Modeling the structural deterioration of urban drainage pipes: the state-of-the-art in statistical methods

References

• Ariaratnam , S. T. 1994 . “ The investigation of methods to assist decision-makers regarding bridge maintenance, repair and restoration activities ” . Ph.D. dissertation. University of Illinois, Urbana-Champaign, Illinois
   Google Scholar
• Ariaratnam , S. T. , El-Assaly , A. and Tang , Y. 2001 . Assessment of infrastructure inspection needs using logistic models . Journal of Infrastructure Systems , 7 ( 4 ) : 160 – 165 .
   Google Scholar
• Baik , H. S. , Jeong , H. S. and Abraham , D. M. 2006 . Estimating transition probabilities in markov chain-based deterioration models for management of wastewater systems . Journal of Water Resources Planning and Management , 132 ( 1 ) : 15 – 24 .
   Web of Science ®Google Scholar
• Baur , R. and Herz , R. 2002 . Selective inspection planning with ageing forecast for sewer types . Water Science and Technology , 46 ( 6–7 ) : 389 – 396 .
   PubMed Web of Science ®Google Scholar
• Baur , R. , Zielichowski-Haber , W. and Kropp , I. Statistical analysis of inspection data for the asset management of sewer networks . Proceedings 19th EJSW on Process Data and Integrated Urban Water Modeling . Lyon, France.
   Google Scholar
• Bewick , V. , Cheek , L. and Ball , J. 2005 . Statistics review 14: Logistic regression . Critical Care , 9 ( 1 ) : 112 – 118 .
   PubMed Web of Science ®Google Scholar
• Butt , A. A. , Feighan , K. J. , Shahin , M. Y. and Carpenter , S. 1987 . “ Pavement performance prediction model using the Markov process ” . In Transportation Research Record 1123 , Washington, DC : Transportation Research Board .
   Google Scholar
• Chae , M. J. , Iseley , T. and Abraham , D. M. Computerized sewer pipe condition assessment . Proceedings International Conference on Pipeline Engineering and Construction . Baltimore, MD.
   Google Scholar
• Davies , J. P. , Clarke , B. A. , Whiter , J. T. and Cunningham , R. J. 2001a . Factors affecting the structural deterioration and collapse of rigid sewer pipes . Urban Water , 3 : 73 – 89 .
   Google Scholar
• Davies , J. P. , Clarke , B. A. , Whiter , J. T. , Cunningham , R. J. and Leide , A. 2001b . The structural condition of rigid sewer pipes: A statistical investigation . Urban Water , 3 : 277 – 286 .
   Google Scholar
• Dayton , C. M. 1992 . Logistic regression analysis Stat 474–574, Articles and Links. http://bus.utk.edu/stat/DataMining/articles.htm (accessed 28 November 2007)
   Google Scholar
• Dirksen , J. , Goldina , A. , ten Veldhuis , J. A.E. and Clemens , F. H.L.R. The role of uncertainty in urban drainage decisions: Uncertainty in inspection data and their impact on rehabilitation decisions . Proceedings IWA 2nd Leading-Edge Conference in Asset Management . Lisbon, Portugal.
   Google Scholar
• El-Assaly , A. , Ariaratnam , S. T. , Ruwanpura , J. and Ng , H. 2006 . Cost forecast model for sewer infrastructure . Municipal Engineer , 159 ( 3 ) : 155 – 160 .
   Web of Science ®Google Scholar
• Fenner , R. A. 2000 . Approaches to sewer maintenance . Urban Water , 2 : 343 – 356 .
   Google Scholar
• Hahn , M. A. , Palmer , R. N. , Merill , M. S. and Lukas , A. 2002 . Expert system for prioritizing the inspection of sewers: Knowledge base formulation and evaluation . Journal of Water Resources Planning and Management , 128 ( 2 ) : 121 – 129 .
   Web of Science ®Google Scholar
• Hair , J. F. , Anderson , R. E. , Tatham , R. L. and Black , W. C. 1998 . Multivariate data analysis , London : Prentice-Hall .
   Google Scholar
• Harwood , D. W. , St. John , A. D. and Bauer , K. M. 1982 . Application of statistical techniques to gas operations , Chicago, IL : Gas Research Institution .
   Google Scholar
• Herz , R. K. 1996 . Aging processes and rehabilitation needs of drinking water distribution networks . Journal of Water , 45 : 221 – 231 .
   Web of Science ®Google Scholar
• Hoffman , F. , Gao , X. Z. , Olhofer , M. and Satyadas , A. 2005 . Editorial . Journal of Applied Soft Computing , 5 : 261 – 264 .
   Google Scholar
• Horold , S. Forecasting rehabilitation needs: Evaluation of the AQUA WertMin software for service life and total cost estimation . SINTEF Report . Trondheim, Norway.
   Google Scholar
• Horold , S. and Baur , R. Modeling sewer deterioration for selective inspection planning – case study Dresden . Proceedings 13th EJSW on Service Life Management Strategies of Water Mains and Sewers . September 8–12 , Switzerland.
   Google Scholar
• IIMM . 2006 . International infrastructure management manual , 2006 edition , New Zealand : National Asset Management Steering (NAMS) Group .
   Google Scholar
• Jansen , K. 2007 . AQUA-WertMin program manual , Klienbittersdorf, , Germany : Expert Office for Sewer Rehabilitation .
   Google Scholar
• Jiang , Y. , Saito , M. and Shinha , K. C. 1988 . “ Bridge performance prediction model using Markov chain ” . In Transportation Research Record 1180 , Washington, DC : Transportation Research Board .
   Google Scholar
• Kleiner , Y. 2001 . Scheduling inspection and renewal of large infrastructure assets . Journal of Infrastructure Systems , 7 ( 4 ) : 136 – 143 .
   Google Scholar
• Kleiner , Y. , Sadiq , R. and Rajani , B. 2006 . Modelling the deterioration of buried infrastructure as a fuzzy Markov process . Journal of Water Supply Research and Technology: Aqua , 55 ( 2 ) : 67 – 80 .
   Web of Science ®Google Scholar
• Kleiner , Y. , Rajani , B. and Wang , S. Consideration of static and dynamic effects to plan water main renewal . Proceedings Middle East Water 2007, 4th International Exhibition and Conference for Water Technology . Manama, Bahrain.
   Google Scholar
• Konig , A. 2005 . “ CARE-S WP2 External corrosion model description ” . In SINTEF Report. Trondheim , Norway : SINTEF Technology and Society .
   Google Scholar
• Kulkarni , R. B. 1984 . “ Dynamic decision model for a pavement management system ” . In Transportation Research Record 997 , Washington, DC : Transportation Research Board .
   Google Scholar
• Lawless , J. F. 1982 . Statistical models and methods for lifetime data , New York : John Wiley and Sons .
   Google Scholar
• Le Gat , Y. 2008 . Modelling the deterioration process of drainage pipelines . Urban Water , 5 ( 2 ) : 97 – 106 .
   Web of Science ®Google Scholar
• Lemer , A. 2000 . Building public works infrastructure management systems for achieving high returns on public assets . Public Works Management & Policy , 3 ( 3 ) : 255 – 272 .
   Google Scholar
• Madanat , S. M. , Mishalani , R. and Ibrahim , W. H.W. 1995 . Estimation of infrastructure transition probabilities from condition rating data . Journal of Infrastructure Systems, ASCE , 1 ( 2 ) : 120 – 125 .
   Google Scholar
• Makar , J. M. 1999 . Diagnostic techniques for sewer systems . Journal of Infrastructure Systems , 5 ( 2 ) : 69 – 78 .
   Google Scholar
• Mehle , J. J. , O'Keefe , S. M. and Wrase , P. E. 2001 . An examination of methods for condition rating of sewer pipelines Center for Development of Technical Leadership, University of Minnesota
   Google Scholar
• Merill , M. S. , Lukas , A. , Palmer , R. N. and Hahn , M. A. 2004 . Development of a tool to prioritize sewer inspections , Virginia, , USA : Water Environmental Research Foundation .
   Google Scholar
• Micevski , T. , Kuczera , G. and Coombes , P. 2002 . Markov model for storm water pipe deterioration . Journal of Infrastructure System , 8 ( 2 ) : 49 – 56 .
   Google Scholar
• Muller , K. and Fischer , B. Objective condition assessment of sewer systems . Proceedings IWA 2nd Leading-Edge Conference in Asset Management . Lisbon, Portugal.
   Google Scholar
• NEN3399 . 1992 . Sewerage systems outside buildings – Classification for visual inspection of sewers , The Netherlands : Nederlands Normalisatie Instituut .
   Google Scholar
• Rajani , B. and Kleiner , Y. 2001 . Comprehensive review of structural deterioration of water mains: Physically-based models . Urban Water , 3 : 151 – 164 .
   Google Scholar
• Ruwanpura , J. , Ariaratnam , S. and El-Assaly , A. 2004 . Prediction models for sewer infrastructure utilizing rule-based simulation . Journal of Civil Engineering and Environmental Systems , 21 ( 3 ) : 169 – 185 .
   Web of Science ®Google Scholar
• Ross , S. M. 2000 . Introduction to probability models , 7th ed , San Diego, CA : Academic Press .
   Google Scholar
• Stone , S. , Dzuray , E. J. , Meisegeier , D. , Dahlborg , A. S. and Erickson , M. 2002 . Decision-support tools for predicting the performance of water distribution and wastewater collection systems , Ohio : US EPA .
   Google Scholar
• Tran , D. H. 2007 . Investigations of deterioration models for stormwater pipelines Doctoral Dissertation. School of Architectural, Civil and Mechanical Engineering, Faculty of Health, Engineering and Science, Victoria University
   Google Scholar
• Tran , D. H. , Ng , A. W.M. , Perera , B. J.C. and Davis , P. 2006 . Application of probabilistic neural networks in modeling structural deterioration of stormwater pipes . Urban Water Journal , 3 ( 3 ) : 175 – 184 .
   Google Scholar
• Tu , J. V. 1996 . Advantages and disadvantages of using artificial neural networks versus logisitic regression for predicting medical outcomes . Journal of Clinical Epidemiology , 49 ( 11 ) : 1225 – 1231 .
   PubMed Web of Science ®Google Scholar
• Vanier , D. J. 2001 . Why industry needs asset management tools . Journal of Computing in Civil Engineering , 15 ( 1 ) : 35 – 43 .
   Web of Science ®Google Scholar
• Wirahadikusumah , R. , Abraham , D. M. , Iseley , T. and Prasanth , R. K. 1998 . Assessment technologies for sewer system rehabilitation . Automation in Construction , 7 : 259 – 270 .
   Google Scholar
• Wirahadikusumah , R. , Abraham , D. and Iseley , T. 2001 . Challenging issues in modeling deterioration of combined sewers . Journal of Infrastructure Systems , 7 ( 2 ) : 77 – 84 .
   Google Scholar
• WRc . 2001 . Sewer rehabilitation manual , 4th ed , UK : Water Research Council Publications .
   Google Scholar
• Yang , J. 2004 . Road crack condition performance modeling using recurrent Markov chains and artificial neural networks Doctoral Dissertation. Department of Civil and Environmental Engineering, College of Engineering, University of South Florida
   Google Scholar