Climate-change Informed Life Cycle Assessment for Pavement ... · 6th ISAP APE; Haslett, Dave, Mo;...

6th ISAP APE; Haslett, Dave, Mo; October 2019

Climate-change Informed Life Cycle Assessment for Pavement

Rehabilitation and Maintenance Decision Process

Eshan V. DaveUniversity of New Hampshire

http://www.uiuc.edu/

6th ISAP APE; Haslett, Dave, Mo; October 2019 2

Katie HaslettPh.D. Student

Co-AuthorsWeiwei Mo

Assistant Professor



Outline Introduction• Motivation for study Background• Life Cycle Assessment (LCA)• Pavement Management Methodology• Future climate data• Realistic traffic conditions• Pavement performance Selected LCA Results Conclusion and Summary



Motivation…U.S. Conditions U.S. highway system is one of the world’s largest consisting of 7 million miles (11.3 million km) of road length According to USDOT, by the year 2045:• U.S. population will spend over 42 hours stuck in traffic

each year per person• Annual cost of congestion in delays and lost fuel is

estimated to be $160 billion• Annual cost of truck congestion alone is $28 billion

Need to design and maintain roadways efficiently and effectively over service life

4



Traditional pavement management is based on performance and economic factors… do not incorporate environmental impactsPavement analysis and performance evaluation is often conducted using historical climate data• However, flexible pavements are sensitive to

climatic stressors and performance is influenced by changing climate

5

Challenges (1/2)



Including both user and agency impacts during the operation phase of an LCA is important for resiliency ofpavement management system

• Unlike so called “Cradle to Gate” LCA for product “carbon foot-printing”

6

Challenges (2/2)



To develop LCA framework that incorporates:

Future climate projections Realistic traffic conditions Ability for consequential analyses of various

Maintenance and rehabilitation (M&R) strategies

Pavement and Asphalt Materials:

Performance prediction with future climate

Quantify changes in fuel consumption and resulting emissions due to pavement distresses and roughness (IRI)

LCA inventory7

Objectives



LCA is a technique used to assess cost and environmental impacts associated with various stages of a product’s design life.

8

Background: LCA

For LCA to be impactful, all phases of system should be included!LCA OutcomesGlobal Warming Potential (GWP)Cumulative Energy Demand (CED)Life Cycle Costs (LCC)



Pavement Management Optimization using pavement performance for M&R decisions

9

Background: Pavement Management



Outline Introduction• Motivation for study Background• Life Cycle Assessment (LCA) Methodology• Future climate data• Realistic traffic conditions• Pavement performance Selected LCA Results Conclusion and Summary Contributions



1. Select Project Domain and Identify Baseline Conditions

2. Inventory

3. Realistic Traffic Data

4. Pavement Performance

5. LCA Impact Assessment

Length: 26 km / 16 miles Location: I-495, MA Baseline: No maintenance treatment (also for comparison,

constant traffic speed)

Material properties, traffic volumes, climatic information, pavement structures

Unit impacts of material, construction, operation etc.

Data mining from Google maps for trip delay times Varying acceleration and deceleration scenarios inferred

from traffic delay (green, orange, red, dark red) Utilize EPA MOVES software to calculate emissions

Simulate pavement cross sections with varying materials as well as maintenance & rehabilitation options

Consider future climate projections Pavement distresses, International Roughness Index (IRI)

Quantify GWP, CED and LCC for each scenario Optimize

General Methodology



12

Existing Subgrade

2" Surface Course

5" AC Binder Course

8" AC Base Course

24" Granular Base

5 cm

12.5 cm

20 cm

60 cm

Pavement Cross-Section and Mixtures

Asphalt Mixture CharacteristicsCross

Section # Mix Name PG Grade AC (%) VMA VFA

Surf

ace

Cou

rses

1 ARGG-1 58-28 7.8 19.4 77.82 ARGG-2 58/28 7.6 18 803 T-1 64-28 5.4 15.57 77.644 THS-1 76-28 5.4 15.57 77.645 SHM-1 70-34 (PMA) 5.5 14.9 80.5

Bin

der

Cou

rse

- B-1 64-28 4.8 15 68.6

Bas

e C

ours

e

- BB-1 64-28 4.8 14.8 69



Coupled Model IntercomparisonProject Phase 5 (CMIP5) Variables: Precipitation, Max. and Min. temperature Coverage: 1950-2099 Temporal Resolution: Daily Spatial Resolution: 12km by 12km grid Climate Scenarios:• Current Emissions• RCP4.5: Radiative forcing stabilizes

at ~4.5 W/m2 after 2100• RCP8.5: Radiative forcing reaches

~8.5 W/m2 at 2100

13

Downscaling is the general name for a procedure to take information known at large scales to make predictions at local scales.

Future Climate Data



-20.00-10.00

0.0010.0020.0030.0040.0050.00

0 360 720 1080 1440 1800 2160 2520 2880 3240 3600Tem

per

atu

re (

deg

. C

)

Days

Maximum Daily Temperature (1950-1960)

-20.00-10.00

0.0010.0020.0030.0040.0050.00

0 360 720 1080 1440 1800 2160 2520 2880 3240 3600Tem

per

atu

re (

deg

. C

)

Days

Maximum Daily Temperature (2020-2030)

CMIP5: RCP 8.5

14




2. Inventory













General Methodology



RTD: Accounts for user costs/emissions with respect to realistic driving conditions Google Maps Daily hourly patterns• Each hour for each day of week (9-10 am on

Monday)

16

Realistic Traffic Data (1/3)



Simulate realistic driving conditions• Acceleration, deceleration, stoppage in various

phases (green, orange, red, maroon)• SHRP2 Naturalistic

Driving StudyHourly VehicleVolumes• MassDOT Traffic

Data ManagementSystem

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EPA Motor Vehicle Emissions Simulator (MOVES)• Vehicle specific power-

based simulations of fuel usage and emissions

• GWP and CED as a function of road roughness (IRI) and realistic traffic (accelerations, decelerations, stoppage etc.)

18


y = 0.003x + 36.206

36.336.436.536.636.736.836.9

0 50 100 150 200 250Glo

bal W

arm

ing

Pote

ntia

l (G

g C

O2

Eq.)

IRI (in/mi)

Monthly GWP




2. Inventory













General Methodology



Maintenance & rehabilitation alternatives

• Do Nothing Reconstruct (DNR)• Crack Sealant (CS)• Microsurfacing (MS 2.2 m/km or 140 in/mi)• Microsurfacing (MS 2.5 m/km or 160 in/mi)• Cold-In-Place Recycling (CIR)• Mill & Overlay (MO)



Performance predicted for 6 M&R scenarios:• AASHTOWare Pavement ME

• Do Nothing Reconstruct (DNR) Cold-In-Place Recycling (CIR) Mill & Overlay (MO)

• Pavement Sections and Data:• Crack Sealant (CS)• Microsurfacing (MS 2.2 m/km or 140 in/mi)• Microsurfacing (MS 2.5 m/km or 160 in/mi)

Thresholds:• Initial IRI = 1 m/km (63 in/mi)• Terminal IRI = 2.7 m/km (172 in/mi)• Minimum 3 full M&R cycles

21

Literature Sources: Cole et al., 2016 Gary Fitts, 2003 Nair et al., 2015 Lane et al., 2005 Mousa et al., 2018 Stephen Damp, 2008 …others

Performance Prediction



Pavement Performance

Maintenance Alternatives




Pavement Performance





Outline Introduction• Motivation for study Background• Life Cycle Assessment (LCA) Methodology• Future climate data• Realistic traffic conditions• Pavement performance Selected LCA Results Conclusion and Summary Contributions



6.4% increase in GWP and CED with the inclusion of RTD (monthly average shown)

25

Results: Effects of RTD



0

5

10

15

20

25

30

35

40

CS DNR CIR MO MS140

MS160

MS140

CS MO DNR CIR MS160

Cross-Section-1 Cross-Section 3

Year

ly C

ost (

Milli

ons

of D

olla

rs)

Yearly Cost ComparisonUser Cost Const./Maint. Cost

Results: Life Cycle Costs with Future Climate

26

17.7% difference in operation cost (users)

41.1% difference in construction/maint. cost (agency)



Most Economical

Least Economical

Net Present Value (Historical Climate Data)Cross

SectionMaintenance Alternative (in millions of $)

DNR CS MS (IRI =140) MS (IRI =160) CIR MO1 $232 $219 $187 $191 $158 $1572 $231 $219 $187 $156 $158 $1573 $232 $224 $187 $191 $158 $1574 $267 $252 $219 $199 $1595 $299 $277 $253 $215 $160

Net Present Value (Future Climate Data)Cross

SectionMaintenance Alternative (in millions of $)

DNR CS MS (IRI =140) MS (IRI =160) CIR MO1 $264 $252 $212 $212 $158 $1573 $269 $256 $218 $212 $158 $157

Average percent difference from MO alternative (most economical):• CIR = 0.678 %• MS 160 = 13.1 %• MS 140 = 17.5 %• CS = 33.7 %• DNR = 38.3 %

• CIR = 0.683 %• MS 160 = 29.6 %• MS 140 = 31.0 %• CS = 47.0 %• DNR = 51.7 %

With Future Climate Data

Results: Life-Cycle Costs(Agency)



The average increase in NPV using future climate to historical climate with respect to the most economical M&R strategy (Mill and Overlay):• CIR = 0.79%• MS 160 = 77.5%• MS 140 = 55.6 %• CS = 33.0%• DNR = 29.7 % For this highway MO or CIR may be more economically

resilient M&R strategy in terms of costs in future climate conditions.

28

Results: M&R Cost Comparisons



0

100

200

300

400

500

600

CIR CS DNR MO MS140

MS160

CIR CS DNR MO MS140

MS160

Cross-Section 1 Cross-Section 3

Glo

bal W

arm

ing

Pote

ntia

l (G

g of

CO

2eq

/yr)

Global Warming Potential Comparison

Const./Maint. Users

29

19.6% difference in operation GWP (users)

70.1% difference in construction/maintenance GWP (agency)

Results: Lifetime Impacts



Use-phase should not be ignored! Substantially larger GWP contributions Cumulative user costs are substantially larger than

agency costs Inclusion of RTD is critical for LCA 6.4% increase GWP and CEDUse of historic climate data in pavement management could result in lack of resiliency Up to 8% user and 38% agency GWP and CED

increase

30

Summary and Conclusions (1/2)



In general, the ranking of M&R alternatives remains constant but the margin of difference in costs increased among different M&R scenariosOptimization of pavement structure, M&R type and timing has different impacts for agencies and users

31

Summary and Conclusions (2/2)



Explore more M&R strategies and a combination of strategies over the service life of the roadway

Investigate the effect of future climate on the effectiveness of certain M&R strategies

Perform LCA using a probabilistic approach, esp. for future climate projections

Improve operational impact component of LCA

Expand framework for network level pavement management decision support

32

Future Extensions



1. Qiao, Y., Dave, D., Parry, T., Valle, O., Mi, L., Ni, G., Yuan, Z., and Zhu, Y., 2019. Life Cycle Costs Analysis of Reclaimed Asphalt Pavement (RAP) Under Future Climate. Sustainability 2019, 11(19), 5414; https://doi.org/10.3390/su11195414.

2. Haslett, K.E., E.V. Dave, and W. Mo, “Realistic Traffic Condition Informed Life Cycle Assessment: Interstate 495 Maintenance and Rehabilitation Case Study,” Sustainability, 11(12), p. 3245, 2019. https://doi.org/10.3390/su11123245

3. Knott, J.F., J.E. Sias, E.V. Dave, and J.M. Jacobs, “Seasonal and Long-Term Changes to Pavement Life Caused by Rising Temperatures from Climate Change, Transportation Research Record, 2019. DOI: https://doi.org/10.1177/0361198119844249

4. Knott, J.F., J.M. Jacobs, J.E. Sias, P. Kirshen, and E.V. Dave, “A Framework for Introducing Climate-Change Adaptation for Pavement Management,” Sustainability, 11(16), p. 4382, 2019. https://doi.org/10.3390/su11164382

5. Y. Qiao, E.V. Dave, and T. Parry, “Life-Cycle Cost Analysis of Reclaimed Asphalt Pavement (RAP) under Future Climate: A New Hampshire Interstate-95 Case Study,” Proceedings of the International Seminar on Resilient Roads and Climate Change Adaptation, Beijing, China, 2018

6. O. Valle, Y. Qiao, E. Dave, and W. Mo, “Life Cycle Assessment of Pavements Under a Changing Climate,” Pavement Life-Cycle Assessment, ISBN: 978-1-138-06605-2, CRC Press, pp. 241-250, 2017. http://dx.doi.org/10.1201/9781315159324-25

7. C. DeCarlo, W. Mo, E.V. Dave, and J. Locore, “Sustainable Pavement Rehabilitation Strategy using Consequential Life Cycle Assessment: An Example of Interstate 95,” Proceedings of the Tenth International Conference on the Bearing Capacity of Roads, Railways and Airfields (BCRRA 2017), Taylor and Francis Group, London, pp. 2193-2200, 2017

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Publications


https://doi.org/10.3390/su11195414

https://doi.org/10.3390/su11123245

https://doi.org/10.1177/0361198119844249

https://doi.org/10.3390/su11164382

http://dx.doi.org/10.1201/9781315159324-25


Acknowledgements

• Minnesota Department of Transportation• UNH Center for Infrastructure Resilience

to Climate

• Contact: [email protected]

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Thank you for your attention!


mailto:[email protected]

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