1. Life Cycle Costing and Reliability H. Scott Matthews February 5, 2003

2. Homework 1 Comments  Average 23.5/25  No major issues other than using wrong data (not sure where you got it)  Key things:  “Condition” is a vague word unless defined  “Conditions” getting better, may hit goal  Probably makes more sense to move towards reliability rather than condition- based assessment methods (more on that today)

3. Recap of Last Lecture  Defined and discussed performance  Humplick paper reminded us of multiple levels and perspectives  We hadn’t discussed users much before  Talked about why infrastructure matters  And why performance measurement is difficult  Overview of performance methods

4. Life Cycle Costing (LCC)  Mentioned earlier in course  Is a tool to assist decision makers in managing ‘total costs’ of projects  Includes design, construction, 4R’s (repair, rehabilitation, replacement, reconstruction), user costs, disposal  Converted into ‘present value’ costs  Generally an “economic-only” (costs only) framework  Others (around CMU and elsewhere) have added consideration of energy/environmental

5. More Background  ISTEA (1991) suggested LCC for pavement, bridge, tunnel projects  FHWA in 1996 linked funds availability to use of LCC in major projects  Why might you not want to use LCC?  How does this differ from Benefit-Cost Analysis?

6. Initial Costs  Usually site preparation and construction  Should consider ‘user costs’ (traffic, etc)  Where to get data - current/completed projects similar in design/scope

7. 4R’s and Salvage Costs  Are dependent on technology and materials choices  E.g. depth of pavement affects useful life  Should not exclude costs that seem ‘too small’ - you don’t know ‘how small’ until total costs estimated!  Salvage - potential value of materials at end-of-life (e.g. scrap steel, asphalt, etc)

8. User (Delay) Costs  Consideration of opportunity cost of time for drivers when inconvenienced due to infrastructure downtime  E.g. congestion, re-routing around road  Should also consider vehicle operating delay cost (fuel, etc).  A cost/vehicle estimate used  $12-$25 for cars/big trucks gets used

9. Examples (No User Costs)  Project B:  Construction $350k  Prevent. Maint. @ Yr 8 $40k  Major Rehab @ Yr 15 $300k  Prevent. Maint. @ Yr 20 $40k  Prevent. Maint. @ Yr 25 $60k  Salvage@ 30 $105k  NPV $610k  Project A:  Construction $500k  Prevent. Maint. @ Yr 15 $40k  Major Rehab @ Yr 20 $300k  Salvage@ 30 $150k  NPV $705k

10. What’s Missing?  Note LCC for infrastructure generally does not consider any ‘pure benefits’ of using it  Its presumed that all alternatives would yield similar/equal value  This is usually the case, but could be affected by design or budget constraints (e.g. a 2 vs 4-lane road or bridge)

11. An Energy Example  Could consider life cycle costs of people using electricity in Texas  Assume coal-fired power plants used  Coal comes from Wyoming  Option 1 (current): coal mined, sent by train to Texas, burned there  Option 2: coal mined, burned in Wyoming into electricity, sent via transmission line to Texas  Which might be cheaper in cost? What are components of cost that may be relevant? Are there other ‘user costs’?

12. Reliability-Based Management  From Frangopol (2001) paper  “Funds are scarce, need a better way”  Have been focused on “condition-based”  Unclear which method might be cheaper  Bridge failure led to condition assessment/NBI methods  Which emphasized need for 4R’s  Eventually money got more scarce  Bridge Management Systems (BMS) born  PONTIS, BRIDGIT, etc.  Use deterioration and performance as inputs into economic efficiency measures

13. BMS Features  Elements characterized by discrete condition states noting deterioration  Markov model predicts probability of state transitions (e.g. good-bad-poor)  Deterioration is a single step function  Transition probabilities not time variant

14. Reliability Assessment  Decisions are made with uncertainty  Should be part of the decision model  Uses consideration of states, distribution functions, Monte Carlo simulation to track life- cycle safety and reliability for infrastructure projects  Reliability index  use to measure safety  Excellent: State 5,  >= 9, etc.  No guarantee that new bridge in State 5!  In absence of maintenance, just a linear, decreasing function (see Fig 1)

15. Reliability (cont.)  Not only is maintenance effect added, but random/state/transitional variables are all given probability distribution functions, e.g.  Initial performance, time to damage, deterioration rate w/o maintenance, time of first rehab, improvement due to maint, subsequent times, etc..  Used Monte Carlo simulation, existing bridge data to estimate effects  Reliability-based method could have significant effect on LCC (savings) Why?