The National Academies
Keck Center, Washington DC
March 10, 2011
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Outside stakeholders
Internal participants
Senior management
Executives • Districts •
Modal units • Engineering disciplines •
Planning • Design • Maintenance
The Public
Elected Officials
Governor • legislature • county
commissioners • city council
Appointed Oversight
Transportation commission •
MPO board
Planning and support
Asset management leadership
Asset management director •
Bridge management engineer •
Pavement management engineer
Funding Bodies
FHWA • FTA
Interest Groups
Highway users • homeowners
associations • business groups •
constituency groups
Budget/finance • Program
management • Strategic
planning • Public information •
Information technology
Maintenance staff
Maintenance engineers/
managers • Facility managers •
Maintenance crew leaders •
Emergency response
Engineering staff
Project engineers and managers •
pavement surveys • materials/research •
bridge design/rating • bridge inspection
Roles in asset management
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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
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1
Define the scope
Set goals and objectives
Identify desired applications
Identify network of interest
Identify asset types
Assess gaps and readiness
Planning
2
How to use this guide
Plan for implementation
Document business processes
Plan the change strategy
List desired reports and tools
Define work plan, resources
Set quality metrics, milestones
3
How to plan
life expectancy models
Establish the framework
Define performance measures
Conceptualize the analysis
Determine data requirements
Mock up tools and reports
Gain buy-in, build expectations
How to design
life expectancy models
4
Develop foundation tools
Prototype lifespan calculations
Evaluate prototype results
Refine computations
Implement foundation tools
Document methods and tools
How to compute
life expectancy models
Development
5
Develop applications
Prepare user group
Prototype applications
Pilot test and evaluate tools
Refine and roll out
Document tools, procedures
How to apply
life expectancy models
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Evaluate and refine
Assess quality, sensitivity
Improve model realism
How to improve
life expectancy models
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Prolong implementation
Measure, promote success
Add to management systems
How to perpetuate
life expectancy models
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Justify maintenance funding
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Plan timing and scope of actions
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Plan staffing and equipment

Set inventory levels
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Evaluate new materials, methods
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Reduce workzone frequency
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Improve consistency of reports
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Optimize cash flow
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Build credibility
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Life expectancy if no maintenance
Life expectancy under a proposed maint policy
Life extension effects of preservation actions
Compare preservation alternatives
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Optimal replacement interval
Optimal preventive maintenance interval
Optimal expenditure on periodic maint
Scope and timing to maximize life extension
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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
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Start small, build incrementally
Prototype
or proof-ofconcept
Pilot test or
experimental
application
Statewide limited rollout
Expansion to agency-wide
and to partner agencies
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Asset management maturity scale
Maturity
Level
Generalized Description
Initial
No effective support from strategy, processes, or tools. There can be
lack of motivation to change
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.
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Part 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
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Get ducks in a row
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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
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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.
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Credible long-term view of asset performance
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Accountability (benefits and fears)
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Tangible levels of service
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Understanding of deterioration and growth
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Optimal preservation

Improved competitiveness for funding
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Constructive political relationships
Be ready to follow through to win these benefits
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Why?
 Ensure the tools are relevant
 Understand how they will be used
 Build the right tools for the job
 Select appropriate
methods
 Help others understand
 Gain buy-in
Gather data:
Inventory • Geodata
Condition • Traffic
Risk • Safety
Inspect
reports
Assess data quality
Set minimum tolerable
performance
Prioritize for further
development
Develop deterioration
models
Identify assets
needing work
Needs
Develop life
expectancy models
Design rehabilitation
actions
Develop corridor plans
Evaluate market
conditions
Find economies
of scale
Corridor
plans
Develop
cost models
Develop work
packages as
projects
Develop effectiveness
models
Project
plans
Designs
Develop budget
constraints
Evaluate equity
Evaluate
fiscal uncertainty
Negotiate with
funding bodies
Prioritize and
schedule
STIP
Monitor
performance
Select rehabilitation
actions
Develop performance
targets
Plan for
delivery
Lettings
Annual
17Reports
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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
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Make sure staff are involved and empowered
Show short-term successes
Keep the focus on the change effort
Anchor new approaches into the culture
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1.
2.
3.
4.
5.
Data acquisition and management
Plan foundation analysis methods
List/describe applications and reports
Write a work plan
Set quality metrics
and milestones
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Geo-referencing
Traffic counts
Crashes
Asset inventory
Asset condition
Asset vulnerability
Climate
Soils
NOAA Climate Divisions
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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
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Probability
Considerations:
 Subject matter
 Filtering
 Aggregation
 Sorting
 Graphics
1.0
Average
0.9
0.8
0.7
Cumulative
0.6
This year
0.5
0.4
0.3
0.2
0.1
0.0
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60
Age
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.
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Value
Straight-line
depreciation
Interval
replacement
Value
Age
Premature
failure
Age
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Performance,
condition, or
value
Performance,
condition, or
value
Decisionsensitive
Deterioration
model
End-of-life
threshold
Age
End-of-life
threshold
Age
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Performance
or condition
Replacement: Extended life = 10 years,
Cost = $100,000
Repair: Extended life = 4 years,
Cost = $50,000
End-of-life
threshold
You are here
(end of 10 year life)
Age
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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 be
managed to maximize
agency objectives or
minimize life cycle
costs.
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Performance,
condition, or
value
Sudden
failure
Performance,
condition, or
value
Age
Standard
Obsolescence due to
raised standard
Age
30
Performance,
condition, or
value
End-of-life
defined by age
Performance
unknown, not
measured, or
doesn't matter
Remaining
capacity, stock,
or value
End-of-life based on
utilization
Age
Consumption or
utilization rate
Age
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Probabilistic
end-of-life
Probability of
failure
Optimal
replacement
interval
Pavement
condition
Median time
to fail
End-of-life from
terminal criteria
Roughness
Cracking
Age
First to fail
End-of-life
threshold
Age
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Normal deck life
expectancy 20 years
Bridge
condition
Normal substructure life
expectancy 50 years
Substructure
rehab adds 10
more years,
allows full
utilization of the
third deck
End-of-life
threshold
Age
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Bridge
condition,
performance
Plan for two deck rehab projects
to extend deck life until ready for
replacement
Traffic forecast
calls for
unacceptable
level of service
after 30 years
End-of-life
threshold
Age
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Remaining service life
Condition
Current condition
Life extension
End-of-life
threshold
Age
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Probability of
failure
20% will have failed
by 10 years
Median time to fail
(life expectancy)
= 12 years
Program period
ends at 10 years
Age
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
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
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Performance,
condition, or
value
Continuous
Deterministic
Performance,
condition, or
value
Age
Performance,
condition, or
value
Continuous
Probabilistic
Age
Discrete
Deterministic
Age
Performance,
condition, or
value
Discrete
Probabilistic
Age
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Visual inspection (100% sample)
10% sample of
road segments
Automated data collection
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Digital dashboards
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Using Excel for
report mock-ups
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Using Excel for
application
development
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What to
Model

What to Model
 Influence of Framework
Model
Selection

Model Selection
 Selection Criteria
 Data Availability
Estimation
Techniques
 Nature of Prediction and Outcome

Estimation Techniques
 Regression
Conclusion
 Survival Models
 Markov Chains
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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

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What to
Model
Model
Selection
Estimation
Techniques

Two general approaches
 Interval-based
▪ Predict time until end-of-life event occurs
▪ Directly predict life based on historical
replacement intervals
Construction, X
Reconstruction, Y
Service Life
Conclusion
Year
Year TX
Year TY
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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
Performance,
condition, or
value
Deterioration
model
End-of-life
threshold
Performance,
condition, or
value
Decisionsensitive
End-of-life
threshold
Age
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Age
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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
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What to
Model
Model
Selection
Estimation
Techniques

Model Selection depends on
 Data Availability
▪ Historical Service Life
▪ Dominating end-of-life condition preferred
▪ Condition Data by Age
▪ Archived Data Preferred
Conclusion
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What to
Model
Model
Selection
Estimation
Techniques

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)
Conclusion
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What to
Model
Model
Selection
Estimation
Techniques

Model Selection depends on
 Nature of End Result
▪ Deterministic
Performance,
condition, or
value
Continuous
Performance,
condition, or
value
Discrete
Age
▪ Probabilistic
Performance,
condition, or
value
Continuous
Age
Performance,
condition, or
value
Discrete
Conclusion
Age
Age
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What to
Model
Model
Selection

Probabilistic estimates can be
represented by
 Density functions
0.14
0.12
Estimation
Techniques
Probability
0.10
Probability Density
Function
0.08
0.06
Median
0.04
Confidence Interval
0.02
Conclusion
0.00
30
35
40
45
50
Service Life in years
55
60
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Model
Selection
Estimation
Techniques
Conclusion

Probabilistic estimates can be represented
by
 Survival or Cumulative functions
▪ Survival Prob. = 1 - Cum. Prob.
Probability of Passing
What to
Model
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
Survival Function
Median
Confidence Interval
30
35
40
45
50
Service Life in years
55
60
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What to
Model

Model
Selection
Estimation
Techniques

Conclusion
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
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What to
Model

Data requirements
 Requires historical replacement data
 Does not require explanatory factors
Model
Selection

Method
 Fits distributions to groups of assets based on average
and standard deviation of data
Estimation
Techniques
Average age at replacem ent
a is culvert age, N is number of culverts
Population standard deviation
(use if list is w hole population) 
Conclusion
Sam ple standard deviation
(use if list is a random sample)
s is an estimate of σ

s
N
1
a 
N
a
 a
a

1 N

ai  a

N  1 i 1

1
N
i 1
N
i 1
i
i
2
2
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What to Model
Model
Selection
Example Demonstration
Estimation
Techniques
Conclusion
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What to
Model

Model
Selection

Estimation
Techniques
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.
Life Prediction
Conclusion
t  b1 X
1
 b
2
X
2
 
 b
n
X
n
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What to
Model
Model
Selection
Example Demonstration
Estimation
Techniques
Conclusion
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What to
Model

Model
Selection

Estimation
Techniques
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 probability
Probability of Passing
Conclusion

y 1 g  exp  1 . 0   g /  

 exp b
1
X 1  b2 X
2
   bn X
n
60

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What to
Model
Model
Selection
Example Demonstration
Estimation
Techniques
Conclusion
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What to
Model
Model
Selection
Estimation
Techniques


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
Probability of Passing
Conclusion

y1g  exp  1.0   g /  


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What to
Model
Model
Selection
Example Demonstration
Estimation
Techniques
Conclusion
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What to
Model
Model
Selection
Estimation
Techniques

Improves upon Quick-and-Simple Weibull technique by
adjusting predictions to a set of independent, life
expectancy factors
Probability of Passing

y1g  exp 1.0  g / expb1 X1  b2 X 2   bn X n 


Example Demonstration
Conclusion
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What to
Model
Model
Selection

Common technique for predicting the probability
of being in any discrete condition state at any
point in time
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
Estimation
Techniques
Good
Fair
Poor
Conclusion
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What to
Model

Probabilities represented in matrix form
PFF=93.2
PGG=95.3
PFP=3.9
PGF=4.6
Model
Selection
Good
PPP=100.0
Fair
Poor
PGP=0.1
Estimation
Techniques
Conclusion
Markov transition probability matrix
State
State probability in one year
Today
Good
Fair
Poor
Good
95.3
4.6
0.1
Fair
0
93.2
3.9
Poor
0
0
100.0
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What to
Model

Data Requirements
 Pairs of inspection Data with Discrete Condition Rating
Model
Selection

Method
 Estimate transition probability between 2 states: ‘failed’
Estimation
Techniques
and ‘not failed’
 Compares % assets in each condition state from one
year to the next
 Median Life taken as
Conclusion
67
67
Quick-and-Dirty Markov Chain
What to
Model
Model
Selection
Example Demonstration
Estimation
Techniques
Conclusion
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What to
Model


Model
Selection

Estimation
Techniques
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
Probability of state k next year:
y k   x j p jk
for all k
j
Conclusion
j is the condition state this year and x is the fraction in state j
p is the transition probability from j to k
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Markov Chain
What to
Model
Model
Selection
Example Demonstration
Estimation
Techniques
Conclusion
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What to
Model

Data Requirements
 Pairs of inspection Data with Discrete Condition Rating
Model
Selection
Estimation
Techniques

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
Conclusion
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One-Step Process
What to
Model
Model
Selection
Example Demonstration
Estimation
Techniques
Conclusion
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What to
Model

Data Requirements
 Transition probabilities by way of expert opinion,
observed frequency, optimization, one-step process, etc.
Model
Selection

Method
 Predict age as a function of condition
 Calculate condition index weighted by time spent in
Estimation
Techniques
Conclusion
condition state or lower state
 Approach converts a Markov model into a Weibull model
log(0.5)
tj 
log( p jj )
 log lnCI

g   10^


g is equivalent age 
CI is condition index
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Equivalent Age Markov
What to
Model
Model
Selection
Example Demonstration
Estimation
Techniques
Conclusion
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What to
Model
▪ End-of-Life Definition(s) Needed
▪ Interval- or Condition-based Approaches
Model
Selection
Estimation
Techniques
▪ Selected Models should be transparent,
applicable, and focused
▪ Selection influenced by nature of dependent
variable and estimate
▪ Basic modeling techniques include
Conclusion
▪ Regression
▪ Survival Models
▪ Markov Chains
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Presentation Outline
APPLYING THE
MODELS
Deterioration
Model
Building
Blocks
Example
Role of
Users
Conclusion
• Life Expectancy Estimates from
Deterioration Model
• Additional Building Blocks for Life
Expectancy Application
• Example Applications
• User Groups
• Conclusion
78
Deterioration Model
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
Building
Blocks
Example
Role of
Users
Conclusion
79
Additional Building Blocks for Life Expectancy Application
APPLYING THE
MODELS
Deterioration
Model
Building
Blocks
Example
Role of
Users
• 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
Conclusion
80
Equivalent age
APPLYING THE
MODELS
Deterioration
Model
Building
Blocks
Example
• 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
Role of
Users
Conclusion
81
Life Extension Benefits of Actions
APPLYING THE
MODELS
Deterioration
Model
Building
Blocks
Example
Role of
Users
• 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
Condition
Change in equivalent age =
Life extension benefit
Conclusion
Life extension action
improves condition
Age
82
Remaining Service Life
APPLYING THE
MODELS
Deterioration
Model
Building
Blocks
Example
• 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)
Unknown past work
Remaining life
Condition
Role of
Users
Conclusion
Current condition
End-of-life
threshold
Age
•Current condition of the asset can be converted to its equivalent age, which is
then subtracted from life expectancy to estimate remaining service life
83
Life Cycle Cost Models
APPLYING THE
MODELS
Deterioration
Model
Building
Blocks
Example
Role of
Users
Conclusion
• 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)
84
Example Applications
APPLYING THE
MODELS
Deterioration
Model
Building
Blocks
Example
Role of
Users
Conclusion
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
85
Routine Preventive Maintenance
APPLYING THE
MODELS
Deterioration
Model
• An example of comparing a preventive
maintenance scenario against do-nothing scenario
Year
Building
Blocks
Example
Role of
Users
Conclusion
1
...
4
...
8
...
12
...
16
...
20
...
24
Cost per lane-mile by strategy
Do-Nothing
Routine Preventive
Maintenance
$400
$400
$400
$400
$400
$30,000
$30,000
86
Routine Preventive Maintenance (contd.)
APPLYING THE
MODELS
Deterioration
Model
Building
Blocks
• Let us assume, interest rate = 4%
• The EUAC of the two alternatives can be compared as follows:
= $9,083/lane-mile
Example
Role of
Users
= $596/lane-mile
Conclusion
= $768/lane-mile
• In this example, the agency could reduce annual costs by $172
per lane-mile if routine preventive maintenance is completed
87
Optimal Replacement Interval
APPLYING THE
MODELS
Deterioration
Model
Building
Blocks
• 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
Example
Role of
Users
Conclusion
Replacement Cost
Rehabilitation Cost
Annual Maintenance Cost
Estimated service life (N)
Rehabilitation years
Interest rate
Compounded Life Cycle Cost
Present Worth at Perpetuity
Option 1
600
200
Option 2
600
200
Option 3
600
200
Option 4
600
200
5
50
25
40
5
60
25
45
0.05
0.05
5
70
25
45
55
0.05
5
80
20
40
60
0.05
$7884
$12727
$21146
$35411
$753
$720
$719
$729
88
APPLYING THE
MODELS
Deterioration
Model
Building
Blocks
Example
Role of
Users
Present worth at Perpetuity ($1000)
Optimal Replacement Interval (contd.)
760
Option 1
750
740
Option 4
730
720
Option 2
Option 3
710
700
40
50
60
70
Replacement cycle (year)
80
90
• Plot suggests that options 2& 3 are preferred and the optimal
interval for replacement is between 60-70 years
Conclusion
89
Comparing/optimizing Design Alternatives
APPLYING THE
MODELS
Deterioration
Model
Building
Blocks
Example
Role of
Users
• 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
Conclusion
Therefore, the coated design option is preferred
90
Pricing Design and Preservation
Alternatives
APPLYING THE
MODELS
Deterioration
Model
Building
Blocks
• 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?
Example
Role of
Users
Conclusion
• 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
91
Pricing Design and Preservation Alternatives (contd.)
APPLYING THE
MODELS
ILLUSTRATION:
Material for bridge deck reinforcement
At what price ratio is stainless steel (SS) more cost-effective than traditional steel (TS)?
Building
Blocks
Example
Role of
Users
Conclusion
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)
Ratio of EUAC for Stainless Steel to
Traditional steel
Deterioration
Model
1.1
1
0.9
0.8
Threshold
Ratio
Current
Ratio
0.7
0.6
0
2
4
6
8
10
Ratio of Stainless Steel Price to Traditional Steel Price
Source: Cope et al. (2011)
Stainless Steel is
MORE cost-effective
Stainless Steel is
LESS cost-effective
92
Effect of Funding Constraints
APPLYING THE
MODELS
Deterioration
Model
Building
Blocks
Example
Role of
Users
Conclusion
• 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
93
Effect of Funding Constraints (contd.)
APPLYING THE
MODELS
• 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
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
Optimal solution: Total utility 242 at a cost $2.675M; remaining $75k
to be carried over
94
Role of User Groups
APPLYING THE
MODELS
Deterioration
Model
Building
Blocks
Example
• 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
Senior management
Role of User
Groups
Outside stakeholders
Asset management leadership
President
Conclusion
Steering/leadership committee
Subcommittee
Subcommittee
Subcommittee
95
Role of User Groups (contd.)
APPLYING THE
MODELS
Deterioration
Model
Building
Blocks
Example
Role of User
Groups
Conclusion
• 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
96
Conclusion
APPLYING THE
MODELS
Deterioration
Model
Building
Blocks
Example
Role of
Users
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
97
Conclusion
APPLYING THE
MODELS
Deterioration
Model
– 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
Building
Blocks
Example
Role of
Users
Conclusion
98
99
100
Rationale
Causes
Sensitivity
Risk

Rationale for Incorporating Uncertainty

Causes of Uncertainty

Sensitivity Analysis

Risk Analysis
 Uncertain Inputs
 Uncertain Outputs
Conclusion
101 101
Rationale

Life expectancy estimates affect business
processes
Causes
Network
Planning
Preservation
Policy
Finance
Budgeting
Sensitivity
Human
Resources
Corridor
Development
Programming
Risk
Design
Conclusion
Maintenance
Project
Development
Preservation
Planning
Life
Expectancy
Analysis
Information
Technology
Research
Data
collection
but asset life is inherently uncertain…
102102
Rationale
Causes
Sensitivity

Uncertainty result of random
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
Risk
Conclusion
103 103
Rationale

Methods to quantify uncertainty
Characteristic
Causes
Sensitivity
Sensitivity
Analysis
Risk Analysis
Nature of Outcome Deterministic
Probabilistic
Assesses how
Outcome varies
due to...
Random Changes
Unit Changes
Risk
Conclusion
- Both can be used to produce ranges of life estimates
- Risk analysis additionally describes the likelihood
of life estimates
104104
Rationale
Causes
Sensitivity

Benefits
 Identify most influential factors
 Guide design selections
 Assess potential life extensions
 Plan for mitigation
Risk
Conclusion
105105
Rationale
Causes

Analysis varies by model selection
 For models without explanatory variables, can assess
how life prediction varies for different groupings of
assets
Sensitivity
Risk
Conclusion
106106
Rationale
Causes

Analysis varies by model selection
 For models with explanatory variables, can assess
how life prediction changes when vary factors over a
range of values
Sensitivity
Risk
Conclusion
107 107
Rationale
Causes
Sensitivity

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
Risk
 For Weibull Regression models
Conclusion
▪ Unit Δ in factor = exp(β) % Δ in Average Life
where β represents the parameter estimate
108108
Rationale

Tornado Diagram Representation
Δ in Life Predictions
Factor 1
Factor 2
Causes
.
Sensitivity
Increasing Influence
on Life
.
.
.
Risk
.
.
Factor n
Conclusion
Increase in Factor leads to a Decrease
in Life
Increase in Factor leads
to an Increase
.
in Life
109109
Rationale
Causes
Example Demonstration
Sensitivity
Risk
Conclusion
110 110
Rationale
Causes
Sensitivity
Risk

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)
Probability of
failure
Median time to fail (life expectancy) = 12 years
20% will have failed
by 10 years
Conclusion
Program period ends at 10 years
Age
111 111
Rationale

 Describe Likelihood and Consequence of Risk
Causes

Sensitivity
Risk
Risk Identification
Risk Assessment
 Quantify Likelihood and Consequence

Risk Management
 Decide on Mitigation Strategy
Conclusion

Risk Monitoring
 Monitor Effectiveness of Strategy
112 112
Rationale

Risk Assessment Process
Causes
X
Sensitivity
Risk
Y
Step 1: Quantify
uncertainty surrounding
life expectancy factors
(e.g., climate conditions,
traffic loading) using
probability distributions
Conclusion
Z
<Van Dorp, 2009 – GWU>
O
113 113
Rationale

Risk Assessment Process
Causes
X
Sensitivity
Risk
Y
Conclusion
Z
Step 2:
Randomly
sample input
distributions
and calculate
life using the
calibrated
model
<Van Dorp, 2009 – GWU>
O
114 114
Rationale
Causes

Risk Assessment Process
X
Sensitivity
Y
Risk
Conclusion
Z
Step 3: Assess the
distribution of life estimates
<Van Dorp, 2009O
– GWU>
115 115
Rationale
Causes

Suppose an agency is interested in the risk of
potential climate change on
business processes
Annual
Costs
Sensitivity
Climate
Risk
Conclusion
Service
Life
Propagating
Uncertainty
Budget
Needs
Project
Utility
116 116
Rationale
Causes

Assume 30 year old bridge asset with
following characteristics:
 Normal annual temperature (°F) = 49
 Normal annual precipitation (in.) = 43
Sensitivity
 Part of NHS system
 Non-Corrosive Soil
Risk
 Steel, girder bridge
 50 feet long
 Wearing Surface Applied
Conclusion
 $50k Replacement Cost
 4% Interest Rate
117 117
Rationale

Causes
Assess how life changes due to
uncertain climate
Δ in Temperature Forecasts
0.7
Risk
0.5
0.4
Low Emissions
% Δ in Precipitation Forecasts
0.3
0.2
Moderately High
0.07
Emissions
0.06
0.1
0.0
0
2
4
Δ in Temperature (°F)
Conclusion
<ICF International, 2009 in CCSP, 2009>
6
Probability
Sensitivity
Probability
0.6
0.05
0.04
Low Emissions
0.03
0.02
Moderately High
Emissions
0.01
0.00
-15
-10
-5
0
5
10
15
20
Δ in Precipitation (in.)
118 118
Rationale
Uncertain Survival for Low Emissions
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
Uncertain Survival for Moderately
High Emissions
Expected
Confidence Interval
1.0
0.8
0
20
40
60
Median Life (years)
Conclusion
80
100
Probability
Risk
Probability
Causes
Sensitivity
After 2,500 Simulations

0.6
0.4
Confidence Interval
0.2
Expected
0.0
0
20
40
60
80
100
Median Life (years)
119 119
Rationale
Causes

Median Life
 Current: 50 years
 Low Emissions: 50 yrs
 Mod. High Emissions: 49 yrs
Sensitivity
Conclusion
Uncertain Median Life
Probability
Risk
90% CI
[46,53]
[45,54]
0.20
0.18
0.16
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0.00
Low Emissions
Moderately High
Emissions
40
45
50
55
Median Life (years)
60
120120
Rationale

Uncertain Life  Uncertain EUAC
 Current: $2,328
Causes
90% CI
[$2,286,$2,394]
[$2,273,$2,394]
 Low: $2,328
 Mod. High: $2,343
Sensitivity
Conclusion
Probability
Risk
Uncertain Median EUAC
0.20
0.18
0.16
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0.00
$2,200
Low Emissions
Moderately High
Emissions
$2,300
$2,400
EUAC ($)
$2,500
121 121
Rationale

Probability Future Average Life or EUAC <
Current Average Life or EUAC
Causes
 Low Emissions: 48.8% chance
Sensitivity
 Mod. High Emissions: 51.6% chance
Risk
Conclusion
122122
Rationale
Causes
Sensitivity
Risk

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 need
P(‘Failure’ within planning horizon)*Cost
[1-S(30+10)] * Replacement Cost
= $16,712 for Low Emissions
= $16,917 for Moderately High Emissions
Conclusion
123 123
Rationale


Risk of programming the wrong project
Assume ranking based decision on ΔURSL
Causes
𝑈 = 1.1659 ∗ 1 − 𝐸𝑋𝑃 −0.0195 ∗ 𝑅𝑆𝐿2
1
0.9
Sensitivity
0.8
Risk
Utility
0.7
0.6
0.5
0.4
0.3
0.2
0.1
Conclusion
0
0
2
4
6
8
10
Remaining Service Life in Years
124124
Rationale

Causes
Sensitivity
Risk
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…
Expected ΔU
P(Max Benefit)
P(Benefit)
+25
18.0%
31.7%
Conclusion
125 125
Rationale
Causes
Sensitivity
Risk
Full Demonstration of
Example Programmed into
Spreadsheet
Conclusion
126126
Rationale
Causes
Sensitivity
Risk
Conclusion
▪ Need to incorporate uncertainty
▪ Causes of uncertainty
▪ Methods for assessing uncertainty
▪ Sensitivity Analysis
▪ Risk Analysis
▪ Recommended to move towards
probabilistic planning and management
framework
127 127
128
129
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
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
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
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
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
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
136