performance models. understand use of performance models identify common modeling approaches...
TRANSCRIPT
![Page 1: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/1.jpg)
PERFORMANCE MODELS
![Page 2: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/2.jpg)
• Understand use of performance models
• Identify common modeling approaches
• Understand methods for evaluating reliability
• Describe requirements for updating models
Instructional Objectives
![Page 3: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/3.jpg)
Overview
• Serviceability-performance concepts
• Deterioration as a representation of change in performance
![Page 4: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/4.jpg)
Performance Model Development Criteria
• Adequate database (Long-Term)• 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
![Page 5: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/5.jpg)
Data Requirements
• Requirements vary depending on the type of model being developed
• Inventory Information
• Condition Monitoring Data
![Page 6: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/6.jpg)
Lack of Historical Data
• If historical databases are not available due to changes in survey procedures or equipment, new rehabilitation techniques, or other factors, other techniques are available:– incorporate input from experienced
practitioners– update the models as additional data are
available
![Page 7: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/7.jpg)
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
![Page 8: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/8.jpg)
Model Limitations• Models must be used appropriately
• Limitations of models must be considered
• Boundary conditions should be identified and satisfied
![Page 9: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/9.jpg)
Regression Analysis
• Statistical tool used to establish the relationship between two or more variables
• Often used in agencies with historical databases available
• Models can be linear or non-linear, depending on the relationship between variables
![Page 10: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/10.jpg)
Deterministic Model Forms
Linear Polynomial Hyperbolic
![Page 11: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/11.jpg)
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
![Page 12: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/12.jpg)
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
![Page 13: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/13.jpg)
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
![Page 14: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/14.jpg)
Family Models
• Start simple (model for each pavement type)
• Increase sophistication (based on climatic zones, functional classes, specific materials, surface type, functional classification, traffic levels, and geographic location)
The assumption is that each pavement section within a family has a similar deterioration pattern.
The pavement performance model developed for the family represents the average deterioration pattern for all sections included in that family.
![Page 15: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/15.jpg)
Shift of the Family Performance Model
Shift in CurveFor IndividualSection
Original Default Family Curve
Actual/Specific Section Condition
Age
Condition
Predicted SectionCondition
The condition of an individual section is determined by shifting the family curve to intersect the condition point for the section.
![Page 16: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/16.jpg)
Advantages/Disadvantages to Family Models
Deterministic models using regression analysis are common in agencies seeking network-level models for a multi-year pavement management analysis such as multi-year prioritization.
These models are popular because: – easy to develop and interpret– can be developed with commonly available
statistical analysis packages– can be developed with any number of variables– a number of different model forms
![Page 17: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/17.jpg)
Statistical Evaluation of Models
• Coefficient of determination (R2)
• Root mean square error (RMSE)
• Number of data points (n)
• Hypothesis tests on regression coefficients
![Page 18: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/18.jpg)
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
![Page 19: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/19.jpg)
RMSE
• Standard deviation of the predicted dependent variable value for a specific value of the independent variable
• Network Level: > 5
![Page 20: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/20.jpg)
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
![Page 21: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/21.jpg)
Reliability of Performance Models
Regression Parameter Expectations
PMS Analysis
Level
R2 RMSE Sample Size
# of Independent
Variables Network Medium
to Low Medium to Low
Large Sample
>1
Project High Low Small Sample
1
![Page 22: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/22.jpg)
Probabilistic Performance Models
• Predict a range of condition values rather than a single value of condition
• Survivor curves represent the percentage of pavements that remain in service as a function of time
• Markovian theory is founded on the assumption that the probability that a pavement will change from one condition state to another is only dependent on its current state
![Page 23: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/23.jpg)
Probabilistic Performance Models transition probabilities matrix
Markov Transition Probability Matrix
100-90 90-80 80-70 70-60100-90 0.2 0.4 0.3 0.190-80 0.2 0.6 0.280-70 0.1 0.3 0.670-60 0.1 0.9
Current State
![Page 24: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/24.jpg)
transition probabilities matrix
9 100-90
889-80
779-70
669-60
559-50
449-40
339-30
229-20
119-10
9 0.9 0.1
100-90
8 0.05 0.65 0.3
89-80
7 0.05 0.6 0.25 0.1
79-70
6 0.05 0.45 0.25 0.2 0.05
69-60
5 0.05 0.25 0.4 0.3
59-50
4 0.05 0.2 0.75
49-40
3 0.05 0.65 0.3
39-30
2 0.1 0.8 0.1
29-20
1 0.05 0.95
19-10
![Page 25: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/25.jpg)
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.
![Page 26: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/26.jpg)
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
![Page 27: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/27.jpg)
Prediction Model
0
1
2
3
4
5
2000 2005 2010 2015 2020
Years
Dis
tres
s In
dex
Default Distress
Index Model
Trigger
Current Time
Site Specific
Distress Model
Predicted
Measured
Pavement Performance Example
![Page 28: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/28.jpg)
NJDOT Performance Models
![Page 29: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/29.jpg)
NJDOT Performance ModelsOriginal Models based on Expert Opinion
SDI = SDIO - e(A−B*C ln(1/Age))
activity coef_a coef_b coef_c coef_o
4" Overlay over AC 28.08 28.79 1.023 5
6" Overlay over AC 28.08 28.98 1.023 5
Full Recon (BC) 13.05 13.592 1.038 5
Micro Surf(NovaChip) 28.08 28.527 1.023 5
Mill 2"+J Repr+Ov 4" 13.05 13.35 1.038 5
Mill 2"+Overlay 2" 28.08 28.625 1.023 5
Mill 2"+Overlay 4" 28.08 28.79 1.023 5
Mill 2"+Overlay 6" 28.08 28.98 1.023 5
Mill(3-4")Ovly(3-4") 28.08 28.79 1.023 5
Mill2+Ovly4(Poly.) 28.08 28.923 1.023 5
Overlay 2" over AC 28.08 28.527 1.023 5
Partial Recon (BC) 13.05 13.509 1.038 5
Patching 13.05 13.408 1.038 5
![Page 30: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/30.jpg)
NJDOT Performance ModelsSDI Predictive Model for the Mill 2”/ Overlay 2” activity
Forced to Fail in 10 years
![Page 31: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/31.jpg)
NJDOT Performance Models Original Models based on Expert Opinion
IRI = IRIO + exp*(A−B*C ln(Age )) activity coef_a coef_b coef_c coef_o
Mill 2"+Overlay 2" 13.959 13.596 0.844 70
Mill 2"+Overlay 4" 25.886 25.463 0.928 70
Overlay 2" over AC 18.949 20.153 0.847 90
Mill 2"+Overlay 6" 25.886 26.057 0.928 70
Mill 2"+J Repr+Ov 4" 25.886 25.463 0.928 70
Mill(2-4")+J Replace 25.886 25.463 0.928 70
Micro Surf(NovaChip) 18.949 18.379 0.847 90
Mill2+Ovly4(Poly.) 25.886 25.781 0.928 70
Partial Recon (BC) 13.209 18.065 0.755 70
Full Recon (BC) 13.209 18.557 0.755 60
Patching 18.949 20.153 0.847 90
4" Overlay over AC 18.949 20.685 0.847 70
6" Overlay over AC 18.949 21.694 0.847 70
Mill(3-4")Ovly(3-4") 25.886 25.463 0.928 70
![Page 32: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/32.jpg)
NJDOT Performance Models IRI Predictive Model for the Mill 2”/ Overlay 2” activity
Forced to Fail in 10 years
![Page 33: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/33.jpg)
NJDOT Performance ModelsNJDOT Enhanced Model Development
based on measured data
• IRI and SDI data points from 1999 was used to analyze the current models and/or develop new curves. • The IRI and SDI condition data for 2001-2002, 2002-2003, 2004, 2005 and 2006 was used to develop actual performance histories of the construction projects completed in 1999 • The 1999 construction projects were divided into groups according to the treatment activities:
Mill 2"+Overlay 2" Mill 2"+Overlay 4"
![Page 34: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/34.jpg)
NJDOT Performance ModelsNJDOT Enhance Model Development
based on measured data
Issues with model development:
• Over the long term, personnel, condition survey equipment, methods, materials, condition methods, and other variable change
• Performance of treatment is influenced by current pavement condition
![Page 35: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/35.jpg)
NJDOT Performance Models
SDI Performance History for the Mill 2”/Overlay 2” Activity
NJDOT Family Model Development
![Page 36: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/36.jpg)
NJDOT Performance ModelsNJDOT Family Model Development
IRI Predictive Model for the Mill 2”/ Overlay 2” activity
![Page 37: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/37.jpg)
NJDOT Performance ModelsNJDOT Family Model Development
Overall IRI Predictive Model
Longterm IRI Prediction
0
50
100
150
200
250
0 5 10 15 20 25 30 35 40 45 50
Age
IRI
Average 502-559 Trigger IRI 30 yr Pred IRI
20 yr Pred IRI
40 yr Pred IRI
25 yr Pred IRI
![Page 38: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/38.jpg)
NJDOT Performance ModelsNJDOT Family Model Development
Overall IRI Predictive Model
Forced to fit 170 in/mile in 25 years
![Page 39: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/39.jpg)
ANALYSESPERFORMACE PREDICTION
MIN. ACCEPTABLE
STRUCTURAL CAPACITY
AGE (YEARS)
DESIGN PERIOD
MAX. ACCEPTABLE
DISTRESS
MIN. ACCEPTABLE
SKID RESISTANCE
RIDE QUALITY
MIN. ACCEPTABLE
![Page 40: PERFORMANCE MODELS. Understand use of performance models Identify common modeling approaches Understand methods for evaluating reliability Describe requirements](https://reader035.vdocuments.mx/reader035/viewer/2022081519/56649f315503460f94c4c009/html5/thumbnails/40.jpg)
Questions?