pavement vehicle interactions – does it matter for virginia? franz-josef ulm, mehdi akbarian,...

Pavement Vehicle Interactions – Does it Matter for Virginia?

Franz-Josef Ulm, Mehdi Akbarian, Arghavan Louhghalam

ACPA. Virginia Concrete Conference

March 6, 2014

With the support of the VDOT Team – Thank YOU!

Motivation: Carbon Management

Pavement design and performance:– Fuel saving– Cost saving– GHG reduction

• Strategy for reducing air pollution!

non profit support group for the Route 29 Bypass

OUTLINE3

• This is not about Concrete vs. Asphalt, this is about unleashing opportunities for Greenhouse Gas savings

• Pavement-Vehicle Interaction: – Roughness/ Vehicle Dissipation– Deflection/ Pavement Dissipation

• Data Application:–US Network–VA Network

• Carbon Management: how to move forward

• Force Distribution in a passenger car vs. speed as a percentage of available power output (Beuving et al., 2004; cited in Pouget et al. 2012)

Context: Rolling Resistance

Due to PVIs: Texture, Roughness and Deflection

• Pavement Texture: Tire industry. Critical for Safety. Tire-Pavement contact area.

• Roughness/Smoothness*: – Absolute Value = Vehicle dependent.– Evolution in Time: Material Specific

• Deflection/Dissipation Induced PVI**:– Critical Importance of Pavement Design Parameters:

Stiffness, Thickness matters! – Speed and Temperature Dependent, specifically for inter-city

pavement systems

Key Drivers of Rolling Resistance

*Zaabar, I., Chatti, K. 2010. Calibration of HDM-4 Models for Estimating the Effect of Pavement Roughness on Fuel Consumption for U.S. Conditions. Transportation Research Record: Journal of the Transportation Research Board, No. 2155. Pages 105-116.** Akbarian M., Moeini S.S., Ulm F-J, Nazzal M. 2012. Mechanistic Approach to Pavement-Vehicle Interaction and Its Impact on Life-Cycle Assessment. Transportation Research Record: Journal of the Transportation Research Board, No. 2306. Pages 171-179.

ROUGHNESS / IRI: Dissipated Energy

• Quarter-Car Model*• Mechanistic/PSD**: with:

IRI

• HDM-4 Model***:

IRI measured at c=80 km/h = 50 mph= Damping of Suspension System (Vehicle Specific)

𝒛 (𝒕) 𝐶𝑆

(*) Sayers et al. (1986). World Bank Technical paper 46

(**) Sun et al. (2001). J. Transp. Engrg., 127(2), 105-111.(***) Zaabar I., Chatti K. (2010) TRB, No. 2155, 105-116.

VehicleSpecific

ReferenceIRI-Value

VEHICLE–SPECIFIC ENERGY DISSIPATION & EXCESS FUEL CONSUMPTION

ROUGHNESS: HDM-4 MODEL

• Zaaber & Chatti (2010)

• Input:– Measured IRI (t)– Reference IRI, – Vehicle Type– Traffic Volume (AADT, AADTT)– Truck Traffic Distribution

• Output:– Excess Fuel Consumption due to

Roughness– For vehicle type and total

*Zaabar, I., Chatti, K. 2010. Calibration of HDM-4 Models for Estimating the Effect of Pavement Roughness on Fuel Consumption for U.S. Conditions. Transportation Research Record: Journal of the Transportation Research Board, No. 2155. Pages 105-116.

𝛿𝐸=% 𝐸0 ⟨ IRI − 𝐼𝑅𝐼 0 ⟩

MIT Model Gen II: Viscoelastic Top Layer

P

k

E = htEs s

c

Speed Dependence

Temperature dependence

Relaxation Time

– Bituminous Materials* – Cementitious Materials**:

* Pouget et al. (2012); William, Landel, Ferry (1980) ** Bazant (1995)

𝒄𝒄𝒓=𝓁𝑠(𝑘 /𝑚)1/2Winkler Length

Consideration of Top-Layer Viscoelastic behavior, including temperature shift factor:

10 20 30 40 50 60 70

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Pouget et al. (2012)MIT Model

TEMPERATURE [Deg.C]

DISS

IPAT

ED E

NER

GY [M

J/km

]

c= 50 km/h

0 10 20 30 40 50 60 700

0.2

0.4

0.6

0.8

1

1.2

1.4

Pouget et al. (2012)MIT Model

TEMPERATURE [Deg.C]

DISS

IPAT

ED E

NER

GY [M

J/km

]

c= 100 km/h

Calibration/Validation | Asphalt Lit. Data

• Model-Based Simulations

𝛿𝐸=𝑐𝑐𝑟

𝑐×

𝑃2

𝑏𝑘𝓁𝑠2𝐹 ( 𝑐𝑐𝑐𝑟

;𝜁=𝝉 (𝑻 )𝑐𝑐𝑟𝓁𝑠

) Vehicle speed ton truck (distribution of loads according to HS 20-44) m (lane width) 40,264 MPa, 35 MPa/m, 0.22 m s

𝜏 (𝑇 )=𝜏0 (𝑇 0 )×𝑎𝑇 (𝑇 )

Calibration c=100 km/h

Validation c=50 km/h

0 20 40 60 80 100 1200

0.05

0.1

0.15

0.2

0.25

0.3

0.35

SPEED [km/h]

DIS

SIP

AT

ED

EN

ER

GY

[M

J/k

m]

New Feature: Temperature and Speed Dependence

(Example taken from Pouget et al. (2012)

Gen I

50 Deg. F

68 Deg. F

Can we do better? – Yes, we can!

Pavement RoughnessPavement Deflection

Structure and Material

MEPDG2011 MIT-Model

PVI Impact

LCA “plus”: MOVING LCA IN THE DESIGN SPACE

MEPDG

StructurallySound Design

INPUT:- Structure- Materials- Traffic- Climate- Design

Criteria

LCA/LCCAEmbodied + Use

OUTPUT:- E(t)- IRI(t)- Maintenance- Traffic-evolution

SustainableDesign

OUTPUT:- Fuel Con.- GHG- Costs

OUTPUT:- Comparative

Design- Design

Alternatives

Network ApplicationUS and VA

FHWA/LTPP General Pavement Study sections (GPS)

Data:Roughness• IRI (Year)• Traffic• Location• Pavement typeDeflection:• Top layer modulus E• Subgrade modulus k• Top layer thickness h• Other layer properties

GPS1: AC on Granular Base

GPS2: AC on Bound Base

GPS3: Jointed Plain CP (JPCP)

GPS4: Jointed Reinforced CP (JRCP)

GPS5: Continuously Reinf. CP (CRCP)

GPS6: AC Overlay of AC Pavement

GPS7: AC Overlay of PCC

GPS9: PCC Overlay of PCC

AC ComPCC

VA Interstate: Road Classification

Pavement type analyzed

Type Lane-mile Center-mileAsphalt (BIT) 3,131 1,416Concrete (CRCP, JRCP)Composite (BOC, BOJ)

4901,221

174459

Total 4,841 2,05065%8%

18%

3%7%

BITBOCBOJCRCPJRCP

VA Label Type LTPP Equivalent

BIT Bituminous GPS 1,2

JRCP Jointed reinforced CP GPS 4

CRCP Continuously reinforced CP GPS 5

BOJ Bituminous over JPCP GPS 6

BOC Bituminous over CRCP GPS 9

VA Interstate: Data Overview

Data: • 15 interstates, 2 direction• Years: 2007-2013• Section ID• Section milepost• AADT, AADTT• Layer thicknesses• Material properties (2007)• IRI (t)

ACComPCC

Pavement Type

Annual Average Daily Truck Traffic (AADTT)

AADTT

Deflection -Induced PVI

Temperature and Speed Sensitivity: AC in VA

𝛿𝐸=− 𝑃 𝑑𝑤𝑑𝑋

=𝑐𝑐𝑟

𝑐𝑃2

𝑏𝑘𝓁𝑠2×𝐹 ( 𝑐

𝑐𝑐𝑟

;𝜁=𝝉(𝑻 )𝑐𝑐𝑟𝓁𝑠

)Asphalt Concrete (BIT)

Temperature sensitivityone order of magnitude higher dissipation

(T= 50 vs. 65 F)

tons (3 axles); mph; s; VA Interstate database for distributions of of AC

Asphalt Concrete (BIT)

tons (3 axles); ; s

Speed Sensitivityhalf order of magnitude higher dissipation

( vs. 60 mph)

1 100

2

4

6

8

10

12

T=10C/50F

T=20C/65F

Dissipated Energy [MJ/km]

PD

F/1

1 100

2

4

6

8

10

12

c=60 mphc=20 mph

Dissipated Energy [MJ/km]P

DF

/1

Temperature Sensitivity: PCC in VA

𝛿𝐸=− 𝑃 𝑑𝑤𝑑𝑋

=𝑐𝑐𝑟

𝑐𝑃2

𝑏𝑘𝓁𝑠2×𝐹 ( 𝑐

𝑐𝑐𝑟

;𝜁=𝝉(𝑻 )𝑐𝑐𝑟𝓁𝑠

)Concrete (JRCP, CRCP)

Temperature sensitivitySmall!

tons (3 axles); mph; s; VA interstate database for distributions of of PCC

Speed Sensitivity Small

Concrete (JRCP, CRCP)

tons (3 axles); ; s

[For pure comparison, assume same as for asphalt]

0.001 0.01 0.10

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

T=20C/65F

T=10C/50F

Dissipated Energy [MJ/km]

PD

F/1

0.001 0.01 0.1 10

0.5

1

1.5

2

2.5

c=20 mph

c=60 mph

Dissipated Energy [MJ/km]P

DF

/1

Would this matter for VA?

BIT/ACTemperature sensitivity

10 Deg. can entail one order of magnitude of higher energy

dissipation; thus fuel consumption.

Assume: Bit @ 95%. P=37 tons (3 axles); τ0=0.015s Assume: PCC @ 95%. P=37 tons (3 axles); τ0=0.015s

PCCTemperature sensitivity

10 Deg. can entail half order of magnitude of higher energy

dissipation; thus fuel consumption.

* Temp data from National Oceanic and Atmospheric Administration (esrl.noaa.gov)

Order of magnitude difference

VA Network: PVI Deflection – Truck

0.0001 0.001 0.010

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6Bituminous

Excess Fuel Consumption (gal/mile)

PD

F/1

Excess fuel consumption due to PVI deflection is 10 times higher on bituminous pavements

c= 100 km/h=62.6 mph; T= 16 C/61 F

Annual Excess Fuel Consumption: PVI Deflection*2013 data

FC (gallon/mile)

c= 100 km/h=62.6 mph; T= 16 C/61 F

• PVI-model Gen II:– Accounts for the effect of temperature and

vehicle speed on the dissipated energy.– Quantifies asphalt and concrete sensitivity to

speed and temperature.– Requires one material input parameter:

relaxation time. So far, calibrated and validated using literature data. Link with Master Curve.

– Simple to use, easy to calculate fuel consumption in excel spreadsheet; thus for LCA use phase…

Summary | For Discussion

IRI-Induced PVI

IRI: US Network – VA Data Comparison

IRI distribution of Virginia and the US network are very similar.

<60 60-94 95-119 120-144 145-170 171-194 195-220 > 2200

0.1

0.2

0.3

0.4

0.5

0.6

VA Network

US Network

IRI (in/mile)

Fre

qu

en

cy

<60 60-94 95-119 120-144 145-170 171-194 195-220 > 220

0

0.2

0.4

0.6

0.8

1

1.2

VA Network

US Network

VA – Roughness

Asphalt and composite pavements are maintained equally. Not concrete

<60 60-94 95-119 120-144 145-170 171-194 195-220 > 220

0

0.2

0.4

0.6

0.8

1

1.2

Concrete

Asphalt

Composite

<60 60-94 95-119 120-144 145-170 171-194 195-220 > 2200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

VA Concrete

VA Asphalt

VA Composite

IRI (in/mile)

Fre

qu

en

cy

*2013 data

IRI depends on pavement maintenance

MN (2011)

<60 60-94 95-119 120-144 145-170 171-194 195-220 > 220

0

0.2

0.4

0.6

0.8

1

1.2

ConcreteAsphaltComposite

<60 60-94 95-119 120-144 145-170 171-194 195-220 > 220

0

0.2

0.4

0.6

0.8

1

1.2

Concrete

Asphalt

Composite

VA (2013)

Pavement Roughness (IRI)*2013 data

IRI (in/mile)

Excess Fuel Consumption: PVI Roughness*2013 data

FC (gallon/mile)

Annual Expenditure on all Pavements in VA

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012$0

$50

$100

$150

$200

$250

$300

$350

$400Asphalt Pavement

Concrete Pavement

Year

Pa

ve

me

nt

Ex

pe

nd

itu

re (

Mill

ion

s o

f $

)

Deficient lane miles due to ride quality by pavement type – Interstate

Pavement Type lane-mile (% total) Deficient lane-miles (% total)*AC 3,131 (65%) 157 (46%)PCC 490 (10%) 181 (54%)Total 3,621 (75%) 338 (100%)

Cost aggregated for:- Interstate pavement- Primary pavement- Secondary pavement

Deficient pavement IRI:- Poor: 140-199- Very poor: >200

*VDOT. State of The Pavement 2012. http://www.virginiadot.org/info/resources/State_of_the_Pavement_2012.pdf

• IRI is vehicle specific

• Concrete pavements are under-maintained

• Difference between pavement systems is IRI-development and pavement aging. Data not consistent with national analyses

• Model Development:

Reference in/mile = Political decision

Higher value of reduces the number of roads contributing to excess fuel consumption.

SUMMARY: IRI-induced PVI

𝛿𝐸=% 𝐸0 ⟨ IRI − 𝐼𝑅𝐼 0 ⟩

Total PVI Impact

Network: Annual PVI Truck* – excess FC per mile*2013 data

Impact Reduction through enhanced pavement design and management

BIT BOC BOJ CRCP JRCP0

2000

4000

6000

8000

10000

12000

14000

16000

0

20

40

60

80

100

120

140

160Roughness Deflection

An

nu

al E

xc

es

s F

ue

l Co

ns

um

pti

on

(G

al/m

ile)

An

nu

al E

xc

es

s C

O2

e E

mis

sio

ns

(t

on

s/m

ile)

c= 100 km/h=62.6 mph; T= 16 C/61 F

Network: Annual PVI Truck – Total FC

2007 2008 2009 2010 2011 2012 20130

1,000,000

2,000,000

3,000,000

4,000,000

5,000,000

6,000,000

7,000,000

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

Annual Truck FC Roughness Annual Truck FC Deflection

Ex

ce

ss

Fu

el C

on

su

mp

tio

n (

Ga

llon

s)

Ex

ce

ss

CO

2e

Em

iss

ion

s (

ton

s)

c= 100 km/h=62.6 mph; T= 16 C/61 F

PVI Total Impact: Roughness and Deflection*2013 data: Trucks

FC (gallon/mile)

c= 100 km/h=62.6 mph; T= 16 C/61 F

CARBON MANAGEMENT = Pavement Performance!

• PVIs contribute highly to pavement induced fuel consumption and GHG emissions

• Concrete pavements not utilized to same performance as in other roadway networks– High deficient lane-miles

– Older pavements

• Room for GHG reduction!

ENGINEERING100%

Moving tire (top view) is on slope= Deflection induced eXtra-Fuel Consumption

CARBON MANAGEMENT = Cost – Benefit!

ECONOMICS = LINGUA FRANCA OF IMPLEMENTATION• LCCA is tool for supporting design

decisions• Analyses typically occur after design

process is complete• Standard practice does not account for

uncertainty• FHWA does not provide guidance on

characterizing inputs and uncertainty

ECONOMICS100%

I N V E S T – I N N O VAT E – I N V I G O R AT E - I M P L E M E N T

LCCA VALUE PROPOSITION

• Context: $ 2 Trillion Infra-structure renewal job within tightest budgetary constraints.

• Problem: Volatility of construction materials pricing for a fiscally sound decision making.

• Solution*: A new LCCA methodology with probabilistic cost modeling of pavement projects, so that decision-makers: – Understand the risk of an investment;– Select a design based on risk perspective.

ECONOMICSDecision Makers (local, national,

and beyond)

IMPLEMENTATION@ State Level: Case Study

* Swei, Gregory & Kirchain (2013)

Uncertainty is pervasive in pavement LCCA

Construction Operation

Long life-cycle

Cash

Flo

w

Uncertainty & Risk

Decisions long before

construction

Uncertainty in unit construction costs

Uncertainty in material price

evolution

Uncertainty in timing of M&R activities

CSHub approach characterizes uncertainty

for all three areas

Statistically Characterize Uncertainty

CSHub LCCA methodology is integrated with pavement design process

Present

Future

LCCA Model

Is the difference significant?

Relative risk

Characterize drivers of uncertainty

MEPDG Output

FHWA guidance is limited

IMPLEMENTATION: LCCA – Why does it matter?

• ECONOMICS = LINGUA FRANCA OF IMPLEMENTATION

ECONOMICS100%

26.8 26.9 27.0 27.1 27.2 27.3 27.4 27.5 27.6 27.7 27.80%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

De-sign ADe-sign B

NPV (Millions of $'s)

Cu

mu

lati

ve P

rob

abili

ty

Gambling withCost overrun

Minimizing Risk

Translating price volatility into value proposition for Decision Makers

Analysis:• LCCA & PVI • Pavement maintenance and PVI• Impacts from pavement age

Data needs:• Longer timeframe (7 years doesn’t cover full pavement lifecycle)• Pavement maintenances and activity• More PCC data (i.e. I-295)

Implementation:• Let’s see where this can take us … TOGETHER !

What’s next?

We seek your input!

Thank you.

References:• Louhghalam, A.; Akbarian, M., Ulm, F-J. (2013) Fluegge's Conjecture: Dissipation vs. Deflection Induced

Pavement-Vehicle-Interactions (PVI); J. Engrg. Mech., ASCE.• Louhghalam, A.; Akbarian, M., Ulm, F-J. (2013) Scaling relations of dissipation-induced pavement-vehicle-

interactions; TRB.• http://web.mit.edu/cshub/

http://web.mit.edu/cshub/

• Beyond my pay grade, but…

• CARBON MANAGEMENT is a vehicle of INFRASTUCTURE MANAGEMENT

• Quantitative Sustainability

• Together, let’s make it a reality…

Predicting the future?

Main distresses of PCC pavements

JPCP Distresses (%slabs)Interstate

D4 D5 D9

Transverse Cracking 11% 10% 0%

Corner Breaks 1% 1% 2%

PCC Patching 8% 2% 2%

Asphalt Patching 13% 12% 1%

Average Pavement Roughness (in/mile) Poor 140-199JRCP IRI 146 128 104

AC IRI 87 73 88

Pavement IRI is a function of pavement maintenance

Comparison: Gen 1 – Gen 2 Model

GPS-1: AC on Granular Base GPS-2: AC on Treated Base

Vehicle speed tons (on 3 axles) m (lane width) (GPS 1, 2 - LTPP Network) sTemperature

Gen

1 I

NP

UT

Gen

2 I

NP

UT

0.0001 0.001 0.01 0.1 1 100

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Gen-1

Gen-II

DISSIPATED ENERGY [Ltr/100km]

PD

F/1

T=10C/50F (+/- 10C)c=100 km/h (62.5mph)

0.0001 0.001 0.01 0.1 1 100

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Gen-I

Gen-II

DISSIPATED ENERGY [Ltr/100km]

PD

F/1

T=10C/50F (+/- 10C)c=100 km/h (62.5mph)

That is, Gen I model is a lower bound.Gen II is more accurate for local response, but requires (at least) one more parameter.

Viscoelastic Modeling | Master Curve

Load Frequency (Speed)

Temperature 𝑎𝑇=exp (−𝐶1(𝑇 −𝑇 𝑟𝑒𝑓 )𝐶2+(𝑇 −𝑇𝑟𝑒𝑓 ) )

Simplified approach:1 - Accounts for the load frequency effect using a simple Maxwell model in frequency range of interest.

2 - Accounts for temperature effect in the same way as asphalt literature (e.g. William Landel Ferry equation)

From Pouget et al. (2012)

Principle of Viscoelastic Model Fitting (Using Master Curve)

complicated viscoelastic model

Fit for the entire frequency range

Simplified (Maxwell) viscoelastic model

Fit for applicable frequency rangeFind t and E

Simplified Maxwell model along with the WLF law accounts for the temperature dependency.

Maxwell model with temperature dependency

Frequency rangeof interest

pavement vehicle interactions – does it matter for virginia? franz-josef ulm, mehdi akbarian,...

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