Mehdi Akbarian (MIT)

Introduction Pavement-Vehicle Interaction

Model Results: LTPP Network Analysis

Conclusion and Future Work

O u t l i n e

Why Bother: Transportation Accounts for 20% of US CO2 Emissions

LCA & LCCA Boundaries

Pavement Vehicle Interaction: 72%

Introduction Pavement-Vehicle Interaction

Model Results: LTPP Network Analysis

Conclusion and Future Work

O u t l i n e

Pavement-Vehicle Interaction

Pavement Deflection

Structure and Material

Pavement Roughness*

* Zabaar and Chatti (2010) Calibration of HDM-4 Models for Estimating the Effect of Pavement Roughness on Fuel Consumption for U.S. Conditions

Literature: Empirical Studies on Pavement Deflection

Empirical Database:

- High uncertainty

- High variability

- Question of objectivity

- Binary material view:

- Asphalt vs. concrete

- No structural consideration

Need:

- Model is missing to relate fuel consumption to:

- Deflection

- Structure

- Material

Introduction Pavement-Vehicle Interaction

Model Results: LTPP Network Analysis

Conclusion and Future Work

O u t l i n e

PVI Deflection Model

Research Problem:

• Evaluate, in first order, the mechanics behind pavement vehicle interaction

Research Goal:

• Create a model that relates fuel consumption to:

• Deflection

• Structure

• Material properties

Simplest model:

• Bernoulli Euler beam on viscoelastic foundation

• Calibrate model parameters

• Validate with experimental data

Bernoulli Euler beam on viscoelastic foundation

𝐸𝐼𝜕4𝑦

𝜕𝑥4+𝑚𝜕2𝑦

𝜕𝑡2+ 𝑐𝜕𝑦

𝜕𝑡+ 𝑘𝑦 = 𝑞(𝑥, 𝑡)

𝐸𝐼𝜕4𝑦

𝜕η4+𝑚𝜕2𝑦

𝜕𝑡2− 2𝑉𝜕2𝑦

𝜕𝑡𝜕η+ 𝑉2𝜕2𝑦

𝜕η2+ 𝑐(𝜕𝑦

𝜕𝑡− 𝑉𝜕𝑦

𝜕η) + 𝑘𝑦 = 𝑞(η, 𝑡)

Moving coordinate system: 𝜂 = 𝑥 − 𝑉𝑡

𝛏 and 𝛀∗ are tranformed fields of 𝛈 and 𝐭

𝑦 η =1

2𝜋

𝑄(ξ)

𝐸𝐼ξ4 −𝑚𝑉2ξ2 + 𝑘(1 + 2𝑖ζ)𝑒𝑖ξη𝑑ξ

∞

−∞

𝑄 ξ = 𝑞 𝑒−𝑖ξη𝑑η𝑎/2

−𝑎/2

Input: 𝐄: Elastic Modulus of Top Layer 𝐡: Thickness of Top Layer 𝐤: Elastic Modulus of Subgrade 𝛇: Damping Ratio 𝐦:Mass of beam per unit length

[𝑒𝑞. 1]

[𝑒𝑞. 2]

[𝑒𝑞. 3]

[𝑒𝑞. 4]

[𝑒𝑞. 5]

(* in the case of periodic load, not considered in what follows) Output 𝒚 𝜼 : Deflection

Model Parameter Study

Inputs:

• E: Top layer modulus

• h: Top layer thickness

• k: Substrate modulus

• M: Vehicle mass

𝐼𝐹𝐶~𝑀2 × 𝐸−12 𝑘−12 ℎ−32

Ls

w

• 𝐼𝐹𝐶~ 𝐺𝑅 × 𝑀 × g : Gradient Force

• 𝐺𝑅~𝑤

𝐿𝑠

• 𝑤 ~ 𝑀1 𝐸−14 𝑘−

34 ℎ−

34

• 𝐿𝑠 ~ 𝐸14 𝑘−

14 ℎ34

* Mechanistic Approach to Pavement-Vehicle

Interaction and Its Impact in LCA - Journal of the

Transportation Research Board, 2012.

GR

Calibration – validation: FHWA DATA

Validation Calibration

FWD Time Histories:

1. Calibration: Arrival time of signal

2. Validation: Maximum deflection at offsets

Role of Damping

Δ

Effect of damping:

1- Distance lag Δ due to increase in

damping.

2- Decrease in maximum deflection.

3- But, second-order effect.

Effect of distance lag Δ:

- Maximum deflection behind the

load

- Wheels are on a constant slope

Introduction Pavement-Vehicle Interaction

Model Results: LTPP Network Analysis

Conclusion and Future Work

O u t l i n e

LTPP Monitored Sections

Asphalt Concrete

Data used:

• Top layer modulus E

• Subgrade modulus k

• Top layer thickness h

• Loading condition q

• Traffic Volume (AADT, AADTT)

Total of 5643 sections: 1079 rigid, 4564 flexible

Monte-Carlo Procedure

PVI Deflection Fuel Consumption

Sample Data (log space)

Calculate Fuel Consumption

Check convergence

𝜇, 𝜎 ≈ (𝜇, 𝜎)

inputs samples

q (μ,σ)

E (μ,σ) k (μ,σ)

h (μ,σ)

Deflection Induced Fuel Consumption

Trucks:

Cars:

Report: *Akbarian M., Ulm J-F. 2012. Model Based Pavement-Vehicle Interaction Simulation for Life Cycle Assessment of Pavements.

Concrete Sustainability Hub. MIT

Use in a LCA

50 yr GHG Emissions of Two Pavement Scenarios Relative to a “Flat” Pavement

𝑇𝑜𝑡𝑎𝑙 𝐼𝐹𝐶~𝐸−12 𝑘−12 ℎ−32 (𝑁𝑖 𝑊𝑖

2)

𝑖

0

200

400

600

800

1000

1200

1400

1600

1800

2000

Concrete Asphalt

GH

G E

mis

sio

ns

(Mg

CO

2e

)

PVI Deflection 95%

Production + M&R

Maintenance and Rehabilitation Schedules: Asphalt Year 17: Mill and Replace Year 28: Mill and Replace Year 38: Mill and Replace Year 47: Mill and Replace Concrete Year 20: Diamond Grind Year 40: AC Overlay

*Embodied GWP for Canadian High Volume Traffic Scenario : Athena (2006)

Introduction Pavement-Vehicle Interaction

Model Results: LTPP Network Analysis Conclusion and Future Work

O u t l i n e

Conclusion

Developed:

• Relationship between material and structural pavement properties with PVI

• Calibration – Validation of model

• Model provides realistic estimates of FC for vehicles and current trends

Future Work:

• More accurate pavement model

• Realistic vehicle model

• Network application

Use in a LCA – with roughness

Embodied GWP for Canadian High Volume Traffic Scenario : Athena (2006)

IRI design criterion = 160 in/mile

0

1000

2000

3000

4000

5000

6000

Concrete Asphalt

GH

G E

mis

sio

ns

(Mg

CO

2e

)

PVI Roughness 95%PVI Deflection 95%Production + M&R

Maintenance and Rehabilitation Schedules: Asphalt Year 17: Mill and Replace Year 28: Mill and Replace Year 38: Mill and Replace Year 47: Mill and Replace Concrete Year 20: Diamond Grind Year 40: AC Overlay