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The National Academies Keck Center, Washington DC March 10, 2011

NCHRP 08-71 Methodology for Estimating Life Expectancies of Highway Assets Workshop for the Project Panel and Invited Participants. The National Academies Keck Center, Washington DC March 10, 2011. Session 1 How to Use the Guidebook. Who should use the Guide. Roles in asset management.

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The National Academies Keck Center, Washington DC March 10, 2011

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  1. NCHRP 08-71Methodology for Estimating Life Expectancies of Highway AssetsWorkshop for the Project Panel and Invited Participants The National Academies Keck Center, Washington DC March 10, 2011

  2. Session 1 How to Use the Guidebook

  3. Who should use the Guide Roles in asset management

  4. Who should use the Guide • Senior management – top-down vision • Oversight bodies – make service life tangible • Asset managers – decision outcome measure • Practitioners – Learn how to compute and present life expectancy • Engineers and planners – Learn how to use life expectancy in design and planning • System designers – How to build life expectancy into software and tools • Researchers – Improve state of the practice

  5. Structure of the Guide

  6. Potential goals and objectives • Justify maintenance funding • Plan timing and scope of actions • Plan staffing and equipment • Set inventory levels • Evaluate new materials, methods • Reduce workzone frequency • Improve consistency of reports • Optimize cash flow • Build credibility

  7. Potential applications • Life expectancy if no maintenance • Life expectancy under a proposed maint policy • Life extension effects of preservation actions • Compare preservation alternatives • Optimal replacement interval • Optimal preventive maintenance interval • Optimal expenditure on periodic maint • Scope and timing to maximize life extension

  8. Potential applications • Compare design alternatives using life cycle cost • Price point where a new material is attractive • Coordinate replacement of multiple assets • Plan corridor work zones and traffic control • Multi-objective prioritization • Funding allocation and effect of budget cuts • Select treatment application policies • Establish research priorities

  9. Scope of the effort • Start small, build incrementally

  10. Assess gaps and readiness • Asset management maturity scale change

  11. Self-assessment topics

  12. How to use self-assessment • Get ducks in a row • Policies in place • Procedures defined • Ability to deliver planned actions • Availability of data • Decide how far to reach in next 2-3 years • Visualize agency capabilities at the end • Create implementation plan • How to get from here to there

  13. Questions?Discussion

  14. Session 2 Plan for Implementation

  15. Change management • Asset management tools, such as life expectancy analysis, are built in order to improve the way your agency does business. • Organizational change can be beneficial, and can be scary. • You need a vision and a strategy in order to be successful.

  16. What to expect • Credible long-term view of asset performance • Accountability (benefits and fears) • Tangible levels of service • Understanding of deterioration and growth • Optimal preservation • Improved competitiveness for funding • Constructive political relationships Be ready to follow through to win these benefits

  17. Document relevant business processes Why? • Ensure the tools are relevant • Understand how they will be used • Build the right tools for the job • Select appropriatemethods • Help others understand • Gain buy-in

  18. Change strategy • Convince staff of the need and benefit of the change and the tools • Create a change leadership coalition • Develop a vision of the end result • Communicate the vision regularly • Take actions consistent with the vision • Make sure staff are involved and empowered • Show short-term successes • Keep the focus on the change effort • Anchor new approaches into the culture

  19. Planning technical implementation • Data acquisition and management • Plan foundation analysis methods • List/describe applications and reports • Write a work plan • Set quality metrics and milestones

  20. Databases used in life expectancy NOAA Climate Divisions • Geo-referencing • Traffic counts • Crashes • Asset inventory • Asset condition • Asset vulnerability • Climate • Soils

  21. Select foundation tools Considerations: • Purpose of the tools • Types of assets to be addressed • Performance measures • Define end-of-life • Define intervention possibilities • Account for uncertainty Analysis level: • Network level – Life expectancy of families of assets based on general characteristics • Project level – Life expectancy of a single asset based on age, condition, and asset characteristics

  22. Describe applications and reports Considerations: • Subject matter • Filtering • Aggregation • Sorting • Graphics

  23. Example work plan Task 1. Define scope of the analysis. Task 2. Develop implementation plan. Task 3. Define performance metrics and analysis concepts, including data requirements and mock-ups. Task 4. Develop foundation tools and models. Task 5. Build applications, possibly through a series of prototypes. Task 6. Ensure long-term support. Evaluate usage of the product and make improvements.

  24. Questions?Discussion

  25. Session 3 Establishing the Framework

  26. Life expectancy estimation based on replacement intervals

  27. Life expectancy estimation based on asset condition/performance

  28. Life expectancy as a measure of investment benefit

  29. Defining end-of-life • Life expectancy depends on how you define the end-of-life. • Agencies may often have a degree of control over life expectancy. • Lifespan can often bemanaged to maximizeagency objectives orminimize life cyclecosts.

  30. Defining end-of-life

  31. Defining end-of-life

  32. Defining end-of-life

  33. Coordinating lifespans of asset components

  34. Planning component life based on functional life

  35. Life extension

  36. Role of uncertainty in program planning

  37. Forecasting life expectancy • Techniques are related to deterioration modeling, but usually simpler. • Select a method based on the kind of data available, the needs of the application, and the importance of uncertainty

  38. Types of models

  39. Data collection Visual inspection (100% sample) 10% sample of road segments Automated data collection

  40. Example report/app mockups Digital dashboards

  41. Example report/app mockups Using Excel for report mock-ups

  42. Example report/app mockups Using Excel for application development

  43. Questions?Discussion

  44. Session 4 Developing Foundation Tools

  45. Presentation Outline • What to Model • Model • Selection • Estimation • Techniques • Conclusion • What to Model • Influence of Framework • Model Selection • Selection Criteria • Data Availability • Nature of Prediction and Outcome • Estimation Techniques • Regression • Survival Models • Markov Chains 45

  46. Defining End-of-Life • What to Model • Model • Selection • Estimation • Techniques • Conclusion • End-of-Life can be taken as the time until • Functional Obsolescence • Changes in standards • Changes in functional requirements • Structural Deficiency • Deterioration • Extreme events • If modeled separately – Min. life assumed • If combined – Direct prediction of life 46

  47. Interval-based • What to Model • Model • Selection • Estimation • Techniques • Conclusion • Two general approaches • Interval-based • Predict time until end-of-life event occurs • Directly predict life based on historical replacement intervals 47

  48. Condition-based • What to Model • Model • Selection • Estimation • Techniques • Conclusion • Two general approaches • Condition-based • Predict condition or measure of performance as a function of time • Predict asset value as a function of time 48

  49. Model Selection Criteria • What to Model • Model • Selection • Estimation • Techniques • Conclusion • General Criteria • Transparent • Staff Knowledge • Able to Replicate and Revise • Applicable • Data Availability • Widespread Use of Results • Focused • Prioritize on Predicting Life • Not necessarily Deterioration-based 49

  50. Data Availability • What to Model • Model • Selection • Estimation • Techniques • Conclusion • Model Selection depends on • Data Availability • Historical Service Life • Dominating end-of-life condition preferred • Condition Data by Age • Archived Data Preferred 50

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