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
Slide 2
OUTLINE
•
•
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:
•
Carbon Management: how to move forward
– US Network
– VA Network
3
Slide 3
Context: Rolling Resistance
• 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)
Due to PVIs: Texture, Roughness and Deflection
Slide 4
Key Drivers of Rolling Resistance
• 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
*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.
Slide 5
ROUGHNESS / IRI: Dissipated Energy
VEHICLE–SPECIFIC ENERGY DISSIPATION & EXCESS FUEL CONSUMPTION
• Quarter-Car Model*
• Mechanistic/PSD**: 𝛿𝐸 =
1
1
2
𝐶𝑆 𝑧 = 𝐶𝑆 (𝜎𝑧 )2 with:
𝑐
𝜎𝑧 =
𝒛(𝒕)
𝐶𝑆
𝑐
𝑐
2/𝜋
IRI
• HDM-4 Model***:
𝛿𝐸 = %𝐸0 IRI−𝐼𝑅𝐼0
Vehicle
Specific
IRI measured at c=80 km/h = 50 mph
𝐶𝑆 = Damping of Suspension System
(Vehicle Specific)
(*) Sayers et al. (1986). World Bank Technical paper 46
Reference
IRI-Value
(**) Sun et al. (2001). J. Transp. Engrg., 127(2), 105-111.
(***) Zaabar I., Chatti K. (2010) TRB, No. 2155, 105-116.
Slide 6
ROUGHNESS: HDM-4 MODEL
𝛿𝐸 = %𝐸0 IRI−𝐼𝑅𝐼0
• Input:
• Zaaber & Chatti (2010)
–
–
–
–
–
Measured IRI (t)
Reference IRI, 𝐼𝑅𝐼0
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.
Slide 7
MIT Model Gen II: Viscoelastic Top Layer
Consideration of Top-Layer Viscoelastic behavior, including
temperature shift factor:
P c
Relaxation Time
𝜏 = 𝜏0 𝑇0 × 𝑎 𝑇 (𝑇)
s
– Bituminous Materials*
−𝐶1 (𝑇 − 𝑇𝑟𝑒𝑓 )
𝐶2 + (𝑇 − 𝑇𝑟𝑒𝑓 )
𝑎 𝑇 = exp
Winkler Length 𝓁𝒔 =
4
s
k
– Cementitious Materials**:
𝑎 𝑇 = exp 𝑈𝑐
h = tE E
Temperature
dependence
1
1
−
𝑇 𝑇𝑟𝑒𝑓
𝐸𝐼/𝑘
𝒄𝒄𝒓 = 𝓁𝑠 (𝑘/𝑚)1/2
* Pouget et al. (2012); William, Landel, Ferry (1980)
** Bazant (1995)
Speed
Dependence
Slide 8
Calibration/Validation | Asphalt Lit. Data
1.4
c= 100 km/h
DISSIPATED ENERGY [MJ/km]
Calibration c=100 km/h
1.2
• Model-Based Simulations
1
0.8
Pouget et al. (2012)
0.6
MIT Model
0.4
𝜏(𝑇) = 𝜏0 𝑇0 × 𝑎 𝑇 (𝑇)
0.2
0
0
20
40
60
80
TEMPERATURE [Deg.C]
1.6
c= 50 km/h
Validation c=50 km/h
1.4
DISSIPATED ENERGY [MJ/km]
𝑐𝑐𝑟
𝑃2
𝑐
𝝉(𝑻)𝑐𝑐𝑟
𝛿𝐸 =
×
𝐹
;𝜁 =
𝑐
𝑐𝑐𝑟
𝓁𝑠
𝑏𝑘𝓁2𝑠
1.2
1
0.8
𝒄 = Vehicle speed
𝑃 = 32.4 ton truck (distribution of loads
according to HS 20-44)
𝑏 = 3.6 m (lane width)
𝐸, 𝑘, ℎ = 40,264 MPa, 35 MPa/m, 0.22 m
𝝉(𝑇0 = 283 K) = 0.015 s
Pouget et al. (2012)
0.6
MIT Model
0.4
0.2
0
0
20
40
TEMPERATURE [Deg.C]
60
80
Slide 9
New Feature: Temperature and Speed Dependence
DISSIPATED ENERGY [MJ/km]
0.35
0.3
0.25
0.2
0.15
68 Deg. F
Gen I
0.1
50 Deg. F
0.05
0
0
50
100
SPEED [km/h]
(Example taken from Pouget et al. (2012)
Slide 10
Can we do better? – Yes, we can!
Pavement Roughness
Pavement Deflection
2011
MIT-Model
PVI Impact
MEPDG
Structure and Material
Slide 11
LCA “plus”: MOVING LCA IN THE DESIGN SPACE
INPUT:
- Structure
- Materials
- Traffic
- Climate
- Design
Criteria
MEPDG
Structurally
Sound
Design
OUTPUT:
- E(t)
- IRI(t)
- Maintenance
- Traffic-evolution
OUTPUT:
- Comparative
Design
- Design
Alternatives
Sustainable
Design
LCA/LCCA
OUTPUT:
- Fuel Con.
- GHG
- Costs
Embodied + Use
Slide 12
Network Application
US and VA
Slide 13
FHWA/LTPP General Pavement Study sections (GPS)
Data:
Roughness
• IRI (Year)
• Traffic
• Location
• Pavement type
Deflection:
• Top layer modulus E
• Subgrade modulus k
• Top layer thickness h
• Other layer properties
AC
PCC
Com
GPS1: AC on Granular Base
GPS3: Jointed Plain CP (JPCP)
GPS6: AC Overlay of AC Pavement
GPS2: AC on Bound Base
GPS4: Jointed Reinforced CP (JRCP)
GPS7: AC Overlay of PCC
GPS5: Continuously Reinf. CP (CRCP)
GPS9: PCC Overlay of PCC
Slide 14
VA Interstate: Road Classification
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
3%
BIT
BOC 18%
BOJ
CRCP
JRCP
7%
7%
Pavement type analyzed
Type
65%
Asphalt (BIT)
Concrete (CRCP, JRCP)
Composite (BOC, BOJ)
Total
Lane-mile Center-mile
3,131
490
1,221
4,841
Slide 15
1,416
174
459
2,050
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)
Pavement Type
AC
Com
PCC
Slide 16
Annual Average Daily Truck Traffic (AADTT)
AADTT
Slide 17
Deflection -Induced PVI
Slide 18
Temperature and Speed Sensitivity: AC in VA
𝑑𝑤 𝑐𝑐𝑟 𝑃2
𝑐
𝝉(𝑻)𝑐𝑐𝑟
𝛿𝐸 = −𝑃
=
×
𝐹
;
𝜁
=
𝑑𝑋
𝑐 𝑏𝑘𝓁2𝑠
𝑐𝑐𝑟
𝓁𝑠
Asphalt Concrete (BIT)
Asphalt Concrete (BIT)
1.4
1.4
1.2
1.2
1
0.8
0.6
0.8
0.6
0.4
0.4
0.2
0.2
0
0.001
0.01
0.1
Dissipated Energy [MJ/km]
c=60 mph
c=20 mph
T=20C/65F
PDF/1
PDF/1
1
T=10C/50F
1
Temperature sensitivity
one order of magnitude higher dissipation
(T= 50 vs. 65 F)
𝑃 = 37 tons (3 axles); 𝑐 = 62.5 mph; τ0 = 0.015 s; VA
Interstate database for distributions of (𝐸, 𝑘, ℎ) of AC
0
0.001
0.01
0.1
Dissipated Energy [MJ/km]
1
Speed Sensitivity
half order of magnitude higher dissipation
(𝑐 = 20 vs. 60 mph)
𝑃 = 37 tons (3 axles); 𝑇 = 10℃/50℉;Slide
τ0 = 19
0.015 s
Temperature Sensitivity: PCC in VA
𝑑𝑤 𝑐𝑐𝑟 𝑃2
𝑐
𝝉(𝑻)𝑐𝑐𝑟
𝛿𝐸 = −𝑃
=
×
𝐹
;
𝜁
=
𝑑𝑋
𝑐 𝑏𝑘𝓁2𝑠
𝑐𝑐𝑟
𝓁𝑠
Concrete (JRCP, CRCP)
Concrete (JRCP, CRCP)
1.6
1.4
c=20 mph
T=10C/50F
2
c=60 mph
1
PDF/1
PDF/1
1.2
2.5
T=20C/65F
0.8
0.6
1.5
1
0.4
0.5
0.2
0
0.001
0.01
0.1
Dissipated Energy [MJ/km]
Temperature sensitivity
Small!
1
0
0.001
0.01
0.1
Dissipated Energy [MJ/km]
Speed Sensitivity
Small
[For pure comparison, assume same 𝜏0 (𝑇0 = 283 K) as for asphalt]
𝑃 = 37 tons (3 axles); 𝑐 = 62.5 mph; τ0 = 0.015 s; VA
interstate database for distributions of (𝐸, 𝑘, ℎ) of PCC
20s
𝑃 = 37 tons (3 axles); 𝑇 = 10℃/50℉; τ0Slide
= 0.015
1
Would this matter for VA?
Order of magnitude difference
BIT/AC
PCC
Temperature sensitivity
10 Deg. can entail one order of
magnitude of higher energy
dissipation; thus fuel
consumption.
Temperature sensitivity
10 Deg. can entail half 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
* Temp data from National Oceanic and Atmospheric Administration (esrl.noaa.gov)
Slide 21
VA Network: PVI Deflection – Truck
c= 100 km/h=62.6 mph; T= 16 C/61 F
1.6
Bituminous
Composite
Concrete
1.4
1.2
PDF/1
1
0.8
0.6
0.4
0.2
0
0.0001
0.001
0.01
Excess Fuel Consumption (gal/mile)
Excess fuel consumption due to PVI deflection is 10 times higher on bituminous pavements
Slide 22
Annual Excess Fuel Consumption: PVI Deflection
*2013 data
c= 100 km/h=62.6 mph; T= 16 C/61 F
FC (gallon/mile)
Slide 23
Summary | For Discussion
• 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…
Slide 24
IRI-Induced PVI
Slide 25
IRI: US Network – VA Data Comparison
0.6
Frequency
0.5
0.4
VA Network
0.3
US Network
0.2
0.1
0
<60
60-94
95-119
120-144
145-170
171-194
195-220
> 220
171-194
195-220
> 220
IRI (in/mile)
<60
60-94
95-119
120-144
145-170
1.2
1
0.8
VA Network
0.6
US Network
0.4
0.2
0
IRI distribution of Virginia and the US network are very similar.
Slide 26
VA – Roughness
*2013 data
0.7
Frequency
0.6
0.5
0.4
VA Concrete
0.3
VA Asphalt
0.2
VA Composite
0.1
0
<60
60-94
95-119
120-144 145-170 171-194 195-220
> 220
IRI (in/mile)
<60
60-94
95-119
120-144 145-170 171-194 195-220
> 220
1.2
1
0.8
Concrete
0.6
Asphalt
0.4
Composite
0.2
0
Asphalt and composite pavements are maintained equally. Not concrete
Slide 27
IRI depends on pavement maintenance
<60
60-94
95-119
120-144 145-170 171-194 195-220
> 220
1.2
1
0.8
Concrete
0.6
Asphalt
Composite
0.4
VA (2013)
0.2
0
<60
60-94
95-119 120-144 145-170 171-194 195-220
> 220
1.2
1
0.8
Concrete
Asphalt
Composite
0.6
0.4
0.2
MN (2011)
0
Slide 28
Pavement Roughness (IRI)
*2013 data
IRI (in/mile)
Slide 29
Excess Fuel Consumption: PVI Roughness
*2013 data
FC (gallon/mile)
Slide 30
Cost aggregated for:
- Interstate pavement
- Primary pavement
- Secondary pavement
Deficient pavement IRI:
- Poor: 140-199
- Very poor: >200
Pavement Expenditure (Millions of $)
Annual Expenditure on all Pavements in VA
$400
Asphalt Pavement
$350
Concrete Pavement
$300
$250
$200
$150
$100
$50
$0
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Year
Deficient lane miles due to ride quality by pavement type – Interstate
Pavement Type
AC
PCC
Total
lane-mile (% total)
3,131 (65%)
490 (10%)
3,621 (75%)
Deficient lane-miles (% total)*
157 (46%)
181 (54%)
338 (100%)
*VDOT. State of The Pavement 2012. http://www.virginiadot.org/info/resources/State_of_the_Pavement_2012.pdf
Slide 31
SUMMARY: IRI-induced PVI
• IRI is vehicle specific
• Concrete pavements are under-maintained
• Difference between pavement systems is IRIdevelopment and pavement aging. Data not
consistent with national analyses
• Model Development: 𝛿𝐸 = %𝐸0 IRI−𝐼𝑅𝐼0
Reference 𝐼𝑅𝐼0 = 63 in/mile = Political decision
Higher value of 𝐼𝑅𝐼0 reduces the number of roads
contributing to excess fuel consumption.
Slide 32
Total PVI Impact
Slide 33
Network: Annual PVI Truck* – excess FC per mile
Annual Excess Fuel Consumption (Gal/mile)
16000
Roughness
Deflection
160
14000
140
12000
120
10000
100
8000
80
6000
60
4000
40
2000
20
0
0
BIT
BOC
BOJ
CRCP
Annual Excess CO2e Emissions (tons/mile)
c= 100 km/h=62.6 mph; T= 16 C/61 F
*2013 data
JRCP
Impact Reduction through enhanced pavement design and management
Slide 34
Network: Annual PVI Truck – Total FC
c= 100 km/h=62.6 mph; T= 16 C/61 F
70,000
6,000,000
60,000
5,000,000
50,000
4,000,000
40,000
3,000,000
30,000
2,000,000
20,000
1,000,000
10,000
0
0
2007
2008
2009
2010
Annual Truck FC Roughness
2011
2012
2013
Annual Truck FC Deflection
Slide 35
Excess CO2e Emissions (tons)
Excess Fuel Consumption (Gallons)
7,000,000
PVI Total Impact: Roughness and Deflection
*2013 data: Trucks
c= 100 km/h=62.6 mph; T= 16 C/61 F
FC (gallon/mile)
Slide 36
CARBON MANAGEMENT = Pavement Performance!
ENGINEERING
100%
• 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!
Moving tire (top view) is on slope
= Deflection induced eXtra-Fuel Consumption
Slide 37
CARBON MANAGEMENT = Cost – Benefit!
ECONOMICS
100%
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
Slide 38
LC C A VA LU E P RO P O S I T I O N
• Context: $ 2 Trillion Infra-structure
renewal job within tightest budgetary
constraints.
• Problem: Volatility of construction
materials pricing for a fiscally sound
decision making.
ECONOMICS
Decision Makers
(local, national,
and beyond)
* Swei, Gregory & Kirchain (2013)
• Solution*: A new LCCA methodology
with probabilistic cost modeling of
pavement projects, so that decisionmakers:
– Understand the risk of an investment;
– Select a design based on risk
perspective.
I M P L E M E N TAT I O N @ State Level: Case Study
Slide 39
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
Uncertainty is pervasive in pavement LCCA
Cash Flow
Decisions
long before
construction
Uncertainty in unit
construction costs
Construction
CSHub approach
characterizes uncertainty
for all three areas
Uncertainty
& Risk
Long
life-cycle
Uncertainty in
material price
evolution
Ope ra t i on
Uncertainty in timing
of M&R activities
Slide 40
CSHub LCCA methodology is integrated with
pavement design process
Propagate uncertainty to
understand risk
Statistically Characterize
Uncertainty
Present
MEPDG
Output
Relative risk
Is the difference
significant?
Future
LCCA
Model
FHWA guidance is limited
Characterize drivers of
uncertainty
Slide 41
IMPLEMENTATION: LCCA – Why does it matter?
Translating price volatility into value proposition for Decision Makers
• ECONOMICS = LINGUA FRANCA
OF IMPLEMENTATION
100%
90%
Minimizing Risk
ECONOMICS
100%
Cumulative Probability
80%
70%
60%
50%
Gambling with
Cost overrun
40%
Design A
30%
Design B
20%
10%
0%
26.8
27.0
27.2
27.4
27.6
NPV (Millions of $'s)
27.8
Slide 42
What’s next?
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 !
Slide 43
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-vehicleinteractions; TRB.
•
http://web.mit.edu/cshub/
Slide 44
Predicting the future?
• Beyond my pay grade, but…
• CARBON MANAGEMENT is a vehicle of
INFRASTUCTURE MANAGEMENT
• Quantitative Sustainability
• Together, let’s make it a reality…
Slide 45
∆𝐈𝐑𝐈 ∆𝐭
:
𝐀𝐀𝐃𝐓𝐓
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 140-199
JRCP IRI
146 128
104
AC IRI
88
87
73
Pavement IRI is a function of pavement maintenance
Slide 46
Comparison: Gen 1 – Gen 2 Model
GPS-1: AC on Granular Base
0.5
0.45
0.5
T=10C/50F (+/- 10C)
c=100 km/h (62.5mph)
Gen-1
0.35
0.35
0.3
0.3
0.25
0.2
0.1
0.1
0.05
0.05
0.01
0.1
1
Gen-II
0.2
0.15
0.001
Gen-I
0.25
0.15
0
0.0001
T=10C/50F (+/- 10C)
c=100 km/h (62.5mph)
0.4
PDF/1
PDF/1
0.45
Gen-II
0.4
0
0.0001
10
𝑐 = Vehicle speed
𝑃 = 36 tons (on 3 axles)
𝑏 = 3.6 m (lane width)
𝐸, 𝑘, ℎ = (GPS 1, 2 - LTPP Network)
𝜏(𝑇0 = 283 K) = 0.015 s
𝑇 = Temperature
Gen 2 INPUT
DISSIPATED ENERGY [Ltr/100km]
Gen 1 INPUT
GPS-2: AC on Treated Base
0.001
0.01
0.1
1
DISSIPATED ENERGY [Ltr/100km]
10
That is, Gen I model is a lower bound.
Gen II is more accurate for local response,
but requires (at least) one more
parameter.
Slide 47
Viscoelastic Modeling | Master Curve
Temperature
−𝐶1 (𝑇 − 𝑇𝑟𝑒𝑓 )
𝑎 𝑇 = exp
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)
Load Frequency (Speed)
Slide 48
Principle of Viscoelastic Model Fitting
(Using Master Curve)
complicated
viscoelastic model
Simplified (Maxwell)
viscoelastic model
Fit for the entire
frequency range
Fit for applicable
frequency range
Find t and E
Frequency range
of interest
Simplified Maxwell model along with the WLF law
accounts for the temperature dependency.
Maxwell model
with temperature
dependency
Slide 49