner arborescence problems. Mathematical Programming, 124, 119–141.10.1007/s10107-010-0368-4
   Web of Science ®Google Scholar
• Raphael, B., & Jadhav, K. S. (2015). Sensor placement for structural monitoring of transmission line towers. Frontiers in Built Environment, 1(24), doi:10.3389/fbuil.2015.00024
   Google Scholar
• Robert-Nicoud, Y., Raphael, B., & Smith, I. F. C. (2005). Configuration of measurement systems using Shannon’s entropy function. Computers & Structures, 83, 599–612.10.1016/j.compstruc.2004.11.007
   Web of Science ®Google Scholar
• Rossman, L. A. (2000). EPANET 2: Users manual. Cincinnati, OH: US Environmental Protection Agency.
   Google Scholar
• Sensus. (2012). Water 20/20: Bringing smart water networks into focus. Technical report. Raleigh, NC: Sensus.
   Google Scholar
• Shannon, C. E., & Weaver, W. (1949). The mathematical theory of communication. Urbana, IL: University of Illinois Press.
   Google Scholar
• Tscheikner-Gratl, F., Sitzenfrei, R., Rauch, W., & Kleidorfer, M. (2016). Enhancement of limited water supply network data for deterioration modelling and determination of rehabilitation rate. Structure and Infrastructure Engineering, 12, 366–380.10.1080/15732479.2015.1017730
   Web of Science ®Google Scholar
• Vtkovský, J. P., Liggett, J. A., & Simpson, A. R. (2003). Optimal measurement site locations for inverse transient analysis in pipe networks. Journal of Water Resources Planning and Management, 129, 480–492.10.1061/(ASCE)0733-9496(2003)129:6(480)
   Web of Science ®Google Scholar
• Watkins, K. (2006). Human Development Report 2006-beyond scarcity: Power, poverty and the global water crisis. UNDP Human Development Reports. New York, NY: Palgrave Macmillan.
   Google Scholar