Managing Infrastructure Network Performance for Increased Sustainability using Dynamic Life Cycle Assessment Models

(1)

Managing Infrastructure Network

Performance for Increased

Sustainability using Dynamic Life

Cycle Assessment Models

Michael D. Lepech

Stanford University

(2)

Research Motivation

• Maintenance and operation of infrastructure systems is a major

contributor to US greenhouse gas emissions.

– 21% of GHGs originate from roadway vehicles (WRI, 2007)

• US$64.4 Billion investment in United States annually

– Based on heuristic cost minimization models

– Influenced by workload balancing

• Significant reductions in GHG emissions by 2030 (IPCC

2008)

(3)

Objective

• Develop a life cycle model that captures dynamic

impacts of pavement and bridge maintenance and

deterioration.

• Construct optimization algorithms to maintain

and operate transportation segments at minimum

environmental impact (Energy consumption and

GHG emissions).

• Construct network optimization scenarios for

maintenance and operation of transportation

networks at minimum environmental impact

(Energy consumption and GHG emissions).

(4)

(5)

System Definition

Concrete overlay

HMA overlay

Overlay length is 10 km. Traffic flow is 70000/day (four lanes). Annual traffic

growth rate is 0% in the baseline model. Truck percentage is 8%.

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System Performance

• Based on material modeling and field performance

– S-N Curves

– Accelerated load testing (Turner-Fairbanks)

– Short term field data

0

10

20

30

40

0

10

20

30

40

50

60 Pavement age, year

D

ist

re

ss I

ndex

Concrete

ECC

HMA

0

10

20

30

40

0

10

20

30

40

50

60 Pavement age, year

D

ist

re

ss I

ndex

Concrete

ECC

HMA

(7)

Use Phase Modeling

• Roughness impacts and construction

congestion are explicitly considered

• Fuel Consumption

• Emission

– Driving behavior

– Speed change

– Lane capacity change

• IRI increases by 1 m/km, lane capacity decreases 150 passenger car units per

hour per lane.

– Engine load

• A constant emission rate (in grams of emissions per gallon of fuel burned) is

assumed.

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Life Cycle Cost Parameters

• Material cost

• User cost

• Cost of time loss

• Cost of fuel: gas $3.0/gal (€0.51/L), diesel $2.7/gal (€0.46/L)

• Environmental cost

Concrete

ECC

HMA

$79.06/m

3 _$294/m

3 _$40.55/tonne

Passenger Autos

$13.61/hour

Single Unit Trucks

$21.78/hour

Combination Trucks

$26.21/hour

Air Pollutant

$/tonne

Air Pollutant

$/tonne

CO

₂

$21

SO

_x

$88

N

₂

O

$7112

CO

$1

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Original Segment LCA Results

• Energy Consumption

• GHG Emissions

0

0.5

1

1.5

2

2.5 Concrete

ECC

HMA

Ener

gy C

onsum

pt

io

n,

10

6

GJ

End-of-Life

Distribution

Materials

Construction

Usage

Congestion

0

20

40

60

80 Concrete

ECC

HMA

CO

2

equi

val

ent

, 10

3

mt

End-of-Life

Distribution

Materials

Construction

Usage

Congestion

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Segment Optimization Results

HMA: 2% traffic growth model

DOT: 2012 minor maintenance, 2014 major maintenance and 2018 minor maintenance

Energy: 2008 minor maintenance, 2011 minor maintenance, 2017 minor maintenance, and 2020 minor

maintenance

0

0.5

1

1.5 Energy

CO2

PM2.5

NOx

Cost

Pb

VOC

SOx

Energy

Origin

Cost

0 10 20 30 40 50 60 2006 2011 2016 2021 2026 Years DI Origin Energy Cost

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Network Level Analysis

Energy Consumption 0 5 10 15 20 25

Concrete ECC HMA

10

5 GJ

Congestion Usage Construction Materials Distribution End-of-Life

Costs 0 2 4 6 8 10 10 7 D o lla rs

Agency Costs User Costs Environmental Costs

10 5 GJ End-of-Life Distribution Materials Construction Usage Congestion GHG Emissions 0 2 4 6 8

10 4 CO 2 eq u iva len t Mt Costs 0 2 4 6 8 10

10 7 Do ll a rs Energy Consumption 0 5 10 15 20 25

10

5 GJ

Congestion Usage Construction Materials Distribution End-of-Life

Costs 0 2 4 6 8 10 10 7 D o lla rs

Agency Costs User Costs Environmental Costs

10 5 GJ End-of-Life Distribution Materials Construction Usage Congestion GHG Emissions 0 2 4 6 8

10 4 CO 2 eq u iva len t Mt Costs 0 2 4 6 8 10

10 7 Do ll a rs 0 10 20 30 40 50 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 Years DI 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 Construction Reconstruction Major Minor Minor 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 Construction Reconstruction Major Minor Minor

Integrated LCA-LCCA

model evaluates energy

consumption, GHG

emissions, and costs of

pavement systems.

LCO model

optimizes

LCA-LCCA

results.

GIS model provides the

input data for LCA-LCCA

model and LCO model

and visualizes their

results.

LCA-

LCCA

Model

LCO

Model

GIS

Model

Integrated

LCA-LCCA

Model

Project-level LCO Model

Priority

Selection

Model

GIS Model

Satisfied All

Budget

Constraint?

Integrated

LCA-LCCA

Model

Yes

No

Initialize Model

GIS Model

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Development of GIS Network Model

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Network Analysis Results

• Where – Control Section Indexed

• When – Year of Maintenance

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System Level Improvements

Average DI Curves

0.00

5.00

10.00

15.00

20.00

25.00

0

10

20

30

40 D

is

tr

ess

I

n

d

ex

Project Level

MDOT

Benefit

Benefit/Cost

Linear Programming

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Network Optimization Benefits

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Scenario Analysis

• Current slack in the system budget allocation

3.60E+10

3.80E+10

4.00E+10

4.20E+10

4.40E+10

0.7M

0.6M

0.5M

0.4M

0.3M

0.2M

Budget Constraint, $

E

n

e

rgy

c

o

ns

um

pt

ion,

M

J

Benefit

Benefit/Cost

BIP

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Ongoing Research

• Effects of various transportation seismic retrofits and

reconstruction

– Repair impacts (construction materials and processes)

– Secondary impacts (traffic congestion)

– Cost/Benefit of Network Hardening

• Where to do it?

• When to do it?

• What technology to use?

• Economic, Environmental, & Social Cost Accounting

• Integrating with sensing network for dynamic

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Conclusions

• Proper management and operations can result in

significant environmental impact reductions.

(Copenhagen 2009-2010)

• Network level pavement asset management system can

maintain a pavement network and allocate budget

resources efficiently.

• Network level pavement asset management system

allow decision makers to have more control in the

policies being generated by life cycle model.

• GIS can be an very important element in a pavement

management system by facilitating the preparation,