Global Infrastructure Stocks/ Reliability H. Scott Matthews February 12, 2004

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

Before the Paper..  Recent data from the World Bank…  Significant infrastructure differences

Expectations  So we don’t lose sight of global relevance of these issues..  Data on previous slide implies WHAT?  Expect less economic output  Lower educational levels  Cause or effect?

Canning Paper  “A Database of World Infrastructure Stocks, 1950–95”, David Canning, World Bank Paper #1929, June 1998  Main stock dataset available on web  152 countries, generally 45 yrs  Some countries no data until recently  What is/is not included in data?

Measures in Dataset  Roads, Paved Roads (km)  Railway lines (km)  Number of telephones  Number of telephone main lines  KW electricity generating capacity  Some infrastructure quality measures  Condition of roads, Percent dropped calls, electricity system losses  What could this data be used for?

Sample Data - Electricity  US capacity 80 TW 1950  700 TW 1995 (~10x increase)  World capacity 200 -> 2500  So what?  Do these numbers tell us anything important?  What kind of values would we want instead?

Population Growth

Canning Paper  Econometric study of infrastructure stocks as related to:  Economic growth  Population Change  Investment  ‘Full report’ available on web:  Workpapers/WPS1900series/wps1929/wps1929- abstract.html (bottom of this page)

Conclusions  Non-transportation infrastructure stocks tend to increase 1:1 with population  Increase more than 1:1 with per-cap GDP  Geographic factors appear to affect provision of non-trans in poor countries  But not in rich countries  Transport. Infras. increases less than 1:1 with population  Increases with income only after threshold reached  Do these conclusions surprise us?

Life Cycle Costing and Reliability

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

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?

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

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)

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-hour estimate used  $12-$25 for cars/big trucks gets used

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

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)

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’?

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

Current 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

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)

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?