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 dataMust have a correct interpretation of the

input data requirementsHave a quality of input data appropriate for

the desired reliability of results

• CalibrationAdjust 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?

D ata

C alib ra tion

'The D epth o f the S ea andthe H e ight o f the W aves'

Data & Calibration

6

• Road User EffectsPredict the correct magnitude of costs and

relativity of components - dataPredict sensitivity to changing conditions -

calibration

• Pavement Deterioration & Works EffectsReflect local pavement deterioration rates and

sensitivity to factorsRepresent maintenance effects

Calibration Focus

7

Un-calibrated Calibrated

Actualdeterioration

Model

We attempt tominimize the

"mistake"

Time

Ext

ent

of

Def

ect

(%)

Actual Progression

Pre

dic

ted

Pro

gre

ssio

n

Actual Progression

Pre

dic

ted

Pro

gre

ssio

n

Estimating Calibration Coefficients

8

General PlanningQuick Prioritisation

Preliminary Screening

Coarse Estimates

Field Surveys

ExperimentalSurveys and

Research

Desk Studies

ResourcesRequired

Time Required

Weeks

Months

Years

Limited Moderate Significant

Project AppraisalDetailed Feasibility

Reliable Estimates

Research andDevelopment

Hierarchy of Effort

9

• Level 1: Basic ApplicationAddresses most critical parameters ‘Desk Study’

• Level 2: CalibrationMeasures key parametersConducts limited field surveys

• Level 3: AdaptationMajor field surveys to requantify relationshipsLong-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

• ComprisesStructured research, medium termAdvanced 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

Observed

Pre

dict

ed

Data

Observed = Predicted

Low BiasLow Precision

Observed

Pre

dict

ed

Data

Observed = Predicted

Low BiasHigh Precision

Observed

Pre

dict

ed

Data

Observed = Predicted

High BiasHigh Precision

Observed

Pre

dict

ed

Data

Observed = Predicted

High BiasLow Precision

A B

C D

Bias and Precision

15

Observed

Pre

dic

ted

Data

Observed = Predicted

Rotation andTranslation

Translation

Rotation

Observed

Pre

dic

ted

Data

Observed = Predicted

Translation

Translation

Observed

Pre

dic

ted

Data

Observed = Predicted

Rotation

Rotation

BA

C

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 DeteriorationCalibration Factors

All relationships have a calibration factor - ‘K’ factor

Used to adjustpredicted to observed

18

ICA = Kcia{a0 exp[a1SNP + a2(YE4/SNP2)]}

CalibrationFactor

ModelCoefficients

Initiation of Cracking

Typical Relationship

19

CalibrationFactor

DeteriorationModel

Kddf Drainage FactorKcia All Structural Cracking - InitiationKciw Wide Structural Cracking - InitiationKcpa All Structural Cracking - ProgressionKcpw Wide Structural Cracking - ProgressionKcit Transverse Thermal Cracking - InitiationKcpt Transverse Thermal Cracking - ProgressionKrid Rutting - Initial DensificationKrst Rutting - Structural DeteriorationKrpd Rutting - Plastic DeformationKrsw Rutting - Surface WearKvi Ravelling - InitiationKvp Ravelling - ProgressionKpi Pothole - InitiationKpp Pothole - ProgressionKeb Edge BreakKgm Roughness - Environmental CoefficientKgp Roughness - ProgressionKtd Texture Depth - ProgressionKsfc Skid ResistanceKsfcs Skid Resistance - Speed Effects

Road Deterioration Calibration Factors

20

Crack Initiation

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20

Years

Per

cen

t A

rea

of

Cra

ckin

g

Kci = 1.00 Kci = 1.80 Kci = 0.55

Cracking Initiation Calibration

21

Crack Progression

0102030405060708090

100

0 5 10 15 20

Years

Per

cent

Are

a of

Cra

ckin

g

Kcp = 1.0 Kcp = 2.0 Kcp = 0.4

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

Without Calibration Scenario

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00

Predicted Roughness Values (IRI, m/km)

Obs

erve

d R

ough

ness

Val

ues

(IR

I, m

/km

)

Bias = Mean Observed / Mean Predicted = 1.14

Roughness Environmental Factor = 1.0Cracking Initiation Factor = 1.0

With Calibration Scenario

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00

Predicted Roughness Values (IRI, m/km)

Obs

erve

d R

ough

ness

Val

ues

(IR

I, m

/km

)

Bias = Mean Observed / Mean Predicted = 1.03

Roughness Environmental Factor = 1.5Cracking Initiation Factor = 0.6

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

Impact SensitivityClass

ImpactElasticity

High S-I >0.50

Medium S-II 0.20-0.50

Low S-III 0.05-0.20

Negligible S-IV <0.05

Sensitivity Classes

28

SensitivityClass2/

ImpactElasticity

Parameter Important forTotal VOC3/

Parameter Important forVOC Savings4/

S-I > 0.50 kp - parts model exponentNew Vehicle Price

kp - parts model exponentNew Vehicle PriceCSPQI - parts modelroughness termC0SP - parts model constantterm

S-II 0.20 - 0.50 RoughnessE0 - speed bias correctionAverage Service Life AverageAnnual UtilisationVehicle Weight

E0 - speed bias correctionARVMAX - max. rectifiedvelocityCLPC - labour model exponent

S-III 0.05 - 0.20 Aerodynamic Drag CoefficientBeta - speed exponentBW - speed width effectCalibrated Engine SpeedCLPC - labour model exponentC0SP - parts model constanttermCSPQI - parts modelroughness termCrew/Cargo/Passenger CostDesired SpeedDriving PowerEnergy Efficiency FactorsFuel CostHourly Utilisation RatioInterest RateProjected Frontal Area

Beta - speed exponentVehicle Age in kmC0LH - labour model constanttermLabour CostHourly Utilisation RatioBW - speed width effectsNumber of tires per VehicleNew tire CostLubricants CostCrew/Cargo/Passenger CostVehicle WeightNumber of Passengers

S-IV <0.05 All Other Variables All Other Variables

Sensitivity Classes

29

Sensitivity Impact Parameter Outcomes Most Impacted Class Elasticity Pavement

Performance Resurfacingand Surface

Distress

EconomicReturn on

Maintenance S-I > 0.50 Structural Number 2/

Modified Structural Number2/

Traffic Volume

Deflection3/

Roughness

S-II 0.20 - Annual Loading

0.50 Age

All cracking area

Wide cracking area

Roughness-environment factor

Cracking initiation factor

Cracking progression factor S-III 0.05 - 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

Sensitivity Classes

30

IQL-1

P erfo rm ance

S truc tu re C ond ition

R ide D is tressFric tio

n

IQL-5

IQL-4

IQL-3

IQL-2

System Perform anceM onitoring

P lanning andPerform anceEvaluation

Program m eAnalysis orD etailedP lanning

Project Level orD etailedProgram m e

ProjectD etail orR esearch

HIGH LEVEL DATA

LOW LEVEL DATA

Information Quality Levels

31

• IQL-1: Fundamental Researchmany attributes measured/identified

• IQL-2: Project Leveldetail typical for design

• IQL-3: Programming Levelfew attributes, network level

• IQL-4: Planningkey management attributes

• IQL-5: Key Performance Indicators

Information Quality Levels

32

IQL-2 IQL-2B IQL-3 IQL-4Lane roughness (m/km IRI) Roughness (6 ranges) Ride quality (class)All Cracks Area (% area) Cracking (score, or

Universal CrackingIndex, UCI)

Wide Cracks Area (% area)Transverse thermal cracks

(no./km)Ravelled Area (% area)

Potholes Number(units/lane-km)

Disintegration (score) Surface Distress Index(SDI)

Pavement Condition(class)

Edge-break area (m2/km)Patched Area (% area)Rut Depth Mean (mm) Deformation (score)

Rut Depth Standard Dev.(mm)

Macro-texture depth (mm) Surface texture (class)Skid Resistance (SF50) Friction (class) Surface Friction (class)

Adapting Local DataRoad Condition

33

EstablishingReliable Input

Data40%

ModelCalibration

10%

Treatments,Triggers and

Resets20%

Running HDM-410%

Verification ofOutput20%

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 usedCalibration extentCheck 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:www.lpcb.org