Reliability of Results Depends On:

Transcription

1 HDM-4 Calibration

2 Reliability of Results Depends On: 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 2

3 How Credible are HDM-4 Outputs? 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 3

4 Approach to Calibration 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 3 4

5 Data & Calibration Need to appreciate importance of data over calibration If input data are wrong why worry about calibration? Calibration 'The Depth of the Sea and the Height of the Waves' Data 5

6 Calibration Focus 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 6

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

8 Hierarchy of Effort Time Required Years Experimental Surveys and Research Months Field Surveys Weeks Desk Studies Limited Moderate Significant Resources Required General Planning Quick Prioritisation Preliminary Screening Coarse Estimates Project Appraisal Detailed Feasibility Reliable Estimates Research and Development 8

9 Calibration Levels 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 9

10 Level 1 - Basic Application 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 10

11 Level 2 - Calibration 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 11

12 Level 3 - Adaptation 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 12

13 Important Considerations 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) 13

14 Bias and Precision A Low Bias High Precision B Low Bias Low Precision Predicted Data Predicted Data Observed = Predicted Observed = Predicted Observed Observed C High Bias High Precision D High Bias Low Precision Predicted Observed = Predicted Predicted Observed = Predicted Data Data Observed Observed 14

15 Calibration Adjustments A Rotation B Translation Translation Predicted Observed = Predicted Rotation Predicted Observed = Predicted Data Data Observed Observed C Rotation and Translation Translation Rotation Predicted Observed = Predicted Data Observed 15

16 Correction Factors 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 16

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

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

19 Road Deterioration Calibration Factors Calibration Factor K ddf K cia K ciw K cpa K cpw K cit K cpt K rid K rst K rpd K rsw K vi K vp K pi K pp K eb K gm K gp K td K sfc K sfcs Deterioration Model Drainage Factor All Structural Cracking - Initiation Wide Structural Cracking - Initiation All Structural Cracking - Progression Wide Structural Cracking - Progression Transverse Thermal Cracking - Initiation Transverse Thermal Cracking - Progression Rutting - Initial Densification Rutting - Structural Deterioration Rutting - Plastic Deformation Rutting - Surface Wear Ravelling - Initiation Ravelling - Progression Pothole - Initiation Pothole - Progression Edge Break Roughness - Environmental Coefficient Roughness - Progression Texture Depth - Progression Skid Resistance Skid Resistance - Speed Effects 19

20 Cracking Initiation Calibration Crack Initiation Percent Area of Cracking Years Kci = 1.00 Kci = 1.80 Kci =

21 Cracking Progression Calibration Crack Progression Percent Area of Cracking Years Kcp = 1.0 Kcp = 2.0 Kcp =

22 Road Deterioration Calibration (1) 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 22

23 Road Deterioration Calibration (2) 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 23

24 Kazakhstan Calibration Example Observed Roughness Values (IRI, m/km) Without Calibration Scenario Roughness Environmental Factor = 1.0 Cracking Initiation Factor = 1.0 Bias = Mean Observed / Mean Predicted = Predicted Roughness Values (IRI, m/km) Roughness surveys three years apart 24 Observed Roughness Values (IRI, m/km) With Calibration Scenario Roughness Environmental Factor = 1.5 Cracking Initiation Factor = 0.6 Bias = Mean Observed / Mean Predicted = Predicted Roughness Values (IRI, m/km)

25 Road Deterioration Calibration (3) 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 25

26 What to Focus On? 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 26

27 Sensitivity Classes Impact Sensitivity Impact Class Elasticity High S-I >0.50 Medium S-II Low S-III Negligible S-IV <

28 Sensitivity Classes ensitivity Class 2/ Impact Elasticity Parameter Important for Total VOC 3/ S-I > 0.50 kp - parts model exponent New Vehicle Price S-II S-III Roughness E0 - speed bias correction Average Service Life Average Annual Utilisation Vehicle Weight Aerodynamic Drag Coefficient Beta - speed exponent BW - speed width effect Calibrated Engine Speed CLPC - labour model exponent C0SP - parts model constant term CSPQI - parts model roughness term Crew/Cargo/Passenger Cost Desired Speed Driving Power Energy Efficiency Factors Fuel Cost Hourly Utilisation Ratio Interest Rate Projected Frontal Area 28 Parameter Important for VOC Savings 4/ kp - parts model exponent New Vehicle Price CSPQI - parts model roughness term C0SP - parts model constant term E0 - speed bias correction ARVMAX - max. rectified velocity CLPC - labour model exponent Beta - speed exponent Vehicle Age in km C0LH - labour model constant term Labour Cost Hourly Utilisation Ratio BW - speed width effects Number of tires per Vehicle New tire Cost Lubricants Cost Crew/Cargo/Passenger Cost Vehicle Weight Number of Passengers S-IV <0.05 All Other Variables All Other Variables

29 Sensitivity Classes Sensitivity Impact Parameter Outcomes Most Impacted Class Elasticity Pavement Performance Resurfacing and Surface Economic Return on Maintenance Distress S-I > 0.50 Structural Number 2/ Modified Structural Number 2/ Traffic Volume Deflection 3/ Roughness S-II Annual Loading 0.50 Age All cracking area Wide cracking area Roughness-environment factor Cracking initiation factor Cracking progression factor S-III Subgrade CBR (with SN) 0.20 Surface thickness (with SN) Heavy axles volume Potholing area Rut depth mean Rut depth standard deviation Rut depth progression factor Roughness general factor S-IV < 0.05 Deflection (with SNC) Subgrade compaction Rainfall (with Kge) Ravelling area Ravelling factor 29

30 Information Quality Levels HIGH LEVEL DATA IQL-5 Performance System Performance Monitoring IQL-4 IQL-3 IQL-2 IQL-1 Structure Condition Ride Distress LOW LEVEL DATA Planning and Performance Evaluation Programme Frictio n Analysis or Detailed Planning Project Level or Detailed Programme Project Detail or Research 30

31 Information Quality Levels 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 31

32 Adapting Local Data Road Condition IQL-2 IQL-2B IQL-3 IQL-4 Lane roughness (m/km IRI) Roughness (6 ranges) Ride quality (class) All Cracks Area (% area) Cracking (score, or Universal Cracking Index, UCI) Wide Cracks Area (% area) Transverse thermal cracks (no./km) Ravelled Area (% area) Potholes Number (units/lane-km) Edge-break area (m2/km) Patched Area (% area) Rut Depth Mean (mm) Rut Depth Standard Dev. (mm) Macro-texture depth (mm) Disintegration (score) Deformation (score) Surface Distress Index (SDI) Surface texture (class) Skid Resistance (SF50) Friction (class) Surface Friction (class) Pavement Condition (class) 32

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

34 Can We Believe HDM-4 Output? 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 23 34

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