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)

15

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

16

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)

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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

23

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.

Questions?

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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