HDM-4 Calibration

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

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 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 Hierarchy of Effort

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 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 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 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 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 Bias and Precision

15 Calibration Adjustments

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

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 Road Deterioration Calibration Factors

20 Cracking Initiation Calibration

21 Cracking Progression Calibration

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 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 Kazakhstan Calibration Example Roughness surveys three years apart

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 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 Sensitivity Classes

28 Sensitivity Classes

29 Sensitivity Classes

30 Information Quality Levels

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 Adapting Local Data Road Condition

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 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 For Further Information A guide to calibration and adaptation Reports on various HDM calibrations from: