1. HDM-4 Calibration

2. 2 How well the available data represent the real conditions to HDM How well the model’s predictions fit the real behaviour and respond to prevailing conditions Reliability of Results Depends On:

3. 3 Depends on Level of Calibration (controls bias) Depends on accuracy and reliability of input data (asset & fleet characteristics, conditions, usage) HDM-4 has proved suitable in a range of countries As with any model, need to carefully check output with good judgement How Credible are HDM-4 Outputs?

4. 4 3 Input data  Must have a correct interpretation of the input data requirements  Have a quality of input data appropriate for the desired reliability of results Calibration  Adjust model parameters to enhance the accuracy of its representation of local conditions Approach to Calibration

5. 5 Need to appreciate importance of data over calibration If input data are wrong why worry about calibration? Data & Calibration

6. 6 Road User Effects  Predict the correct magnitude of costs and relativity of components - data  Predict sensitivity to changing conditions - calibration Pavement Deterioration & Works Effects  Reflect local pavement deterioration rates and sensitivity to factors  Represent maintenance effects Calibration Focus

7. 7 Un-calibrated Calibrated Actual deterioration Model We attempt to minimize the "mistake" Time Extent of Defect (%) Actual Progression Predicted Progression Actual Progression Predicted Progression Estimating Calibration Coefficients

8. 8 Hierarchy of Effort

9. 9 Level 1: Basic Application  Addresses most critical parameters  ‘Desk Study’ Level 2: Calibration  Measures key parameters  Conducts limited field surveys Level 3: Adaptation  Major field surveys to requantify relationships  Long-term monitoring Calibration Levels

10. 10 Required for ALL HDM analyses Once-off ‘set-up’ investment for the model Mainly based on secondary sources Assumes most of HDM default values are appropriate Level 1 - Basic Application

11. 11 Makes measurements to verify and adjust predictions to local conditions Requires moderate data collection and moderate precision Adjustments entered as input data, typically no software changes Level 2 - Calibration

12. 12 Comprises  Structured research, medium term  Advanced data collection, long term Evaluates trends and interactions by observing performance over long time period May lead to alternative local relationships/models Level 3 - Adaptation

13. 13 Calibrate over full range of values likely to be encountered Have sufficient data to detect the nature of bias and level of precision High correlation (r^2) does not always mean high accuracy: can still have significant bias Primary aim: minimize bias (mean observed values / mean predicted values) Important Considerations

14. 14 Bias and Precision

15. 15 Calibration Adjustments

16. 16 Used to correct for bias Two types of factors  Rotation (CF = Observed/Predicted)  Translation (CF = Observed - Predicted) Rotation factors adjust the slope Translation factors shift the predictions vertically Correction Factors

17. 17 HDM-4 Road Deterioration Calibration Factors All relationships have a calibration factor - ‘K’ factor Used to adjust predicted to observed

18. 18 ICA = K cia {a 0 exp[a 1 SNP + a 2 (YE4/SNP 2 )]} Calibration Factor Model Coefficients Initiation of Cracking Typical Relationship

19. 19 Road Deterioration Calibration Factors

20. 20 Cracking Initiation Calibration

21. 21 Cracking Progression Calibration

22. 22 Simulation of Past Since Construction  take sample of roads with historical data (traffic, design, etc.)  simulate with HDM-4 the deterioration from construction time to current age  compare the simulated results with actual road condition at current age  deal with the uncertainty regarding the road conditon at construction time Road Deterioration Calibration (1)

23. 23 Simulation from Two Points in Time  take sample of roads with road condition data available for two years (e.g. roughness measurements surveyed in two different years)  simulate with HDM-4 the deterioration from the first year to the second year  compare the simulated results with the actual road condition at the second year Road Deterioration Calibration (2)

24. 24 Kazakhstan Calibration Example Roughness surveys three years apart

25. 25 Controlled Studies  collect detailed data over time on traffic, roughness, deflections, condition, rut depths, etc.  sections must be continually monitored  long-term (5 year) commitment to quality data collection Road Deterioration Calibration (3)

26. 26 HDM-III has about 80+ data items and model parameters; HDM-4 has more. Sensitivity of each item has been classified by sensitivity tests Simplify effort for less-sensitive items What to Focus On?

27. 27 Sensitivity Classes

28. 28 Sensitivity Classes

29. 29 Sensitivity Classes

30. 30 Information Quality Levels

31. 31 IQL-1: Fundamental Research  many attributes measured/identified IQL-2: Project Level  detail typical for design IQL-3: Programming Level  few attributes, network level IQL-4: Planning  key management attributes IQL-5: Key Performance Indicators Information Quality Levels

32. 32 Adapting Local Data Road Condition

33. 33 Establishing Reliable Input Data 40% Model Calibration 10% Treatments, Triggers and Resets 20% Running HDM-4 10% Verification of Output 20% Time Spend on Different Phases of Analysis

34. 34 23 Yes, if sufficiently calibrated HDM-4 has proved suitable in a range of countries As with any model, need to carefully scrutinize output against judgement If unexpected predictions occur, check:  Data used  Calibration extent  Check judgment of the expert Can We Believe HDM-4 Output?

35. 35 For Further Information A guide to calibration and adaptation Reports on various HDM calibrations from: www.lpcb.org