Pavement Surface Characteristics and Sustainability
2011 Road Profiler User’s Group
Reno, NV
Ting Wang, In-Sung Lee, Alissa Kendall,
John Harvey (presenter), Chang-Mo Kim, EB Lee,
Motivation
• Reduce energy use – national policy objective
• Reduce greenhouse gas emissions– California state law
• Achieve these goals by the most cost-effective policies possible– Prioritize based on $/benefit
California’s AB32 framework(reaffirmed by voters November 2010)
• AB32 requires– 2020 GHG emissions at 1990 levels– 2050 GHG emissions at 0.2 x 1990 levels
Transportation 38% of 2004 GHG
Refineries and cement plants significant parts of Industry (20% of 2004 GHG)
3
Objectives of Project• Develop LCA model for state highway and local
road networks– Initial models using available data sources– Update as develop regional databases
• Use model to answer questions regarding GHG ($/ton CO2e) and fuel use (net reductions):– Rolling resistance– Design life– Recycling vs local materials, transportation
costs– Alternative rehabilitation strategies
The Pavement Life Cycle(compatible with ISO 14040)
Materials
Construction
Use
Maintenance & Rehabilitation
End-of-Life
Materials extraction and production
Traffic delay
Onsite equipment
Transportation
Rolling resistanceCarbonationLightingAlbedoLeachate
Adapted from: Santero, N. (2009). Pavements and the environment: A life-cycle assessment approach. Ph.D. Thesis, UC Berkeley.
Sponsors:
Additional Sponsors:
Collaborators:
Additional Support Provided by:
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Workshop on LCA for Pavement, Davis, CA, May 2010Discussions and UCPRC approach downloadableat www.ucprc.ucdavis.edu/p-lca
Basic Approach by UCPRC forApplication to State, Local Networks
• Divide network into categories based on factorial
• Case studies for categories with sensitivity analyses
– IRI and MPD (for MIRIAM)
– Materials (type, production method, etc.)
– Hauling distance
– Traffic levels and congestion
– Fleet composition over time (new vehicle technologies)
• Initial assumption: surface type doesn’t change
– Except RHMA vs HMA, PCC vs CSA cement
– Consider LCA effects of changing surface type after models completed
Factorial for LCA for California State and Local Networks
Factorials Possible Value
Road type Rural road; urban road
Road grades Flat road; mountainous road
Road access type Restricted access; unrestricted access
Traffic level Different levels of AADT and AADTT, categorized
Pavement surface type Asphalt pavement; Cement concrete pavement
Pavement surface characteristics
Different levels of IRI and MPD, categorized
Pavement Treatment Different treatment options
Models: Materials and construction
• Materials production and plant emissions: – Existing databases & studies– review by CNCPC, APACA
• Off-Road equipment– OFFROAD: California’s off-road equipment emission
inventory• On-Road equipment
– EMFAC: California’s on-road vehicles emission inventory
• Equipment and hours– CA4PRS: Caltrans construction schedule analysis tool
• Road user delay– CA4PRS (not yet implemented)
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Models: Use Phase• MOVES
– MOtor Vehicle Emission Simulator– US EPA’s current official model for estimating air
pollutant emissions from cars and trucks– Can consider speed profiles
• HDM-4– Highway Development and Management Model– Steady speed fuel use– Relationship between pavement surface
characteristics (MPD, IRI) and rolling resistance– Developed by World Bank, recently calibrated with
fuel use instrumentation on North American vehicles by Imen & Chatti (Michigan State Univ), through NCHRP 01-45
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Pavement – Rolling Resistance – Energy
• HDM-4 model (World Bank; Imen & Chatti)– Surface characteristics:
– Rolling Resistance:
• MOVES model (US EPA)– Vehicle power demand (VSP):
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[ ]2 2 0 1 2 3CR Kcr a a Tdsp a IRI a DEF= + × + × + ×
( )( )212
. . .
( ) 1b
a D front w i R
P Air resist Inertial and Gradient resist Rolling resist
C A v v v M a g grade v C Mg vρ ε
= + +
= + × + + + × × + ×
Fr = CR2*FCLIM*[b11Nw+CR1(b12M+b13v2)]
Update MOVES parameter
• MOVES parameter from dynamometer test
• Proportionally increase from dynamometer to real-world pavement:
( )( )( )( )
2
2 3
. . .1 ( ) 12
1
b
R a D front w i
i
P Rolling resist Air resist Inertial and Gradient resist
C Mg v C A v v v a g grade M v
A v B v C v a g grade M
ρ ε
ε
= + +
= × + + × + + + × ×
= × + × + × + + + × v×
[ ]( )
2 2 0 1 2 32 2 0 1 1.02 0 0.28 2 0 3 0
0 1 2 30 1 0.28
updated pavement
default dynamometer
A CR Kcr a a Tdsp a IRI a DEFA CR Kcr a a a a
a a Tdsp a IRI a DEFa a
+ × + × + ×= =
+ × × + + × + × + × + × + ×
=+ × 12
Case Study 2 (Finished):Concrete CPR B on rural/flat freeway
10 mile (16 km) segment in need of rehabRural freeway4 lanes, southboundAADT: ~80,000; ~25% trucks
Cars Trucks IRILane 1 (Inner) 38% 0.2% 3Lane 2 34% 8% 3Lane 3 16% 42% 3.5Lane 4 (Outer) 13% 49% 4
Compare:- Do Nothing- 10 year CPR B
-Type III, CSA cement13
Construction ScenarioTreatment
Design life
Material Smoothness
CPR B with 3% slab replacement and grinding the entire lane
10 yrs
Type III Rapid Strength Cement (3.2 Mpa in 4 hours)
Smooth Rehab (-2σ)
Medium Smooth Rehab (mean)
Less Smooth Rehab (+2σ)
Calcium Sulpho-Aluminate (CSA) Cement (2.8Mpa in 4 hours)
Smooth Rehab (-2σ)
Medium Smooth Rehab (mean)
Less Smooth Rehab (+2σ) 14
Concrete IRI over 10 years: Lane 1
0
50
100
150
200
250
300
0.0
1.0
2.0
3.0
4.0
0 1 2 3 4 5 6 7 8 9 10
IRI (
in/m
i)
IRI (
m/k
m)
Year
Lane 1: Do NothingLane 1: Less Smooth Rehab (+2σ)Lane 1: Medium Smooth Rehab (Mean)Lane 1: Smooth Rehab (-2σ)
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Grinding
0
50
100
150
200
250
300
0.0
1.0
2.0
3.0
4.0
0 1 2 3 4 5 6 7 8 9 10
IRI (
in/m
i)
IRI (
m/k
m)
Year
Lane 4: Do NothingLane 4: Less Smooth Rehab (+2σ)Lane 4: Medium Smooth Rehab (Mean)Lane 1: Smooth Rehab (-2σ)
Concrete IRI over 10 years: Lane 4
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Grinding
Concrete Mean Texture Depth (MTD) Progression
From: Shreenath Rao and James W. Mack, Longevity of Diamond-Ground Concrete Pavements, Transportation Research Record, Vol 1684, 1999
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0.0
0.2
0.4
0.6
0.8
1.0
1 2 3 4 5 6 7 8 9 10
MT
D (m
m)
Year
Rehabilitated LanesDo Nothing
Energy in Use Phase with 0 & 3% Traffic Growth (US unit)
18
1.2E+7
1.4E+7
1.5E+7
1.7E+7
1.8E+7
1.5E+9
1.7E+9
1.9E+9
2.1E+9
2.3E+9
2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
Equ
ival
ent G
asol
ine
(gal
)
Tota
l Ene
rgy
(MJ)
Year
3% Traffic growth + Do Nothing + No fuel economy improvement3% Traffic growth + Do Nothing + Fuel economy improvement0% Traffic growth + Do Nothing + Fuel economy improvement0% Traffic growth + Less Smooth Rehab + Fuel economy improvement0% Traffic growth + Medium Smooth Rehab + Fuel economy improvement0% Traffic growth + Smooth Rehab + Fuel economy improvement
-4.7E+6
-2.7E+6
-6.9E+5
1.3E+6
3.3E+6
5.3E+6
-1.5E+8
-5.0E+7
5.0E+7
1.5E+8
2.5E+8
3.5E+8
4.5E+8
5.5E+8
6.5E+8
Material Production Construction Use
Equ
ival
ent G
asol
ine (
L)
Tota
l Ene
rgy
Savi
ng C
ompa
red
to D
o N
othi
ng
(MJ)
Phase
3% Traffic growth: Smooth Rehab compared to Do Nothing3% Traffic growth: Medium Smooth Rehab compared to Do Nothing3% Traffic growth: Less Smooth Rehab compared to Do Nothing0% Traffic growth: Smooth Rehab compared to Do Nothing0% Traffic growth: Medium Smooth Rehab compared to Do Nothing0% Traffic growth: Less Smooth Rehab compared to Do Nothing
10-year energy savings from materials (Type III), construction, use phase compared to “Do Nothing”
PCA Stripple Huntzinger
0
19
Cumulative energy savings from materials (Type III), construction, use phase compared to “Do Nothing”
Construction
20
-1.6E+6
3.4E+6
8.4E+6
1.3E+7
1.8E+7
-5.0E+7
5.0E+7
1.5E+8
2.5E+8
3.5E+8
4.5E+8
5.5E+8
6.5E+8
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
Equ
ival
ent G
asol
ine (
L)
Cum
mul
ativ
e Ene
rgy
Savi
ng C
ompa
red
to D
o N
othi
ng (M
J)
Year
3% Traffic growth: Smooth Rehab compared to Do Nothing3% Traffic growth: Medium Smooth Rehab compared to Do Nothing3% Traffic growth: Less Smooth Rehab compared to Do Nothing0% Traffic growth: Smooth Rehab compared to Do Nothing0% Traffic growth: Medium Smooth Rehab compared to Do Nothing0% Traffic growth: Less Smooth Rehab compared to Do Nothing
Case Study 1 (Finished):Asphalt overlay on rural/flat freeway
10 mile (16 km) segment in need of rehabRural freeway2 lanes, southboundAADT: 34,000; ~35% trucks
Passenger Trucks
Inner Lane 77% 9%
Outer Lane 23% 91%
Compare:- Do Nothing-10 year rehab
-HMA, RHMA 21
Construction Scenarios: Case Study 1
HMA Type
Design life
Treatment Cross Section Smoothness
HMA 10 YearsMill & Overlay
45 mm (0.15’) Mill + 75 mm (0.25’) HMA with 15% RAP
Smooth Rehab
Less smooth Rehab
RHMA 10 yearsMill & Overlay
30 mm (0.1’) Mill + 45 mm (0.15’) RHMA
Smooth Rehab
Less smooth Rehab
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Asphalt IRI Scenarios over 10 years*
* 1st draft from empirical data, needs review and modeling
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
1 2 3 4 5 6 7 8 9 10
IRI (
m/k
m)
Year
Smooth Rehab: Ln1 Smooth Rehab: Ln2Less Smooth: Ln1 Less Smooth: Ln2Do Nothing: Ln1 Do Nothing: Ln2
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Asphalt MPD Progression from CA data* (For rehabilitated lanes)
* 1st draft from empirical data, needs review and modeling
0
0.2
0.4
0.6
0.8
1
1.2
1 2 3 4 5 6 7 8 9 10
MPD
(mm
)
Year
HMA: Ln1 HMA: Ln2
RHMA: Ln1 RHMA: Ln2
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-1.6E+6
-6.3E+4
1.4E+6
2.9E+6
4.4E+6
5.9E+6
7.4E+6
-5.0E+7
0.0E+0
5.0E+7
1.0E+8
1.5E+8
2.0E+8
2.5E+8
Feedstock Energy Material Production
Construction Use
Equ
ival
ent G
asol
ine
(L)
Tota
l Ene
rgy
Savi
ng C
ompa
red
to D
o N
othi
ng
(MJ)
Phase
3% Traffic growth: Init. IRI=1 compared to Do Nothing3% Traffic growth: Init. IRI=1.67 compared to Do Nothing0% Traffic growth: Init. IRI=1 compared to Do Nothing0% Traffic growth: Init. IRI=1.67 compared to Do Nothing
10-year energy savings from materials, construction (HMA), and use phase compared to “Do Nothing”
USLCI Athena
Stripple Ecoinvent
25
0
-1.6E+6
-6.3E+4
1.4E+6
2.9E+6
4.4E+6
5.9E+6
7.4E+6
-5.0E+7
0.0E+0
5.0E+7
1.0E+8
1.5E+8
2.0E+8
2.5E+8
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
Equ
ival
ent G
asol
ine
(L)
Cum
mul
ativ
e E
nerg
y Sa
ving
Com
pare
d to
D
o N
othi
ng (M
J)
Year
3% Traffic growth: Init. IRI=1 compared to Do Nothing3% Traffic growth: Init. IRI=1.67 compared to Do Nothing0% Traffic growth: Init. IRI=1 compared to Do Nothing0% Traffic growth: Init. IRI=1.67 compared to Do Nothing
Cumulative energy savings from materials, construction
(HMA), and use phase compared to “Do Nothing”
Construction
0
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Acknowledgement
• Thanks to California Department of Transportation and University of California for research funding
• This project is part of a larger pooled-effort program called “Miriam”, partnering with 8 European National Highway Research Laboratories– Studying pavement rolling resistance
Disclaimer
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The contents of this presentation reflect the views of the authors, who are responsible for the facts and accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the State of California, the Federal Highway Administration, the University of California, the MIRIAM project or its sponsors, the International Society for Concrete Pavements, or the International Society for Asphalt Pavements. This presentation does not constitute a standard, specification, or regulation.
Supply Curve
• First-order quantitative “what-if” analysis• Prioritizing Climate Change Mitigation Alternatives: Comparing
Transportation Technologies to Options in Other Sectors, Lutsey, N. (2008)
Initial cost
Net costs = initial cost + direct energy saving benefits
Bang for your buck metric: $/ton CO2e vs CO2e reduction
Details: Future Work• Finish the rest of the case studies• Finish the application to California’s
network• Include the effect of construction work
zone traffic and traffic congestion (urban cases)
• Perform sensitivity analysis on parameters such as RR range, materials, traffic closure, etc.
• Apply the result to the Cost-Effectiveness curve
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Future Work (Cont’d)• Investigate the dissipated energy on
asphalt, composite, semi-rigid pavement• Investigate the macro-texture progression
of concrete pavement• Investigate the effect of congestion on
rolling resistance impact• Investigate the overall rolling resistance
on composite and semi-rigid pavement
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