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Transcript of The National Academies Keck Center, Washington DC March 10, 2011 1.
NCHRP 08-71Methodology 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 1
Session 1
How to Use the Guidebook
2
Who should use the Guide
Roles in asset management
Elected OfficialsGovernor • legislature • county commissioners • city council
Appointed OversightTransportation commission • MPO board
Funding BodiesFHWA • FTA
The Public
Interest GroupsHighway users • homeowners associations • business groups • constituency groups
Senior managementExecutives • Districts • Modal units • Engineering disciplines • Planning • Design • Maintenance
Engineering staffProject engineers and managers • pavement surveys • materials/research • bridge design/rating • bridge inspection
Maintenance staffMaintenance engineers/managers • Facility managers • Maintenance crew leaders • Emergency response
Planning and supportBudget/finance • Program management • Strategic planning • Public information • Information technology
Asset management leadershipAsset management director • Bridge management engineer •Pavement management engineer
Outside stakeholders Internal participants
3
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
4
Evaluate and refineAssess quality, sensitivityImprove model realism
Develop applicationsPrepare user groupPrototype applicationsPilot test and evaluate toolsRefine and roll outDocument tools, procedures
Develop foundation toolsPrototype lifespan calculationsEvaluate prototype resultsRefine computationsImplement foundation toolsDocument methods and tools
Establish the frameworkDefine performance measuresConceptualize the analysisDetermine data requirementsMock up tools and reportsGain buy-in, build expectations
Plan for implementationDocument business processesPlan the change strategyList desired reports and toolsDefine work plan, resourcesSet quality metrics, milestones
Define the scopeSet goals and objectivesIdentify desired applicationsIdentify network of interestIdentify asset typesAssess gaps and readiness
12
3
45
6
Planning
Development
How to use this guide
How to plan life expectancy models
How to designlife expectancy models
How to computelife expectancy models
How to apply life expectancy models
How to improvelife expectancy models
Prolong implementationMeasure, promote successAdd to management systems
7
How to perpetuatelife expectancy models
Structure of the Guide
5
Potential goals and objectives
6
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
Potential applications
Life expectancy if no maintenance Life expectancy under a proposed maint policy Life extension effects of preservation actions Compare preservation alternatives
7
Optimal replacement interval Optimal preventive maintenance interval Optimal expenditure on periodic maint Scope and timing to maximize life extension
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
8
Multi-objective prioritization Funding allocation and effect of
budget cuts Select treatment application
policies Establish research priorities
Scope of the effort
Start small, build incrementally
Expansion to agency-wide and to partner agencies
Statewide limited rolloutPilot test or
experimental application
Prototypeor proof-of-
concept
9
Assess gaps and readiness
Asset management maturity scale
Maturity Level
Generalized Description
Initial No effective support from strategy, processes, or tools. There can be lack of motivation to improve.
Awakening Recognition of a need, and basic data collection. There is often reliance on heroic effort of individuals.
Structured Shared understanding, motivation, and coordination. Development of processes and tools.
Proficient Expectations and accountability drawn from asset management strategy, processes, and tools.
Best Practice Asset management strategies, processes, and tools are routinely evaluated and improved.
change
10
Self-assessment topicsPart A. Policy Guidance. How does policy guidance benefit from improved asset management practice?
Policy guidance benefitting from good asset management practice Strong framework for performance-based resource allocation Proactive role in policy formulation
Part B. Planning and Programming Do Resource allocation decisions reflect good practice in asset management?
Consideration of alternatives in planning and programming Performance-based planning and a clear linkage among policy, planning and programming Performance-based programming processes
Part C. Program Delivery Do program delivery processes reflect industry good practices?
Consideration of alternative project delivery mechanisms Effective program management Cost tracking and estimating
Part D. Information and Analysis Do information resources effectively support asset management policies and decisions?
Effective and efficient data collection Information integration and access Use of decision-support tools System monitoring and feedback 11
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
12
Questions?Discussion
13
Session 2
Plan for Implementation
14
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.
15
What to expect
Credible long-term view of asset performance
Accountability (benefits and fears)Tangible levels of serviceUnderstanding of deterioration and growthOptimal preservation Improved competitiveness for fundingConstructive political relationships
Be ready to follow through to win these benefits
16
Document relevant business processes
Why?Ensure the tools are relevantUnderstand how they will be
usedBuild the right tools for the jobSelect appropriate
methodsHelp others understandGain buy-in
Identify assets needing work
Develop work packages as
projects
Prioritize and schedule
Assess data quality
Monitor performance
Set minimum tolerable performance
Develop deterioration models
Develop lifeexpectancy models
Select rehabilitation actions
Design rehabilitation actions
Prioritize for further development
Developcost models
Develop effectiveness models
Develop corridor plans
Evaluate market conditions
Find economiesof scale
Evaluate equity
Evaluatefiscal uncertainty
Negotiate with funding bodies
Plan for delivery
Develop budget constraints
Develop performance targets
STIP
Designs
Lettings
Needs
Corridorplans
Inspectreports
AnnualReports
Projectplans
Gather data:Inventory • GeodataCondition • Traffic
Risk • Safety
17
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
18
Planning technical implementation
1. Data acquisition and management
2. Plan foundation analysis methods
3. List/describe applications and reports
4. Write a work plan5. Set quality metrics
and milestones19
Databases used in life expectancy
Geo-referencingTraffic countsCrashesAsset inventoryAsset conditionAsset vulnerabilityClimateSoils
NOAA Climate Divisions
20
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 characteristics21
Describe applications and reports
Considerations:Subject matter
FilteringAggregationSortingGraphics
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Pro
bab
ility
Age
Cumulative
This year
Average
22
Example work planTask 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.
23
Questions?Discussion
24
Session 3
Establishing the Framework
25
Life expectancy estimation based on replacement intervals
Value
Age
Straight-line depreciation
Value
Age
Interval replacement
Prematurefailure
26
Life expectancy estimation based on asset condition/performance
Performance, condition, or
value
Age
Deterioration model
End-of-lifethreshold
Performance, condition, or
value
Age
Decision-sensitive
End-of-lifethreshold
27
Life expectancy as a measure of investment benefit
Performance or condition
Age
Replacement: Extended life = 10 years, Cost = $100,000
End-of-lifethreshold
You are here(end of 10 year life)
Repair: Extended life = 4 years, Cost = $50,000
28
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.
29
Defining end-of-life
Performance, condition, or
value
Age
Sudden failure
Performance, condition, or
value
Age
Obsolescence due to raised standard
Standard
30
Defining end-of-life
Performance, condition, or
value
Age
End-of-life defined by age
Performanceunknown, notmeasured, ordoesn't matter
Remaining capacity, stock,
or value
Age
End-of-life based on utilization
Consumption or utilization rate
31
Defining end-of-life
Probability of failure
Age
Median time to failOptimal
replacement interval
Probabilisticend-of-life
Pavementcondition
Age
End-of-life from terminal criteria
End-of-lifethreshold
Cracking
Roughness
First to fail
32
Coordinating lifespans of asset components
Bridge condition
Age
End-of-lifethreshold
Substructure rehab adds 10 more years, allows full utilization of the third deck
Normal substructure life expectancy 50 years
Normal deck life expectancy 20 years
33
Planning component life based on functional life
Bridge condition,
performance
Age
End-of-lifethreshold
Traffic forecast calls for unacceptable level of service after 30 years
Plan for two deck rehab projects to extend deck life until ready for replacement
34
Life extension
Condition
Age
End-of-lifethreshold
Current conditionRemaining service life
Life extension
35
Role of uncertainty in program planning
Probability of failure
Age
Median time to fail (life expectancy) = 12 years
20% will have failed by 10 years Program period
ends at 10 years
36
Forecasting life expectancyTechniques 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
37
Types of models
Performance, condition, or
value
Age
ContinuousDeterministic
Performance, condition, or
value
Age
DiscreteDeterministic
Performance, condition, or
value
Age
ContinuousProbabilistic
Performance, condition, or
value
Age
DiscreteProbabilistic
38
Data collection
Visual inspection (100% sample)
10% sample of road segments
Automated data collection39
Example report/app mockups
Digital dashboards
40
Example report/app mockups
Using Excel for report mock-ups
41
Example report/app mockups
Using Excel for
application developme
nt
42
Questions?Discussion
43
Session 4
Developing Foundation Tools
44
Presentation Outline
What to Model Influence of Framework
Model Selection Selection Criteria Data Availability Nature of Prediction and Outcome
Estimation Techniques Regression Survival Models Markov Chains
What to Model
Model
Selection
Estimation
Techniques
Conclusion
45 45
Defining End-of-Life
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
What to Model
Model
Selection
Estimation
Techniques
Conclusion
46
Interval-based
Two general approaches Interval-based
▪ Predict time until end-of-life event occurs
▪ Directly predict life based on historical replacement intervals
47
Reconstruction, Y Construction, X
Service Life
Year TX Year
Year TY
What to Model
Model
Selection
Estimation
Techniques
Conclusion
47
Condition-based
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
Performance, condition, or
value
Age
Deterioration model
End-of-lifethreshold
Performance, condition, or
value
Age
Decision-sensitive
End-of-lifethreshold
What to Model
Model
Selection
Estimation
Techniques
Conclusion
48
Model Selection Criteria
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
What to Model
Model
Selection
Estimation
Techniques
Conclusion
49
Data Availability
Model Selection depends on Data Availability
▪ Historical Service Life▪ Dominating end-of-life condition preferred
▪ Condition Data by Age▪ Archived Data Preferred
50
What to Model
Model
Selection
Estimation
Techniques
Conclusion
50
Continuous vs. Discrete
Model Selection depends on Nature of Dependent Variable
▪ Continuous Variable▪ Time until rationale event occurs▪ Performance Measures (e.g., IRI, Rutting, NBI Sufficiency Rating)
▪ Discrete Variable▪ Performance Measures (e.g. NBI element Condition Rating, PSI)
51
What to Model
Model
Selection
Estimation
Techniques
Conclusion
51
Deterministic vs. Probabilistic
Model Selection depends on Nature of End Result
▪ Deterministic
▪ Probabilistic
52
Performance, condition, or
value
Age
ContinuousPerformance,
condition, or value
Age
Discrete
Performance, condition, or
value
Age
ContinuousPerformance,
condition, or value
Age
Discrete
What to Model
Model
Selection
Estimation
Techniques
Conclusion
52
Interpreting Probability
Probabilistic estimates can be represented by Density functions
53
What to Model
Model
Selection
Estimation
Techniques
Conclusion 0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
30 35 40 45 50 55 60
Prob
abili
ty
Service Life in years
Probability Density Function
Median
Confidence Interval
53
Interpreting Probability
Probabilistic estimates can be represented by Survival or Cumulative functions
▪ Survival Prob. = 1 - Cum. Prob.
54
What to Model
Model
Selection
Estimation
Techniques
Conclusion0.00.10.20.30.40.50.60.70.80.91.0
30 35 40 45 50 55 60
Prob
abili
ty o
f Pas
sing
Service Life in years
Survival Function
Median
Confidence Interval
54
Techniques
Basic Techniques Deterministic
▪ Regression (Continuous Data) Probabilistic
▪ Simple Average (Continuous Data)▪ Survival Models (Continuous Data)▪ Markov Chains (Discrete Data)
Alternatively, may be forced to rely on published life expectancy values or expert opinion
55
What to Model
Model
Selection
Estimation
Techniques
Conclusion
55
Simple Average
Data requirements Requires historical replacement data Does not require explanatory factors
Method Fits distributions to groups of assets
based on average and standard deviation of data
56
What to Model
Model
Selection
Estimation
Techniques
Conclusion
Average age at replacementa is culvert age, N is number of culverts
Population standard deviation(use if list is w hole population)
Sample standard deviation(use if list is a random sample)s is an estimate of σ
N
iia
Na
1
1
N
ii aa
N 1
21
N
ii aa
Ns
1
2
1
1
56
Simple Average
Example Demonstration
57
What to Model
Model
Selection
Estimation
Techniques
Conclusion
57
Ordinary Regression
Data requirements Requires
▪ Historical replacement data or Continuous performance/condition data & age
▪ Set of independent, explanatory factors Method
Predicts dependent variable as a function of explanatory factors▪ E.g., predict life as a function of traffic
volume, maintenance history, material type, climate conditions, etc.
58
What to Model
Model
Selection
Estimation
Techniques
ConclusionLife Prediction
nn XbXbXbt 2211
58
Ordinary Regression
Example Demonstration
59
What to Model
Model
Selection
Estimation
Techniques
Conclusion
59
Cox Regression
Data requirements Historical replacement data or
Time until end-of-life criteria reached
Set of independent, explanatory variables
Method Predicts survival curve (% assets
passing beyond point in time) as a function of explanatory variables
No assumption of statistical distribution
Median life = 50% survival probability60
What to Model
Model
Selection
Estimation
Techniques
Conclusion
Probability of Passing
nng XbXbXbgy 22111 exp/0.1exp
60
Cox Regression
Example Demonstration
61
What to Model
Model
Selection
Estimation
Techniques
Conclusion
61
Quick-and-Simple Weibull
Data requirements Historical replacement data or
Time until end-of-life criteria reached Method
Predicts survival curve (% assets passing beyond point in time)
Probabilities governed by Weibull distribution (or Markov/Exponential model if shape parameter = 1)
Median life = 50% survival probability
62
What to Model
Model
Selection
Estimation
Techniques
Conclusion
Probability of Passing
/0.1exp1 gy g
62
Example Demonstration
63
Quick-and-Simple Weibull
What to Model
Model
Selection
Estimation
Techniques
Conclusion
63
Weibull Regression
Improves upon Quick-and-Simple Weibull technique by adjusting predictions to a set of independent, life expectancy factors
Example Demonstration
64
What to Model
Model
Selection
Estimation
Techniques
Conclusion
Probability of Passing
nng XbXbXbgy 22111 exp/0.1exp
64
Intro to Markov Chains
Common technique for predicting the probability of being in any discrete condition state at any point in time
65
What to Model
Model
Selection
Estimation
Techniques
Conclusion
Good Fair Poor
Pii ≡ Probability of staying in same condition state i after unit time
Pij ≡ Probability of transitioning from state i to a worse condition state j after unit time
65
Intro to Markov Chains
Probabilities represented in matrix form
66
What to Model
Model
Selection
Estimation
Techniques
Conclusion
Markov transition probability matrixStateToday Good Fair Poor
Good 95.3 4.6 0.1Fair 0 93.2 3.9Poor 0 0 100.0
State probability in one year
Good Fair Poor
PGG=95.3
PGF=4.6
PGP=0.1
PFF=93.2 PPP=100.0
PFP=3.9
66
Quick-and-Dirty Markov Chain
Data Requirements Pairs of inspection Data with Discrete
Condition Rating
Method Estimate transition probability between 2
states: ‘failed’ and ‘not failed’ Compares % assets in each condition state
from one year to the next Median Life taken as
67
What to Model
Model
Selection
Estimation
Techniques
Conclusion
67
Example Demonstration
68
Quick-and-Dirty Markov Chain
What to Model
Model
Selection
Estimation
Techniques
Conclusion
68
Markov Chain
Similar to Quick-and-Dirty but now analyzes multiple (>2) states
Data Requirements Transition probabilities by way of expert
opinion, observed frequency, optimization, one-step process, etc.
Method Probabilistic estimate of condition states by
age Median Life = 50% assets in threshold state
69
What to Model
Model
Selection
Estimation
Techniques
Conclusion
Probability of state k next year: for all k
j is the condition state this year and x is the fraction in state jp is the transition probability from j to k
j
jkjk pxy
69
Example Demonstration
70
Markov Chain
What to Model
Model
Selection
Estimation
Techniques
Conclusion
70
One-Step Process
Data Requirements Pairs of inspection Data with Discrete
Condition Rating
Method Predicts transition probabilities by comparing
% assets in a condition state at the end of the year to that at the beginning of the year
Assumes condition state never drops more than one step per year
Life prediction same as previous example
71
What to Model
Model
Selection
Estimation
Techniques
Conclusion
71
Example Demonstration
72
One-Step Process
What to Model
Model
Selection
Estimation
Techniques
Conclusion
72
Equivalent Age Markov
Data Requirements Transition probabilities by way of expert
opinion, observed frequency, optimization, one-step process, etc.
Method Predict age as a function of condition Calculate condition index weighted by time
spent in condition state or lower state Approach converts a Markov model into a
Weibull model
73
What to Model
Model
Selection
Estimation
Techniques
Conclusion)log(
)5.0log(
jjj p
t g is equivalent ageCI is condition index
CI
glnlog
^10
73
Example Demonstration
74
Equivalent Age Markov
What to Model
Model
Selection
Estimation
Techniques
Conclusion
74
Conclusion
▪ End-of-Life Definition(s) Needed
▪ Interval- or Condition-based Approaches
▪ Selected Models should be transparent, applicable, and focused
▪ Selection influenced by nature of dependent variable and estimate
▪ Basic modeling techniques include▪ Regression▪ Survival Models▪ Markov Chains
What to Model
Model
Selection
Estimation
Techniques
Conclusion
75
Questions?Discussion
76
Session 5
How to apply the life expectancy models
77
Presentation Outline• Life Expectancy Estimates from
Deterioration Model • Additional Building Blocks for Life
Expectancy Application • Example Applications• User Groups• Conclusion
Deterioration Model
Building Blocks
Example
Role of Users
Conclusion
78
APPLYING THE MODELS
Deterioration Model• Life expectancy estimates -- easily derived from deterioration
models• Additional tools are developed on top of life expectancy
estimate to help management decision making process
Deterioration Model
Building Blocks
Example
Role of Users
Conclusion
79
APPLYING THE MODELS
Additional Building Blocks for Life Expectancy Application
• Techniques of life expectancy analysis open the door for many useful applications to support TAM decision making, but few more building blocks are required:– Equivalent age– Life extension benefits of actions– Remaining service life– Life cycle cost models
Deterioration Model
Building Blocks
Example
Role of Users
Conclusion
80
APPLYING THE MODELS
Equivalent age
• Deterioration models often use age of an asset to forecast its condition
• However, many applications require finding out ‘equivalent age’ from known condition of an asset
Deterioration Model
Building Blocks
Example
Role of Users
Conclusion
81
APPLYING THE MODELS
Life Extension Benefits of Actions
• Effect of repair & rehabilitation actions is expressed as an improvement in condition
• Once the improved condition is forecast, we can find equivalent age, before and after the action
• The difference in age is one way of expressing the benefit of the action
Deterioration Model
Building Blocks
Example
Role of Users
Conclusion
82
Condition
Age
Change in equivalent age = Life extension benefit
Life extension action improves condition
APPLYING THE MODELS
Remaining Service Life
• Computed by subtracting actual age of an asset from its life expectancy (provided no repair was done)
• If an asset has been repaired, it is more accurate to use a condition-based approach (i.e., taking advantage of deterioration and equivalent age models)
Deterioration Model
Building Blocks
Example
Role of Users
Conclusion
83
Condition
Age
End-of-lifethreshold
Current condition
Remaining life
Unknown past work
• Current condition of the asset can be converted to its equivalent age, which is
then subtracted from life expectancy to estimate remaining service life
APPLYING THE MODELS
Life Cycle Cost Models
• Life cycle cost models, combined with life expectancy and deterioration models, may be used in numerous useful applications to support TAM decision making
• Few concepts associated with life cycle cost models– Time value of money– Benefit/cost ratio– Comparing alternatives using Net Present Value (NPV)– Comparing alternatives using Equivalent Uniform Annual
Cost (EUAC)– Comparing alternatives using Present Worth at Perpetuity– Comparing alternatives using Internal Rate of Return
(IRR)
Deterioration Model
Building Blocks
Example
Role of Users
Conclusion
84
APPLYING THE MODELS
Example Applications
Many useful asset management applications can be created using the building blocks discussed
– Routine preventive maintenance– Optimal replacement interval– Comparing and optimizing design alternatives– Comparing and optimizing life extension
alternatives– Pricing design and preservation alternatives– Synchronizing replacements– Effect of funding constraints– Value of life expectancy information
Deterioration Model
Building Blocks
Example
Role of Users
Conclusion
85
APPLYING THE MODELS
Routine Preventive Maintenance
• An example of comparing a preventive maintenance scenario against do-nothing scenario
Deterioration Model
Building Blocks
Example
Role of Users
Conclusion
86
Cost per lane-mile by strategyYear Routine Preventive
MaintenanceDo-Nothing
1...4 $400...8 $400...12 $400...16 $400...20 $400 $30,000...24 $30,000
APPLYING THE MODELS
Routine Preventive Maintenance (contd.)
• Let us assume, interest rate = 4%• The EUAC of the two alternatives can be compared as follows:
= $9,083/lane-mile
= $596/lane-mile
= $768/lane-mile
• In this example, the agency could reduce annual costs by $172 per lane-mile if routine preventive maintenance is completed
Deterioration Model
Building Blocks
Example
Role of Users
Conclusion
87
APPLYING THE MODELS
Optimal Replacement Interval
• Assets may have a number of service life alternatives, depending on different strategies for maintenance and life extension
• Optimal service life would be the life cycle activity profile that can be sustained at minimum life cycle cost
• Here is an example of comparing several alternative profiles
Deterioration Model
Building Blocks
Example
Role of Users
Conclusion
88
Option 1 Option 2 Option 3 Option 4Replacement Cost 600 600 600 600Rehabilitation Cost 200 200 200 200
Annual Maintenance Cost 5 5 5 5Estimated service life (N) 50 60 70 80Rehabilitation years 25 25 25 20
40 45 45 4055 60
Interest rate 0.05 0.05 0.05 0.05
Compounded Life Cycle Cost $7884 $12727 $21146 $35411
Present Worth at Perpetuity $753 $720 $719 $729
APPLYING THE MODELS
Optimal Replacement Interval (contd.)
• Plot suggests that options 2& 3 are preferred and the optimal interval for replacement is between 60-70 years
Deterioration Model
Building Blocks
Example
Role of Users
Conclusion
89
40 45 50 55 60 65 70 75 80 85700
710
720
730
740
750
760
Option 1
Option 2 Option 3
Option 4
Replacement cycle (year)
Pre
sen
t w
ort
h a
t P
erp
etu
ity
($10
00)
APPLYING THE MODELS
Comparing/optimizing Design Alternatives
• Comparing two products or methods that have different costs, different life expectancies, and different life extension possibilities
• Here is an example, deciding on whether to apply coating to a pipe culvert– a non-coated culvert, expected to survive 50 years with a
construction cost of $1000, and a coated culvert, expected to survive 56 years with a construction cost of $1200
Deterioration Model
Building Blocks
Example
Role of Users
Conclusion
90Therefore, the coated design option is preferred
APPLYING THE MODELS
Pricing Design and Preservation Alternatives
• Many agencies have active research programs to develop new and improved maintenance materials and techniques
• But, how cheap does it need to be before it’s worth using?
• The methods of life expectancy analysis can often play a part in this evaluation
– Example: To assess feasibility of switching from traditional carbon steel reinforcement bars to solid stainless steel reinforcement bars
Deterioration Model
Building Blocks
Example
Role of Users
Conclusion
91
APPLYING THE MODELS
Pricing Design and Preservation Alternatives (contd.)
Deterioration Model
Building Blocks
Example
Role of Users
Conclusion
92
APPLYING THE MODELS
Source: Cope et al. (2011)
ILLUSTRATION: Material for bridge deck reinforcement
At what price ratio is stainless steel (SS) more cost-effective than traditional steel (TS)?
Answer: depends on service life of each alternative
FHWA Laboratory and field simulations: SS – 100 years (no deck replacement)TS – 70 years (1 deck replacement, 2 deck rehabs)
0.6
0.7
0.8
0.9
1
1.1
0 2 4 6 8 10
Rat
io o
f E
UA
C
for
Sta
inle
ss S
teel
to
T
rad
itio
nal
ste
el
Ratio of Stainless Steel Price to Traditional Steel Price
Current Ratio
ThresholdRatio
Stainless Steel is MORE cost-effective
Stainless Steel is LESS cost-effective
Effect of Funding Constraints
Deterioration Model
Building Blocks
Example
Role of Users
Conclusion
93
• Decision support tools based on life expectancy and life cycle cost can help an agency to do more with less
– Example: an agency calculated utility of a set of projects
with respect to life expectancy, deterioration, life cycle
cost, and estimated project cost. Let budget be $2.75M
Activity Utility Cost
Bridge A replacement 100 $2400k
Bridge B rehabilitation 75 $250k
Box Culvert A replacement 55 $100k
Pipe Culvert A replacement 35 $5k
Bridge C deck patching 32 $20k
APPLYING THE MODELS
Effect of Funding Constraints (contd.)
• Optimization techniques can be applied to select a set of projects (Solver option in Excel may be used)
Deterioration Model
Building Blocks
Example
Role of Users
Conclusion
94
Optimal solution: Total utility 242 at a cost $2.675M; remaining $75k to be carried over
APPLYING THE MODELS
Activity Utility Cost
Bridge A replacement 100 $2400k
Bridge B rehabilitation 75 $250k
Box Culvert A replacement 55 $100k
Pipe Culvert A replacement 35 $5k
Bridge C deck patching 32 $20k
Role of User Groups
• One of the best ways to create involvement and buy-in is to form a user group for the applications that are to be developed
• A user group should consist of people who will be hands-on users of the applications, as well as people who may receive and act on the information
Deterioration Model
Building Blocks
Example
Role of User Groups
Conclusion
95
Steering/leadership committee
President
Subcommittee
Subcommittee
Subcommittee
Outside stakeholders
Senior management
Asset management leadership
APPLYING THE MODELS
Role of User Groups (contd.)
• The user group’s tasks include planning, development, & production of different applications
• Often the user group will be large and may expand over time to include all hands-on users and many indirect users of the applications
• Once the group reaches sufficient size, it should create sub-groups to whom it delegates many of the tasks above
Deterioration Model
Building Blocks
Example
Role of User Groups
Conclusion
96
APPLYING THE MODELS
Conclusion
• An agency may launch a big system development effort to implement various applications of lifecycle estimations
• Alternatively, it can select relatively small subset of applications at first (often just one), and develop working prototype– The prototype addresses core functions, from data
collection to analysis & reports– Should gradually expand to cover more applications and
to add more features – Should identify data gaps, procedures and standards that
are required, in the context of a working application
Deterioration Model
Building Blocks
Example
Role of Users
Conclusion
97
APPLYING THE MODELS
Conclusion
– It gives users more day-to-day control and involves them more deeply in the creation of the tools they will use, thus helps avoiding “not invented here” syndrome Deterioration
Model
Building Blocks
Example
Role of Users
Conclusion
98
APPLYING THE MODELS
Questions?Discussion
99
Session 6
Accounting for Uncertainty
100
Presentation Outline
Rationale for Incorporating Uncertainty
Causes of UncertaintySensitivity AnalysisRisk Analysis
Uncertain Inputs Uncertain Outputs
Rationale
Causes
Sensitivity
Risk
Conclusion
101101
Rationale
Life expectancy estimates affect business processes
but asset life is inherently uncertain…
102
Rationale
Causes
Sensitivity
Risk
Conclusion
Human Resources
Data collectionPreservation
Planning
ProjectDevelopment
Programming
Budgeting
PreservationPolicy
NetworkPlanning
CorridorDevelopment
Design
Maintenance
Research
InformationTechnology
Finance
LifeExpectancy
Analysis
102
Causes of Uncertainty
Uncertainty result of random
103
Rationale
Causes
Sensitivity
Risk
Conclusion
Random Process Example
Structural Response Actual strength unknown due to material imperfections
Loadings Uncertainty surrounding future traffic levels and % trucks
Site Conditions Uncertain soil properties. future climate conditions, or random extreme weather events
Human Influence Unknown construction and/or inspection rating quality
Externalities Unforeseen development of new technologies or standards
103
Quantifying Uncertainty
Methods to quantify uncertainty
- Both can be used to produce ranges of life estimates
- Risk analysis additionally describes the likelihood of life estimates
104
Rationale
Causes
Sensitivity
Risk
Conclusion
Characteristic Sensitivity Analysis
Risk Analysis
Nature of Outcome
Deterministic Probabilistic
Assesses how Outcome varies due to...
Unit Changes Random Changes
104
Sensitivity Analysis
Benefits Identify most influential factors
Guide design selections Assess potential life extensions
Plan for mitigation
105
Rationale
Causes
Sensitivity
Risk
Conclusion
105
Sensitivity by Model Selection
Analysis varies by model selection For models without explanatory
variables, can assess how life prediction varies for different groupings of assets
106
Rationale
Causes
Sensitivity
Risk
Conclusion
Markov Chains
Quick & Simple Weibull
Simple Average
106
Sensitivity by Model Selection
Analysis varies by model selection For models with explanatory variables,
can assess how life prediction changes when vary factors over a range of values
107
Rationale
Causes
Sensitivity
Risk
Conclusion
Cox Regression
Weibull Regression
Ordinary Regression
107
Sensitivity by Model Type
Analysis varies by model type
For Ordinary Regression▪ Unit Δ in factor = β Δ in life prediction
For Cox Regression models▪ Unit Δ in factor = exp(β) % Δ in Hazard
Ratio
For Weibull Regression models▪ Unit Δ in factor = exp(β) % Δ in Average
Life
where β represents the parameter estimate 108
Rationale
Causes
Sensitivity
Risk
Conclusion
108
Showing Sensitivity
Tornado Diagram Representation
109
Rationale
Causes
Sensitivity
Risk
Conclusion
Factor 1
Factor 2
Factor n
Δ in Life Predictions
.
.
.
.
.
.
.
.
Increase in Factor leads to a Decrease in Life
Increase in Factor leads to an Increase in Life
Increasing Influence on Life
109
Example Sensitivity Analysis
Example Demonstration
110
Rationale
Causes
Sensitivity
Risk
Conclusion
110
Probabilistic Techniques
To mitigate uncertainty, probabilistic techniques emphasized Describe likelihood of life expectancy
and related business processes Ranges of life produced by level of
confidence (μ point estimate)
111
Rationale
Causes
Sensitivity
Risk
Conclusion
Probability of failure
Age
Median time to fail (life expectancy) = 12 years
20% will have failed by 10 years
Program period ends at 10 years
111
Risk Analysis
Risk Identification
Describe Likelihood and Consequence of Risk
Risk Assessment
Quantify Likelihood and Consequence
Risk Management
Decide on Mitigation Strategy
Risk Monitoring
Monitor Effectiveness of Strategy
112
Rationale
Causes
Sensitivity
Risk
Conclusion
112
Risk Assessment
Risk Assessment Process
<Van Dorp, 2009 – GWU>
113
Rationale
Causes
Sensitivity
Risk
Conclusion
X
Y
Z O
Step 1: Quantify uncertainty surrounding life expectancy factors (e.g., climate conditions, traffic loading) using probability distributions
113
Risk Assessment
Risk Assessment Process
<Van Dorp, 2009 – GWU>
114
Rationale
Causes
Sensitivity
Risk
Conclusion
X
Y
Z O
Step 2: Randomly sample input distributions and calculate life using the calibrated model
114
Risk Assessment
Risk Assessment Process
<Van Dorp, 2009 – GWU>
115
Rationale
Causes
Sensitivity
Risk
Conclusion
X
Y
Z O
Step 3: Assess the distribution of life estimates 115
Example Risk Analysis
Suppose an agency is interested in the risk of potential climate change on business processes
Propagating
Uncertainty116
Rationale
Causes
Sensitivity
Risk
Conclusion
Climate ServiceLife
Annual Costs
Budget Needs
Project Utility
116
Example Risk Analysis
Assume 30 year old bridge asset with following characteristics: Normal annual temperature (°F) = 49 Normal annual precipitation (in.) = 43 Part of NHS system Non-Corrosive Soil Steel, girder bridge 50 feet long Wearing Surface Applied $50k Replacement Cost 4% Interest Rate
117
Rationale
Causes
Sensitivity
Risk
Conclusion
117
Example Risk Analysis
Assess how life changes due to uncertain climate
<ICF International, 2009 in CCSP, 2009>
118
Rationale
Causes
Sensitivity
Risk
Conclusion
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 2 4 6
Prob
abili
ty
Δ in Temperature (°F)
Δ in Temperature Forecasts
Low Emissions
Moderately High Emissions
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
-15 -10 -5 0 5 10 15 20
Prob
abili
ty
Δ in Precipitation (in.)
% Δ in Precipitation Forecasts
Low Emissions
Moderately High Emissions
118
Example Risk Analysis
After 2,500 Simulations
119
Rationale
Causes
Sensitivity
Risk
Conclusion
0.00.10.20.30.40.50.60.70.80.91.0
0 20 40 60 80 100
Prob
abili
ty
Median Life (years)
Uncertain Survival for Low Emissions
Confidence Interval
Expected
0.0
0.2
0.4
0.6
0.8
1.0
0 20 40 60 80 100
Prob
abili
ty
Median Life (years)
Uncertain Survival for Moderately High Emissions
Confidence Interval
Expected
119
Example Risk Analysis
Median Life Current: 50 years 90%
CI Low Emissions: 50 yrs [46,53] Mod. High Emissions: 49 yrs [45,54]
120
Rationale
Causes
Sensitivity
Risk
Conclusion
0.000.020.040.060.080.100.120.140.160.180.20
40 45 50 55 60
Prob
abili
ty
Median Life (years)
Uncertain Median Life
Low Emissions
Moderately High Emissions
120
Example Risk Analysis
Uncertain Life Uncertain EUAC Current: $2,328 90%
CI Low: $2,328
[$2,286,$2,394] Mod. High: $2,343
[$2,273,$2,394]
121
Rationale
Causes
Sensitivity
Risk
Conclusion
0.000.020.040.060.080.100.120.140.160.180.20
$2,200 $2,300 $2,400 $2,500
Prob
abili
ty
EUAC ($)
Uncertain Median EUAC
Low Emissions
Moderately High Emissions
121
Example Risk Analysis
Probability Future Average Life or EUAC < Current Average Life or EUAC
Low Emissions: 48.8% chance
Mod. High Emissions: 51.6% chance
122
Rationale
Causes
Sensitivity
Risk
Conclusion
122
Example Risk Analysis
Suppose assessing needs for 10 year planning horizon If assumed 50 year life then would not set
aside funds for 30 year old bridge If consider risk of ‘failure’ then would
expect to needP(‘Failure’ within planning
horizon)*Cost[1-S(30+10)] * Replacement Cost= $16,712 for Low Emissions= $16,917 for Moderately High
Emissions123
Rationale
Causes
Sensitivity
Risk
Conclusion
123
Example Risk Analysis
Risk of programming the wrong project
Assume ranking based decision on ΔURSL
124
Rationale
Causes
Sensitivity
Risk
Conclusion
𝑈= 1.1659∗ሾ1−𝐸𝑋𝑃ሺ−0.0195∗𝑅𝑆𝐿2ሻሿ
00.10.20.30.40.50.60.70.80.9
1
0 2 4 6 8 10
Util
ity
Remaining Service Life in Years
124
Example Risk Analysis
Suppose ranking replacement projects for 10 year planning horizon based solely on RSL If assumed 50 year life then would
estimate no utility for replacing 30 year old bridge
Considering risk for either emission scenario…
125
Rationale
Causes
Sensitivity
Risk
Conclusion
Expected ΔU P(Max Benefit)
P(Benefit)
+25 18.0% 31.7%
125
Example Risk Analysis
Full Demonstration of Example
Programmed into Spreadsheet
126
Rationale
Causes
Sensitivity
Risk
Conclusion
126
Conclusion
▪ Need to incorporate uncertainty
▪ Causes of uncertainty▪ Methods for assessing uncertainty
▪ Sensitivity Analysis▪ Risk Analysis
▪ Recommended to move towards probabilistic planning and management framework127
Rationale
Causes
Sensitivity
Risk
Conclusion
127
Questions?Discussion
128
Session 7 Ensuring Implementation
129
Measuring success
To how many of these can you answer “yes”?
LONG TERM VIEW Does the agency now feel confident in publishing life
expectancy estimates, and using them to evaluate and anchor budgetary requests?
Do senior managers have confidence that they know how much it will cost in the long term to sustain the desired level of service?
Do outside stakeholders agree with management estimates of the long-term cost of sustaining the desired level of service?
Do senior managers and stakeholders know what level of service can be sustained under current or proposed future funding levels? 130
Measuring success
To how many of these can you answer “yes”?
TRANSPARENCY
Is there a public comparison of forecast vs actual life expectancies?
Are actions taken in response to life expectancy estimates and findings, and do stakeholders know what these actions are?
Are comparisons routinely and publicly made of the agency’s performance against peer agencies, and against itself over time?
131
Measuring success
To how many of these can you answer “yes”?
LEVELS OF SERVICE
Can the agency accurately measure, track, and publish the level of service it is currently providing?
Are life extension and replacement decisions accurately timed to avoid interruptions in service while minimizing costs?
Is the agency reducing the annual number of traffic disruptions due to planned and unplanned maintenance, repair, and replacement activity?
132
Measuring success
To how many of these can you answer “yes”?
EFFICIENCY
Is the agency improving in its quantitative performance, in relation to the cost of providing the desired levels of service?
Can the agency show, from its actual data, that its more refined timing of life extension and replacement actions is saving money, relative to earlier practice?
Does the agency routinely compute, and effectively communicate, the life cycle costs of its services? Are these costs showing a clear trend of improvement?
133
Measuring success
To how many of these can you answer “yes”?
AGENCY COMPETITIVENESS
Is the agency using its asset management information as a competitive weapon to secure adequate funding?
Are legislators confident that the agency is doing everything it can to control costs?
Is the agency able to maintain adequate funding levels over time, in the face of competing uses of the money?
134
Measuring success
To how many of these can you answer “yes”?
CONSTRUCTIVE RELATIONSHIPS
Is the agency working actively with outside stakeholders on strategies to maintain and enhance the level of service provided to the public?
Do outside stakeholders understand how their own interests are served by maintaining the agency’s level of service objectives?
Do legislators and funding bodies rely on the agency’s models of the relationship between level of service and funding?
135
Questions?Discussion
136