performance models lecture 16. understand use of performance models identify common modeling...
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PERFORMANCE MODELS
Lecture 16
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• Understand use of performance models
• Identify common modeling approaches
• Understand methods for evaluating reliability
• Describe requirements for updating models
Instructional Objectives
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Overview
• Serviceability-performance concepts
• Deterioration as a representation of change in performance
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Uses of Performance Models in Pavement Management
• Network Level
• Project Level
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Types of Performance Models
Deterministic Probabilistic
PrimaryResponse
Structural Functional Damage SurvivorCurves
Transition ProcessModels
Deflection Stress Strain
Distress Pavement
Condition
PSI Safety
LoadEquiv.
Markov Semi-Markov
NationalLevel
State orDistrictLevel
ProjectLevel
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Importance of Accurate Modeling
Condition
100
0
60
10 20
Age
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Performance Model Development Criteria
• Adequate database
• Inclusion of all significant variables that affect performance
• Adequate functional form of the model
• Satisfaction of the statistical criteria concerning the precision of the model
• Understanding of the principles behind each modeling approach
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Data Requirements
• Requirements vary depending on the type of model being developed
• Inventory Information
• Monitoring Data
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Lack of Historical Data
• If historical databases are not available due to changes in survey procedures, new rehabilitation techniques, or other factors, other techniques are available:– incorporate input from experienced practitioners– update the models as additional data are
available
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On-the-Diagonal Issue
Traffic (ESALs)
Light Heavy
Thin
Pavement Thickness
Thick
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Data Requirements
• Sufficient amounts of data must be used
• Data must be measured accurately and without bias
• Data must be representative
• Data must be maintained over time
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Model Limitations
• Models must be used appropriately
• Limitations of models must be considered
• Boundary conditions should be identified and satisfied
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Deterministic vs. Probabilistic
• Predicted Occurrence
• Techniques
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Mechanistic Models
• No purely mechanistic performance models have been developed
• Calculated stress and strain attributes from mechanistic models can be used as the input for an empirical prediction model
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Mechanistic-Empirical Models
• Models developed using pavement response as the dependent variable
• Use elements of both mechanistic models (fundamental principles of pavement behavior) and empirical models (results from experience or experiments)
• N = A * (1/e)B
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Regression Analysis
• A technique used to determine the relationship between variables
• Often used in agencies with historical databases available
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Subjective Approaches
• Used in agencies that do not have historical data available
• Can be used to develop either deterministic or probabilistic models
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Development of Deterministic Performance Models
• Very common modeling techniques in pavement management
• Predict a single number based on its relationship with one or more variables
• Can be empirical or mechanistic-empirical correlations calibrated using regression
• Condition is modeled as a function of other variables
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Regression Analysis
• Statistical tool used to establish the relationship between two or more variables
• Models can be linear or non-linear, depending on the relationship between variables
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Regression Model Forms
• Linear Regression Y = a + bX
• Multiple Linear Regression Y = a0 + a1X1 + a2X2 + . . . AnXn
• Non-Linear Regression Y = a0 + a1X1 + a2X2 + . . . AnXn
Polynomial regression models may be constrained
Least squares fit is used to improve the models
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Deterministic Model Forms
Linear Polynomial Hyperbolic
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Family Models
• Reduces number of variables
• Group pavement sections by characteristics
• Assume similar deterioration patterns
• Reflects average deterioration for family
• Allows ranges of values to be used for developing families
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Shift of the Family Performance Model
Shift in CurveFor IndividualSection
Original Family Curve
Actual SectionCondition
Age
Condition
Predicted SectionCondition
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Advantages/Disadvantages to Family Models
• Advantages
• Disadvantages
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Statistical Evaluation of Models
• Coefficient of determination (R2)
• Root mean square error (RMSE)
• Number of data points (n)
• Hypothesis tests on regression coefficients
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Coefficient of Determination (R2)
• Provides an indication of how much of the total variation in the data is explained by the regression equation or performance curve
• Network Level normally < 0.9
• Project Level normally > 0.9
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RMSE
• Standard deviation of the predicted dependent variable value for a specific value of the independent variable
• Project Level: <=5
• Network Level: > 5
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Other Tests
• Number of Data Points
• Hypothesis Test of Regression Constants
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Limitation of Statistical Evaluations
• Statistical analyses only evaluate reliability of model for data used in its development
• A model can be statistically valid but not representative of actual deterioration patterns of network if poor quality data are used
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Reliability of Performance Models
• Network Level
• Project Level
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Reliability of Performance Models
Regression Parameter Expectations
PMSAnalysis
Level
R2 RMSE SampleSize
# ofIndependent
VariablesNetwork Medium
to LowMediumto Low
LargeSample
>1
Project High Low SmallSample
1
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Update Requirements
• Performance models must be updated regularly to continue to reflect deterioration patterns
• Feedback loops should be established to link deterioration models with engineering practices.
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Examples
• Washington DOT– Deterministic models
• Illinois DOT– Deterministic models
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Washington State DOT
• Priority programming process
• Developed in-house
• Prediction models developed for combined ratings
• Raw distress severity and extent data are stored so models can be modified as needed
• Capabilities exist for statistical analysis of performance trends
• Performance models for individual sections
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WSDOT Model Form
Condition
Age
Random Fluctuation in Ratings
PCR = C-mAP
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Fluctuations in Degree of Curvature in WSDOT Models
Age (Years)
PCR
10 15
100
0
PCR = 100-10(Age)0.5
PCR = 100-0.1(Age)2.5
PCR = 100-10.0(Age)1.0
PCR = 100-1.0(Age)2.0
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Over Estimation of Performance
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Illinois DOT Deterministic Models
• Condition Rating Survey (CRS)– 1.0 to 9.0– Type, severity, and extent of 5 predominant
distress
• Automated the CRS Process– Safety of expert panel– Reduction in staff– PaveTech vans purchased– Calculation of CRS value
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IDOT Family ModelsSystem Surface District TotalInterstate Composite (2) 1-4
5-94
Concrete (2) 1-45-9
4
Non-Interstate
Flexible (3) 1-45-9(1-9)ST
3
Overlays (5) 1-45-9(1-9)CRC
9
Concrete (4) 1-45-9
8
SMART Flexible & OL(4)
All 4
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IDOT Polynomial Approach
1
2
3
4
5
6
7
8
9
0 5 10 15 20 25 30 35 40 45 50
Age (Years)
CRS
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IDOT Trajectories
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D-Crack Adjustments
• Asphalt concrete overlays: deducts increased by 20%
• Jointed reinforced concrete: deducts increased by 20%
• CRCP: deducts increased by 50%
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• Understand use of performance models
• Identify common modeling approaches
• Understand methods for evaluating reliability
• Describe requirements for updating models
Instructional Objectives