BRI Presentation 6 June 2005
description
Transcript of BRI Presentation 6 June 2005
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BRI Presentation 6 June 2005
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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
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Objective of Research Study
• To improve reliability in budget/cost estimates for road asset management (Maintenance and rehabilitation)
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• 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
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Background (Cont.)
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Background (Cont.)
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Background (Cont.)
• Developed a probability-based method for assessing variability in budget estimates for highway asset management
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Outline of Presentation
• Identification of critical parameters
• Demonstrate a method in assessing variation in budget estimates for road maintenance and rehabilitation
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Part One
Identification of critical parameters
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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
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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
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Identification of Critical Input Parameters
COV of Input Parameters Compared with COV of output Variable
Note: COV is coefficient of variation (σ/μ)
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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
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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
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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
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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
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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
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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
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Identification of Critical Input Parameters
Critical input parameters
• Pavement strength• Rut depth• Annual equivalent number of axles• Initial roughness• Unit costs
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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
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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
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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
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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
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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
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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
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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