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

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Presentation on theme: "UIM, Dec 9, 2008 Criticality and Prioritization of Pipe Rehab Projects Annie Vanrenterghem Raven, Ph.D. Research Associate Professor Polytechnic Institute."— Presentation transcript:

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

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

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

4 4 UIM, Dec 9, 2008 Water and Waste Water Rehabilitation Planning Buried Network Buried Non Network Above ground Network Above Ground Non network Water PipesValves; fittings Hydrants; Manholes Plant; Pumping Station Waste Water Sewer Storm Water Storm Drains

5 5 UIM, Dec 9, 2008 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)

6 6 UIM, Dec 9, 2008 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)

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

8 8 UIM, Dec 9, 2008 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.

9 9 UIM, Dec 9, 2008 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…

10 10 UIM, Dec 9, 2008 Spectacular failure…

11 11 UIM, Dec 9, 2008 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.

12 12 UIM, Dec 9, 2008 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.

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

14 14 UIM, Dec 9, 2008 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.

15 15 UIM, Dec 9, 2008 Criterion nameCodeDefinition Coordination with Other Infrastructure Co-ordination Score COS Score [-1,1] Repair Costs Annual Repair Cost ARC ARC(i) = PFR(i) x UCRp(i) Water Loss Water Losses Index WLI Index [0,1] Water Interruptions Predicted Water Interruptions PWI PWI(i) = PFR(i) x EDI(i) x NPS(i) Predicted Critical Water Interruptions PCWI PCWI(i) = PFR(i) x EDI(i) x SC(i) Predicted Frequency of Water Interruptions PFWI PFWI(i) = L (i)/100 x PFR(i) x EDI(i) Damages and Disruptions Damages due to Flooding in Housing areas DFH DFH(i) = PFR(i) x DI^2(i) x P(i) x SFH(i) Damages due to Flooding in Industrial or commercial areas DFI DFI(i) = PFR(i) x DI^2(i) x P(i) x SFI(i) Damages due to Soil Movement DSM DSM(i) = PFR(i) x DI^2(i) x P(i) x LS(i) Traffic Disruption DT DT (i) = PFR(i) x SR(i) Damages and/or Disruption on other Infrastructure DDI DDI(i) = PFR(i) x DI^2(i) x P(i) x SI(i) Water Quality Water Quality Deficiencies WQD Index [0,1] Hydraulic Criticality Hydraulic Criticality Index HCI Index [0,1]

16 16 UIM, Dec 9, 2008 Criterion name CodeDefinition Coordination with Other Infrastructure Co-ordination Score COS Score [-1,1] Repair Costs Annual Repair Cost ARC ARC(i) = PFR(i) x UCRp(i) Water Loss Water Losses Index WLI Index [0,1] Water Interruptions Predicted Water Interruptions PWI PWI(i) = PFR(i) x EDI(i) x NPS(i) Predicted Critical Water Interruptions PCWI PCWI(i) = PFR(i) x EDI(i) x SC(i) Predicted Frequency of Water Interruptions PFWI PFWI(i) = L (i)/100 x PFR(i) x EDI(i) Damages and Disruptions Damages due to Flooding in Housing areas DFH DFH(i) = PFR(i) x DI^2(i) x P(i) x SFH(i) Damages due to Flooding in Industrial or commercial areas DFI DFI(i) = PFR(i) x DI^2(i) x P(i) x SFI(i) Damages due to Soil Movement DSM DSM(i) = PFR(i) x DI^2(i) x P(i) x LS(i) Traffic Disruption DT DT (i) = PFR(i) x SR(i) Damages and/or Disruption on other Infrastructure DDI DDI(i) = PFR(i) x DI^2(i) x P(i) x SI(i) Water Quality Water Quality Deficiencies WQD Index [0,1] Hydraulic Criticality Hydraulic Criticality Index HCI Index [0,1]

17 17 UIM, Dec 9, 2008 Criterion nameCodeDefinition Coordination with Other Infrastructure Co-ordination Score COS Score [-1,1] Repair Costs Annual Repair Cost ARC ARC(i) = PFR(i) x UCRp(i) Water Loss Water Losses Index WLI Index [0,1] Water Interruptions Predicted Water Interruptions PWI PWI(i) = PFR(i) x EDI(i) x NPS(i) Predicted Critical Water Interruptions PCWI PCWI(i) = PFR(i) x EDI(i) x SC(i) Predicted Frequency of Water Interruptions PFWI PFWI(i) = L (i)/100 x PFR(i) x EDI(i) Damages and Disruptions Damages due to Flooding in Housing areas DFH DFH(i) = PFR(i) x DI^2(i) x P(i) x SFH(i) Damages due to Flooding in Industrial or commercial areas DFI DFI(i) = PFR(i) x DI^2(i) x P(i) x SFI(i) Damages due to Soil Movement DSM DSM(i) = PFR(i) x DI^2(i) x P(i) x LS(i) Traffic Disruption DT DT (i) = PFR(i) x SR(i) Damages and/or Disruption on other Infrastructure DDI DDI(i) = PFR(i) x DI^2(i) x P(i) x SI(i) Water Quality Water Quality Deficiencies WQD Index [0,1] Hydraulic Criticality Hydraulic Criticality IndexHCIIndex [0,1]

18 18 UIM, Dec 9, 2008 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 )

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

20 20 UIM, Dec 9, 2008 Knowledge base, example UCR CodeCostDescription 13000 Unknow 21900 Diam <12” & easy context 33100 Diam <12” & normal context or Diam >12” & easy context 44700 Diam >12” & normal context or Diam <12” & difficult context 56200 Diam >12” difficult context

21 21 UIM, Dec 9, 2008 Weights, example

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

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

24 24 UIM, Dec 9, 2008 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.

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

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

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

28 28 UIM, Dec 9, 2008 Failure Forecasting Hydraulic 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

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

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

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