BRI Presentation 6 June 2005

This research study is undertaken by the Cooperative Research Centre for Construction Innovation (CRC CI).

Research partners:RMIT University Queensland University of Technology (QUT)

Organisations Partners:Queensland Department of Main Roads (QDMR)Queensland Department of Public Works (QDMP)

Background

Objective of Research Study

• To improve reliability in budget/cost estimates for road asset management (Maintenance and rehabilitation)

• Department of Main Roads has 34,000km of road network consist various pavement types, soils, traffic, environment

• Queensland have well developed Asset Management practices– Comprehensive, relevant, quality asset data

ARMIS (A Road Management Information System) Database

– Investment modelling tools: (SCENARIO)• Improve reliability in budget estimates for road

asset management

Background

Background (Cont.)

Background (Cont.)

Background (Cont.)

• Developed a probability-based method for assessing variability in budget estimates for highway asset management

Outline of Presentation

• Identification of critical parameters

• Demonstrate a method in assessing variation in budget estimates for road maintenance and rehabilitation

Part One

Identification of critical parameters

Identification of Critical Input Parameters

The variability of Input parameters

• Pavement strength • Rut depth• Annual equivalent number of axles• Initial roughness for the analysis year• Pavement thickness• Cracking

The variability of out parameters

• Annual change in pavement roughness

Identification of Critical Input Parameters

ΔRI = Kgp (ΔRIs + ΔRIc + ΔRIr + ΔRIt) + m Kgm RIa

ΔRIs = change in roughness due to pavement strength deterioration due to vehicles

SNPKb = Modified Structural numberYE4 = Equivalent standard number of axles AGE3 = Pavement ageKgp = calibration factor, Default value = 1.0ΔRI = total change in roughnessΔRIc = change in roughness due to crackingΔRIr = change in roughness due to ruttingΔRIt = change in roughness due to pothole(m kgm RIa = ΔRIe) = change in roughness due to climatic condition

Identification of Critical Input Parameters

COV of Input Parameters Compared with COV of output Variable

Note: COV is coefficient of variation (σ/μ)

Identification of Critical Input Parameters

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5

Pavement Thickness (mm)

Co

eff

icie

nt

of

Va

ria

tio

n (

Co

v)

Cov of SNPKb

Cov of AnnualChange inRoughness

200-300 300-400 400-500 500-600

Identification of Critical Input Parameters

0

0.5

1

1.5

2

2.5

0 1 2 3 4 5

Pavement Thickness (mm)

Co

eff

icie

nt

of

Va

ria

tio

n (

Co

v)

StandardDeviation of RutDepth

Annual Changein Roughness

200-300 300-400 400-500 500-600

Identification of Critical Input Parameters

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 1 2 3 4 5

Pavement Thickness (mm)

Co

effi

cien

t o

f V

aria

tio

n (

Co

v)

Cov of AnnualEquivalentStandard Axles(YE4)

Cov of AnnualChange inRoughness

200-300 300-400 400-500 500-600

Identification of Critical Input Parameters

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0 1 2 3 4 5

Pavement Thickness (mm)

Co

eff

icie

nt

of

Va

ria

tio

n (

Co

v)

Cov of InitialRoughness at theStart of theAnalysis Year

Cov of AnnualChange inRoughness

200-300 300-400 400-500 500-600

Identification of Critical Input Parameters

00.10.20.30.40.5

0.60.70.80.9

1

0 1 2 3 4 5

Pavement Thickness (mm)

Co

effi

cien

t o

f V

aria

tio

n (

Co

v)

Cov of PavementAge (AGE3)

Cov of AnnualChange inRoughness

200-300 300-400 400-500 500-600

Identification of Critical Input Parameters

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5

Pavement Thickness (mm)

Co

eff

icie

nt

of

Va

ria

tio

n (

Co

v)

Cov of % ofCracking of TotalCarriageway

Cov of AnnualChange inRoughness

200-300 300-400 400-500 500-600

Identification of Critical Input Parameters

Critical input parameters

• Pavement strength• Rut depth• Annual equivalent number of axles• Initial roughness• Unit costs

Case studyAssessment of Variation in Budget Estimates for Road

Maintenance and Rehabilitation

92 km Bruce highway•Pavement strength•Rut depth•Annual average daily traffic (AADT)•Initial roughness

Case studyAssessment of Variation in Budget Estimates for Road

Maintenance and Rehabilitation

0

2

4

6

8

0 20 40 60 80 100

Distance (km)

Mea

n V

alu

es o

f S

tru

ctu

ral

Nu

mb

er

Mean Values

0

0.5

1

1.5

2

2.5

3

0 20 40 60 80 100

Distance (km)

Sta

nd

ard

Dev

iati

on

of

Str

uct

ura

l N

um

ber

Standard Deviations

Case studyAssessment of Variation in Budget Estimates for Road

Maintenance and Rehabilitation

0

2

4

6

8

10

12

0 20 40 60 80 100

Distance (km)

Ave

rag

e R

ut

Dep

th (

mm

)

Mean Values

0

1

2

3

4

5

6

7

8

0 20 40 60 80 100

Distance (km)

Sta

nd

ard

Dev

iati

on

of

Ave

rag

e R

ut

Dep

th (

mm

)

Standard Deviations

Case studyAssessment of Variation in Budget Estimates for Road

Maintenance and Rehabilitation

0

5000

10000

15000

20000

25000

30000

35000

40000

0 20 40 60 80 100

Distance (km)

Mea

n V

alu

es o

f A

AD

T

Mean Values

0

200

400

600

8001000

1200

1400

1600

1800

0 20 40 60 80 100

Distance (km)

Sta

nd

ard

Dev

iati

on

of

AA

DT

Standard Deviations

Case studyAssessment of Variation in Budget Estimates for Road

Maintenance and Rehabilitation

0

0.5

1

1.5

2

2.5

3

3.5

4

0 20 40 60 80 100

Distance (km)

Mea

n V

alu

es o

f In

itia

l R

ou

gh

nes

s (I

RI)

Mean Values

0

0.5

1

1.5

2

0 20 40 60 80 100

Distance (km)

Sta

nd

ard

Dev

iati

on

of

Init

ial

Ro

ug

hn

ess

(IR

I)

Standard Deviations

Case studyAssessment of Variation in Budget Estimates for Road

Maintenance and Rehabilitation

0

1

2

3

4

5

6

7

2003 2004 2005 2006 2007

Years

Co

st E

stim

ate

($ M

illi

on

)

Mean of CumulativeCosts

Mean+SD ofCumulative Costs

95th Percentile ofCumulative Costs