From sewer inspection programmes to rehabilitation needs: research and results related to data quality and availability with the support of numerical experiment

References

• Ahmadi, M., Cherqui, F., De Massiac, J.-C., & Le Gauffre, P. (2013). Influence of available data on sewer inspection program efficiency. Urban Water Journal, 17 p. doi:10.1080/1573062X.2013.831910
   Web of Science ®Google Scholar
• Ahmadi, M., Cherqui, F., De Massiac, J. C., & Le Gauffre, P. (accepted). Benefits of using basic, imprecise or uncertain data for elaborating sewer inspection programs. Journal of Structure and Infrastructure Engineering.
   Google Scholar
• Ahmadi, M., Cherqui, F., De Massiac, J. C., Werey, C., Lagoutte, S., & Le Gauffre, P. (2014). Condition grading for dysfunction indicators in sewer asset management. Journal of Structure and Infrastructure Engineering: Maintenance, Management, Life-Cycle Design and Performance, 10, 346–358.
   Web of Science ®Google Scholar
• Alegre, H., Hirner, W., Baptista, J. M., & Parena, R. (2000). Performance indicators for water supply services IWA (International Water Association) manual of best practice. London: IWA.
   Google Scholar
• Ana, E., & Bauwens, W. (2010). Modeling the structural deterioration of urban drainage pipes: The state-of-the-art in statistical methods. Urban Water Journal, 7, 47–59.10.1080/15730620903447597
   Web of Science ®Google Scholar
• Berardi, L., Giustolisi, O., Savic, D. A., & Kapelan, Z. (2009). An effective multi-objective approach to prioritisation of sewer pipe inspection. Water Science & Technology, 60, 841–850.10.2166/wst.2009.432
   PubMed Web of Science ®Google Scholar
• Berlin, M., Suresh, Kumar G., & Nambi, Indumathi M. (2013). Numerical modelling on fate and transport of nitrate in an unsaturated system under non-isothermal condition. European Journal of Environmental and Civil Engineering, 17, 350–373.
   Web of Science ®Google Scholar
• Boissier, D., & Al-Hajjar, J. (1993). Système Interactif d’Aide à la décision (SIAD) et incertain en génie civil [Decision aid interactive system (SIAD) and uncertain in civil engineering]. EUROPIA 93: Delft.
   Google Scholar
• Bouchon-Meunier, B. (1995). La logique floue et ses applications [Fuzzy logic and its applications]. Addison Wesley, p. 257.
   Google Scholar
• Breysse, D., Vasconcelos, E., & Schoefs, F. (2007). Management strategies and improvement of performance of sewer networks. Computer-aided Civil and Infrastructure Engineering, 22, 462–477.10.1111/mice.2007.22.issue-7
   Web of Science ®Google Scholar
• Cashman, A., Savic, D., Saul, A., Ashley, R., Walters, G., Blanksby, J., Djordjevic, S., Unwin, D., & Kapelan, Z. (2004). Whole life costing of sewers (Cost-S): Developing a sustainable approach to sewer network management. Proceedings Novatech. Lyon, France.
   Google Scholar
• Clément, A., Laurens, S., Arliguie, G., & Deby, F. (2012). Numerical study of the linear polarisation resistance technique applied to reinforced concrete for corrosion assessment. European Journal of Environmental and Civil Engineering, 16, 491–504.
   Web of Science ®Google Scholar
• Curt, C., & Boissier, D. (2008). Utilisation de la théorie des possibilités pour l’évaluation de la performance des barrages [Use of possibility theory for assessing dams’ performance]. 26e Rencontres de l’Association Universitaire du Génie Civil, Université de Nancy, juin 4–6. Actes sur CD-ROM, 8 p.
   Google Scholar
• Davies, J. P., Clarke, B. A., Whiter, J. T., Cunningham, R. J., & Leidi, A. (2001). The structural condition of rigid sewer pipes: A statistical investigation. Urban Water, 3, 277–286.10.1016/S1462-0758(01)00036-X
   Google Scholar
• Dirksen, J. (2013). Monitoring ground settlement to guide sewer asset management (PhD thesis). TUD Technische Universiteit Delft, Netherlands, 175 p.
   Google Scholar
• Dirksen, J., Clemens, F. H. L. R., Korving, H., Cherqui, F., Le Gauffre, P., Ertl, T., … Snaterse, C. T. M. (2013). The consistency of visual sewer inspection data. Structure and Infrastructure Engineering, 9, 214–228.10.1080/15732479.2010.541265
   Web of Science ®Google Scholar
• Ellis, J. B., & Bertrand-Krajewski, J.-L. (Eds.). (2006). Assessing infiltration and exfiltration on the performance of Urban Sewer Systems. London: IWA, p. 192.
   Google Scholar
• Hahn, M., Palmer, R., Merrill, M., Lukas, A., & Lukas, A. (2002). Expert system for prioritizing the inspection of sewers: Knowledge base formulation and evaluation. Journal of Water Resources Planning and Management, 128, 121–129.10.1061/(ASCE)0733-9496(2002)128:2(121)
   Web of Science ®Google Scholar
• Knolmar, M., & Szabo, G. C. (2003). CARE-S report D3 - WP2 Structural condition - Classification systems based on visual inspection. Technical report of the CARE-S project, Budapest University of Technology and Economics , 192 p.
   Google Scholar
• Koo, D.-H., & Ariaratnam, S. T. (2006). Innovative method for assessment of underground sewer pipe condition. Automation in Construction, 15, 479–488.10.1016/j.autcon.2005.06.007
   Web of Science ®Google Scholar
• Le Gauffre, P., & Cherqui, F. (2009). Sewer rehabilitation criteria evaluated by fusion of fuzzy indicators. LESAM 2009 – 3rd IWA Leading Edge Conference on Strategic Asset Management. Miami, US.
   Google Scholar
• Le Gauffre, P., Joannis, C., Vasconcelos, E., Breysse, D., Gibello, C., & Desmulliez, J. J. (2007). Performance indicators and multicriteria decision support for sewer asset management. Journal of Infrastructure Systems, 13, 105–114.10.1061/(ASCE)1076-0342(2007)13:2(105)
   Web of Science ®Google Scholar
• Malm, A., Svensson, G., Bäckman, H., & Morrison, G. M. (2013). Prediction of water and wastewater networks rehabilitation based current age and material distribution. Water Science & Technology: Water Supply, 13, 227–237.
   Web of Science ®Google Scholar
• Marlow, D., & Burn, S. (2008). Effective use of condition assessment within asset management. Journal of the American Water Works Association, 100, 54–63.
   Web of Science ®Google Scholar
• Marlow, D., Heart, S., Burn, S., Urquhart, A., Gould, S., Anderson, M., Cook, S., Ambrose, M., Madin, B., & Fitzgerald, A. (2007). Condition assessment strategies and protocols for water and wastewater utility assets. London: WERF – Water Environment Research Foundation/IWA, p. 476.
   Google Scholar
• Matos, R., Ashley, R. M., Cardoso, A., Molinari, A., Schulz, A., & Duarte, P. (2003). Performance indicators for wastewater services. London: International Water Association, ISBN: 1900222906.
   Google Scholar
• McCullagh, P., & Nelder, J. A. (1989). Generalized Linear Models (2nd ed.). Boca Raton, FL: Chapman & Hall/CRC.10.1007/978-1-4899-3242-6
   Google Scholar
• McDonald, S., & Zhao, J. (2001). Condition assessment and rehabilitation of large sewers. International conference on Underground Infrastructure Research, University of Waterloo, Waterloo, Ontario, June 10–13, 2001, pp. 361–369.
   Google Scholar
• Merrill, S., Lukas, A., Palmer, R. N., & Hahn, M. A. (2004). Development of a tool to prioritize sewer inspections. Technical report, WERF – Water Environment Research Foundation –123).
   Google Scholar
• Rahman, S., & Vanier, D. J. (2004). MIIP client report: An evaluation of condition assessment protocols for sewer management. Ottawa: National Research Council of Canada.
   Google Scholar
• Salman, B. (2010). Infrastructure management and deterioration risk assessment of wastewater collection systems (Thesis PhD). University of Cincinnati, OH, USA.
   Google Scholar
• Salman, B., & Salem, O. (2012). Modeling failure of wastewater collection lines using various section-level regression models. Journal of Infrastructure Systems, 18, 146–154.10.1061/(ASCE)IS.1943-555X.0000075
   Web of Science ®Google Scholar
• Saegrov, S. (Ed.). (2005). Computer aided rehabilitation for water networks. London: IWA, pp. 208.
   Google Scholar
• Saegrov, S. (Ed.). (2006). CARE-S – computer aided rehabilitation for sewer and stormwater networks. London: IWA, ISBN: 1843391155.
   Google Scholar
• Scheidegger, A., Hug, T., Rieckermann, J., & Maurer, M. (2011). Network condition simulator for benchmarking sewer deterioration models. Water Research, 45, 4983–4994.10.1016/j.watres.2011.07.008
   PubMed Web of Science ®Google Scholar
• Urich, C., Sitzenfrei, R., Möderl, M., & Rauch, W. (2010). An agent-based approach for generating virtual sewer systems. Water Science & Technology, 62, 1090–1097.10.2166/wst.2010.364
   PubMed Web of Science ®Google Scholar
• Van der Steen, A. J., Dirksen, J., & Clemens, F. H. L. R. (2013). Visual sewer inspection: Detail of coding system versus data quality? Structure and Infrastructure Engineering: Maintenance, Management, Life-Cycle Design and Performance, Published online: August 25, 2013. doi: 10.1080/15732479.2013.816974
   Google Scholar
• Vasconcelos, E. (2005). Outils d’aide à la gestion du patrimoine réseau d’assainissement non visitable Thèse de Doctorat de l’Université Bordeaux 1.
   Google Scholar
• Werey, C., Cherqui, F., Ibrahim, M., & Gauffre, P. (2008). Sewer asset management tool: Dealing with experts’ opinions. In E. A. Cabrera, Jr. & M. A. Pardo (Eds.), Performance assessment of urban infrastructure services. Drinking water, waste water and solid waste (pp. 125–134). London: IWA.
   Google Scholar
• Wirahadikusumah, R., Abraham, D. M., Iseley, T., & Prasanth, R. K. (1998). Assessment technologies for sewer system rehabilitation. Automation in Construction, 7, 259–270.10.1016/S0926-5805(97)00071-X
   Google Scholar