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Criticality and Prioritization of Pipe Rehab Projects Annie Vanrenterghem Raven, Ph.D.

Criticality and Prioritization of Pipe Rehab Projects Annie Vanrenterghem Raven, Ph.D. Research Associate Professor Polytechnic Institute of NYU. Assignment. Choose best pipes candidates for rehab for the next year (short term) given a certain budget. Constraints.

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Criticality and Prioritization of Pipe Rehab Projects Annie Vanrenterghem Raven, Ph.D.

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  1. Criticality and Prioritization of Pipe Rehab Projects Annie Vanrenterghem Raven, Ph.D. Research Associate Professor Polytechnic Institute of NYU UIM, Dec 9, 2008

  2. Assignment Choose best pipes candidates for rehab for the next year (short term) given a certain budget. UIM, Dec 9, 2008

  3. Constraints • Several thousands of miles of pipes • Limited budget • Many criteria to take into account • Choices will have to be justified UIM, Dec 9, 2008

  4. Water and Waste Water Rehabilitation Planning UIM, Dec 9, 2008

  5. Points of View Points of view: • Time: long term and next year/short term • Space: whole system; zone; cohort of pipes; pipe level • Prioritize or optimize • Problems to address: • Structural • Water quality • Hydraulic (adequate pressure) UIM, Dec 9, 2008

  6. Other Points of View Points of view: • Time: long term and next year/short term • Space: whole system; zone; cohort of pipes; pipe level • Prioritize or optimize • Problems to address: • Structural • Water quality • Hydraulic (adequate pressure) UIM, Dec 9, 2008

  7. The suite of tools Macro analysis Input and output data at system, zone or cohort level Micro analysis Input and output data at pipe level Failure Forecasting = Calculate the probability of failure for each year and each pipe Long-Term Rehabilitation Planning = Design CIP Based on stock and degradation Performance Indicators = Prioritize problems Zones cohorts Annual Rehabilitation Planning Hydraulic Criticality and Vulnerability UIM, Dec 9, 2008

  8. Overall Context • Legal GASB 34; no federal mandate; state or local incentives • Physical • Assets are ageing; in need of rehab (ASCE report card: D-) • Water quality problems, decrease of hydraulic capacity • Systems need to grow or shrink • Natural • Drought situations make leaks and breaks unacceptable • Financial • Capital needs ( to maintain and replace existing water infrastructure between 2003 and 2023) expected to be $277 billion; up to 2/3 for buried assets. (U.S. EPA, 2005) • Gap between projected revenues and expenses • Less public funding Full cost pricing rate increases • Less demand/revenues due to conservation technologies, change in public behavior, current financial crisis. UIM, Dec 9, 2008

  9. Rehab planning: challenges • Buried • A lot to replace (US: 1M+ mi; LV: 4K mi; NYC: 6K mi) • Networks are scattered and ubiquitous; even at most sensitive areas • Inspection, repair, replacement expensive and disruptive • Degradation/failure/impact unknown or very spectacular… UIM, Dec 9, 2008

  10. Spectacular failure… UIM, Dec 9, 2008

  11. Rehab planning: challenges • Multi-problems; multi-disciplinary, complex tasks involving multiple criteria. • In the past, rehabilitation decisions have been pragmatic, opportunistic, and difficult to justify. In house DSS attempts; could be quite simplistic (matrix) and erroneous. • No comprehensive research in the US. • Lack of trust for advanced models (“black boxes”.) • Funds are limited but business case of AM is still difficult to make. UIM, Dec 9, 2008

  12. Rehab planning: Challenges with data • If data does not exist, it has to be collected. Even simple solutions need data. • Data is needed to populate the GIS and the HM, CMMS and AM system. • Data collection is expensive. Data should be used for more than having a snap shot of the system at a given time, to set priorities. • Prioritization should use the kind of advanced tools (used in other industries) that provide more answers and deal with uncertainty. • This can be done at a rather low marginal cost. Added value and function to high price tools such as GIS, HM, CMMS. However the data must meet certain needs. UIM, Dec 9, 2008

  13. Assignment Choose best pipes candidates for rehab for the next year (short term) given a certain budget. UIM, Dec 9, 2008

  14. Annual Rehab Planning • Multi-Criteria Decision Making Model (Electre) uses reference profiles to mitigate uncertainty. • Criteria express risk (probability x consequences of failure) as well as other relevant points of view. • Data is collected at different levels of refining. UIM, Dec 9, 2008

  15. UIM, Dec 9, 2008

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  18. Criteria, example PWI PWI(i) = PFR(i) x EDI(i) x NPS(i) Units: (No./mile/year) x (hours) x (persons) With: • PFR (i) Predicted Failure Rate for pipe i (No./mile/year) • EDI (i) Expected Duration of Interruption (hours) • NPS (i) Number of Customers Supplied by pipe (i) (or by all pipes that will be affected by the interruption of service; using hydraulic criticality results ) UIM, Dec 9, 2008

  19. Criteria, example ARC ARC (i) = PFR (i) x UCRp(i) Units: (No./100m/year) x ($) With : • PFR (i) Predicted Failure Rate for pipe i (No./mile/year) • UCRp (i) is the Unit Cost of Repair ($) UIM, Dec 9, 2008

  20. Knowledge base, example UCR UIM, Dec 9, 2008

  21. Weights, example UIM, Dec 9, 2008

  22. The reference profiles (electre) UIM, Dec 9, 2008

  23. Dealing with uncertainty (electre) UIM, Dec 9, 2008

  24. The categories • C33: Pipes with highest priority level. Pipes have been assigned to C3 according to both OP and PP. • C32 (or C31): No consensus among criteria may be due to incomparability. • C31, C22, C21, C11: low and moderate performance deficiencies. UIM, Dec 9, 2008

  25. Annual Rehab Planning Categories UIM, Dec 9, 2008

  26. Annual Rehab Planning results on GIS UIM, Dec 9, 2008

  27. The suite of tools Macro analysis Input and output data at system, zone or cohort level Micro analysis Input and output data at pipe level Failure Forecasting = Calculate the probability of failure for each year and each pipe Long-Term Rehabilitation Planning = Design CIP Based on stock and degradation Performance Indicators = Prioritize problems Zones cohorts Annual Rehabilitation Planning Hydraulic Criticality and Vulnerability UIM, Dec 9, 2008

  28. Failure ForecastingHydraulic criticality • FF = Calculate Probability of each pipe for each year (PHM, LEYP) • Hydraulic criticality and vulnerability • Effect of one pipe being out of service on delivery of service in rest of system • Effect of each pipe being out of service on one specific pipe • 2 pipes being out of service UIM, Dec 9, 2008

  29. Implication of Polytechnic University Macro analysis Input and output data at system, zone or cohort level Micro analysis Input and output data at pipe level Annual Rehabilitation Planning Hydraulic Criticality and Vulnerability Long-Term Rehabilitation Planning Performance Indicators Failure Forecasting UIM, Dec 9, 2008

  30. Thank you for your attention! avanraven@poly.edu UIM, Dec 9, 2008

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