doing more with less: economically-efficient management of ...doing more with less:...

Doing more with less:

economically-efficient management

of pavement networks

Jeremy Gregory

ACPA Mid-Year Meeting

June 13, 2017

The US is not sufficiently investing in its ailing road system

21% of the US

highways are in

poor condition

Governments are being forced to do more with less

Governments Look for New Ways to

Pay for Roads and BridgesFeb 14, 2013

Life cycle cost analysis is a key element of addressing

infrastructure funding gap

Reduce life cycle costs by 50% by 2025*

Areas of focus

• Resilience

• Innovation

• Life cycle costs

• Performance standards

*$4.6 trillion needed in infrastructure investment by 2025

$2 trillion is unfunded

Asset management allocation tools are critical to

economically-efficient infrastructure

Use asset management best practices to prioritize projects

and improve the condition, security, and safety of assets

while minimizing costs over its entire life span.

Tools for economically-efficient management of pavement

networks

Competitive Paving Prices

Life Cycle Cost Analysis

Asset Management

Statistical analysesof Oman BidTabs* data using these parameters:

• Quantity

• Annual spending

• Number of bidders

• Share of AC/PCC bids

• Use of AC & PCC on job

• Share of spending on AC vs. PCC

5-Year Average Percent Spending on AC

Proxy for inter-

industry competition

Does the presence of competition between material

substitutes impact pavement material prices?

*2005-2015, 47 states, 298k pay items, 164k jobs

Viable competition is an opportunity

WHAT MATTERS? PRICE SIGNIFICANCE

Volume of concrete on job

5-year rolling average:

Spending on bids per state-year


Share of spending on concrete

Mass of asphalt also on job

Concrete percent of bid

Count of bidders on jobs

Statistical analysis results for concrete initial prices

$$$

$$$

$$

$-$$

$

$

Viable competition is an opportunity

WHAT MATTERS? PRICE SIGNIFICANCE


Spending on bids per state-year


Mass of asphalt per state-year

Count of bidders on jobs

Mass of asphalt on job

Share of spending on concrete

per state-year

Asphalt percent of bid

Instability of Share of spending

on concrete per state-year

Volume of concrete also on job

Statistical analysis results for asphalt initial prices

$$$

$$$

$$

$$

$$-$$$$$-$$$

$$-$$$

$$


networks



Asset Management

LCCA – Life-cycle cost analysis:

Method for evaluating total costs of ownership

A

A A A

B

B B B

0

2

4

6

8

10

12

Real C

ost

A

B

0

2

4

6

8

10

12

Lif

e-C

ycle

Co

st

Transform individual pavement

expenditures over time into…

…total life-cycle

cost

Present Value

Pavement design is iterative, but tools are not:

Accelerated feedback more testing, more improvement

10.0” JPCP

w/ 1.25” Dia Dowels

Subgrade

6.0” Agg Subbse

Design

Proposal &

Context

Layers

Traffic

Climate

Analyze Using ME

Design Principles

N Adequate

Performance

8.0” JPCP


Subgrade

6.0” Agg Subbse

Final

Design

Develop Lifecycle

Bill of Activities

Evaluate

LCCA / LCA

Y

Pavement design is iterative, but tools are not:

Accelerated feedback more testing, more improvement

10.0” JPCP


Subgrade

6.0” Agg Subbse

Design

Proposal &

Context

Layers

Traffic

Climate

Analyze Using ME

Design Principles

Develop Lifecycle

Bill of Activities

Evaluate

LCCA / LCA

N YAdequate

Performance

MIT research

aims to

integrate these

activities

8.0” JPCP


Subgrade

6.0” Agg Subbse

Final

Design

CSHub created linkage between design tools and evaluation

Life cycle inventory

• Material & energy inflows

• Emissions outflows

Bill – of –activities

• Material quantities

• Construction activities

• Maintenance timing

• Logistics

Cash flows

• Magnitude

• Timing

7.0” JPCP


Subgrade

6.0” Agg Subbse

Pavement

Design

&

Context

MEPDG

LCCA

Model

LCA

Model

Pavement

Performance

Performance-

to-activity

modeling

Pavement has associated activities;…activities translate to cost & impact

CSHub created probabilistic cost estimates for entire life-cycle

Construction Operation

Cash

Flo

w

Agency:• Unit-price of inputs• Quantity of inputs Agency:

• Quantity of inputs• Future construction prices• Maintenance timingUser:• Traffic delays & fuel loss

CSHub conducted LCCAs for a wide range of scenarios

4 Locations

FL: Wet

no freeze

MO: Wet

freeze

CO: Dry

freeze

AZ: Dry

no freeze

3 Traffic Levels

• Rural local

street/highway

• Rural state highway

• Urban interstate

Several framing

conditions

• Pavement designs

• Maintenance

schedules

• Design life

• Analysis period

Life cycle matters

Future costs can be significant

Initial construction

costs47%

Future maintenance

and rehabilitation

costs53%

Total life-cycle costs for a state highway in Florida

Flexible pavement design developed by Applied Research Associates (ARA), Inc,:

AADTT 1k/day; 4 lanes; Wet-no-freeze-FL; FDOT-based rehabilitation schedule;

Analysis period = 50 years.

Context matters

Costs vary with location, traffic level, & pavement design

Initial costs98%

Rehab costs2%

Interstate, rigid design

Initial costs79%

Rehab costs21%

Interstate, flexible design

Initial costs47%

Rehab costs53%

State highway, flexible design

Initial costs89%

Rehab costs11%

Local highway, rigid design

Life cycle perspective alters relative competitiveness

0

0.5

1

1.5

2

2.5

Init

ial

Co

st

A

B

0

0.5

1

1.5

2

2.5

3

3.5

Re

al

Pri

ce

LC

C

A B

(millions $) (millions $)

0%

25%

50%

75%

100%

2.00 2.50 3.00 3.50 4.00

Fre

qu

en

cy

Net Present Value of LCC (million $)

Probabilistic analysis provides insight on relative risks

Probabilistic Life-Cycle Cost Including Price Projections

B A

Initial cost is

usually major

driver of variation

in comparative

pavement

LCCAs


networks



Asset Management

FHWA has issued new performance management rules due to

MAP-21

FHWA motivation for

performance management:

• Provide the most efficient investment of Federal

transportation funds

• Refocus on national transportation goals

• Increase accountability and transparency

• Improve decision-making through performance-

based planning and programming

Pavement network performance management process

Set targets Measure performance

Implement strategies to meet targets

% Good

% Poor

Pavement

Management

System

Which strategies?

At what cost?

How to allocate funds to obtain best performance at lowest cost?

Many approaches to allocate funds

Pavement Segment Pavement Condition

Index

A 10

B 12

C 15

D 18

E 20

F 23

G 27

• How to prioritize which

segments to repair?

• Will targets be met?

• Which strategies should

be used?

• Many short term fixes?

• Few long-term fixes?

An optimization modeling approach is required to answer these questions:

Performance-Based Planning

Goal of asset management research: improve allocation decisions

Current & historical pavement

network data

Projection of network

performance

Allocation of resources

Key elements of pavement management systems for

performance-based planning

Objective: prioritize projects that maximize performance and minimize cost

• Analysis period

• Available maintenance,

rehabilitation, and

reconstruction activities

• Uncertainty in prices

and deterioration

• Optimization algorithm

• Optimization metrics

• Network scale

• Guide project decisionsKey Considerations

MIT probabilistic network allocation model

Allocation

Optimization

Tool

Current state of

system

Projection models

of future

conditions

A set of

objectives/goals

Available MRR

actions

Objective: find allocation policy

that maximizes expected

performance or minimizes cost

Project Resource

Allocation

Computationally

efficient algorithm

Key elements of the allocation model

Conventional models MIT model

Short analysis periods

(~5 yr)

Long analysis period

(30+ yr)

Prescriptive decision

rules for limited MRR

alternatives

Dynamic decision rules

for diverse set of MRR

alternatives

Deterministic data &

analysis

Risk analysis including

uncertainty in prices &

deterioration

Select segments and strategies based on optimal benefits and costs

Example with VA interstate network

Low expenditures per unit area

Medium expenditures per unit area

High expenditures per unit area

Expected roadway

expenditures over 50 year

analysis period

Budget=$67m/yr

Objective: minimize traffic-

weighted roughness

Allocation strategy diversifies after 50 years

Current Composition of System Expected Composition in Year 50

AC

PCC

Fixed budget ($67m)

AC

PCC

Diversity of MRR alternatives is a means to enhance network

performance

0

0.25

0.5

0.75

1

70 80 90 100 110

Cu

mu

lati

ve P

rob

abili

ty

Average Traffic-Weighted IRI (in./mi.) over 50-year analysis period

Diversified

Network

Concrete

onlyAsphalt only

($67m budget for scenarios)

Single material network performs

worse than a diversified network

We are testing our approach with DOT partners

Thank you

http://cshub.mit.edu/

[email protected]

http://cshub.mit.edu/

mailto:[email protected]

LIFE-CYCLE COST:

Details on the CSHub Approach

Design A Design B

Net

Pre

sen

t V

alu

e

Generally, we think of computing and comparing

the Net Present Value (NPV) of A and B.

Goal of Life-cycle Costing:

Identify the smarter investment choice

But, in real life, can we ever know those

values for sure?...

Conventional NPV analysis ignores real-world variation:

Example of variation in initial cost

0%

10%

20%

30%

20 60 100 140 180 220 260 300 340 380 420 460 500 540 580

Event

Occurr

ence a

s a P

erc

enta

ge

Unit Price of Concrete ($/CY)

Distribution of Unit Price of Concrete for Pavement Projects

Source: Caltrans

12 months of project bid

data from the CalTrans

database

Conventional NPV analysis ignores real-world variation:

Examples of variation in future cost

0

100

200

300

400

500

600

1976 1981 1986 1991 1996 2001 2006 2011

Norm

alized P

rice Index

Time

Price Index of Concrete and Asphalt Over Time

Concrete

Asphalt

Material prices have

increased at different rates

over time

0

40

80

120

160

1975 1980 1985 1990 1995 2000 2005 2010 2015

Norm

alized R

eal Pri

ce Index

Asphalt

Ave. annual price

change = 1.3%

Concrete

Ave. annual price

change = -0.2%

Lumber

Ave. annual price

change = -0.8%

The future is worth considering

Prices change differently for different materials

The future is worth considering

Effective price projections are plausible

Concrete (grey)

Asphalt (black)

50

100

150

200

2010 2020 2030 2040 2050

Re

al P

ric

e In

de

x (

Ye

ar

20

13

= 1

00

)

Year

CSHub forecasts have been shown to be more

effective than current assumptions

0%

20%

40%

60%

0 5 10 15 20

Av

era

ge E

rro

r o

f F

ore

cast

Years into the future

CSHub Forecasting Model

Current Practice

Incre

asin

g P

erfo

rman

ce

Testing the effectiveness of the model for the

state of Colorado

Real price projections outperform conventional

assumptions of no real price change

Design A Design B

Net

Pre

sen

t V

alu

e

…in reality, the input values, and therefore output values, are not

certain

More Complete Question:

What is the relative economic risk among the alternatives?

Need to understand relative risk and

significance of difference.

Important to Develop Probabilistic Input Values to Push

Probabilistic LCCA into Practice

• FHWA (1998) published a

technical report promoting

the use of probabilistic

LCCA in practice.

Provides a high-level

overview of the process

• Little guidance on the

selection of appropriate

probabilistic input values

for conducting such an

LCCA

Objectives of MIT CSHub LCCA Project

Overall Objectives:

• Quantify appropriate probabilistic input

values for conducting an LCCA

• Incorporate uncertainty to determine the

risk associated with different pavement

alternatives

• Comparative assessment of alternative

pavement designs

Ultimate Vision: Create a decision-making tool,

allowing a pavement engineer to select a design

based on their risk perspective

Materials &

ConstructionUse Maintenance EOL

User Agency

Importance of life-cycle cost planning and considering uncertainty

Construction Operation

CostMaterials End-of-LifeConstruction

Use

Maintenance

Long l i fe-CycleDecisions long before construction

e.g. Uncertainty in initial construction costs e.g. Uncertainty in

pavement deteriorationUncertainty

& Risk

Initial research goal:

Characterize uncertainty, develop model, apply to scenarios

Statistically Characterize Uncertainty

Propagate uncertainty to understand risk

LCCA

Model

Pavement

Design

ToolPresent

Future

Is the difference

significant?

Relative risk

Characterize drivers of

uncertainty

Output of Model:

Difference of pavement alternatives at different risk profiles

0%

20%

40%

60%

80%

100%

1.5 2.0 2.5 3.0 3.5

Cum

ula

tive P

robabilit

y

Net Present Value

Design BDesign A

Mean difference

between the designs

90 th

percentile difference

between the designs

𝐴𝑠𝑝ℎ𝑎𝑙𝑡 − 𝐶𝑜𝑛𝑐𝑟𝑒𝑡𝑒

𝐶𝑜𝑛𝑐𝑟𝑒𝑡𝑒

Therefore, if asphalt is

more expensive, α90 and

Δμ are positive

Δμ

a90

50% Confidence

Output of Model: Frequency a given design costs less

Denoted with the term 𝛽

Frequency𝐴𝑠𝑝ℎ𝑎𝑙𝑡 −𝐶𝑜𝑛𝑐𝑟𝑒𝑡𝑒

𝐶𝑜𝑛𝑐𝑟𝑒𝑡𝑒> 0

Therefore, if asphalt is

always more expensive, b = 1,

and if concrete is always more

expensive, b = 0

0%

4%

8%

12%

16%

20%

-0.2 0 0.2 0.4 0.6 0.8 1

NPV Design B – NPV Design ANPV Design A

JPCP less costHMA less cost

b > 0.9 – JPCP alternative has

statistically significant lower

cost

b < 0.1 – HMA alternative has

statistically significant lower

cost

Output of Model: What are the key parameters driving

variation in the final cost?

0%

5%

10%

15%

20%

Factor 1 Factor 2 Factor 3 …

Contr

ibuti

on t

o V

ari

ance

Case Study: Pavement Selection in Southern CA

• Medium volume roadway

• Expected AADT of 23,400

• Expected AADTT of 1,400

• Located in Southern CA

• Designs, maintenance

schedule and analysis

assumptions from CalTrans

LCCA standards

HMAJPCP

Subgrade

8.4” Aggregate Sub

base

10.6” Aggregate

Base

6.6” AC

Subgrade

7.2” Aggregate Sub

base

4.8” Lean Concrete

Base

9.6” JPCP

Case Study: Three types of analyses conducted

Three types of analyses are conducted:

1) Initial Costs (no uncertainty)

2)Real price adjusted life cycle cost (no uncertainty)

3)Full probabilistic life-cycle cost (with uncertainty)

The purpose is to understand:

a) The impact of scope of analysis

b) The impact of risk on the decision

Broadening Scope Alters Relative Competitiveness

0

0.5

1

1.5

2

2.5

Init

ial

Co

st

HMA

JPCP

0

0.5

1

1.5

2

2.5

3

3.5

Re

al

Pri

ce

LC

C

HMA JPCP

(millions $) (millions $)

0%

25%

50%

75%

100%

2.00 2.50 3.00 3.50 4.00

Fre

qu

en

cy

Net Present Value of LCC (million $)

Probabilistic Analysis Provides Insight on Relative Risks

Probabilistic Life-Cycle Cost Including Price Projections

JPCPHMA

Contexts analyzed as a part of case studies*

Traffic

Initial Design

Life

Analysis Period

Location

Maintenance

Schedule

20-50 years

50-100 years

30 – 8,000 AADTT

MEPDG and DOT

Discount Rate

1-7%

*Functionally-equivalent pavement designs developed

independently by Applied Research Associates

General Results – 30 year design life and 50 year analysis period

Δµ a90 β Δµ a90 β Δµ a90 β Δµ a90 β

Initial -40% -37% 0%

LCC –

MEPDG-32% -33% 0%

LCC – DOT -20% -20% 1%

Initial -22% 24% 3% -33% -35% 8% 16% 19% 71% -41% -64% 22%

LCC –

MEPDG-12% -14% 17% -14% -20% 29% 27% 29% 82% -29% -49% 33%

LCC – DOT -4% -3% 48% 9% 6% 69% 50% 54% 95% 11% -18% 68%

Initial 15% 29% 76% -1% -7% 49% 18% 22% 69% 16% -19% 62%

LCC –

MEPDG22% 30% 86% 15% 10% 70% 23% 36% 75% 23% -6% 71%

LCC – DOT 30% 36% 95% 34% 24% 91% 42% 42% 93% 63% 21% 90%

MO AZ CO FL

Local

Traffic

Highway

Traffic

Interstate

Traffic

Question: How many case studies

have alternative pavements with

statistically significant differences?

b > 0.9 – JPCP alternative has statistically significant lower cost

b < 0.1 – HMA alternative has statistically significant lower cost

Larger conclusions from all case studies

- Traffic Volume - Roadways designed for higher traffic volumes tended to favor the JPCP

alternatives

- Location- Using state-specific cost data plays an important role in a probabilistic

analysis.

- Design Life- Having an increased design life favored the JPCP alternatives, albeit

marginally

- Analysis Period- After 50 years, analysis period had little impact

- Drivers of Variation- Unit-cost was the major contributor across cases

Initial cost tended to be the major driver of variation in

comparative pavement LCCAs

JPCP Layer Unit-Price 84

Aggregate Unit-Price -

AC Surface Unit-Price 2

AC BinderUnit-Price 5

JPCP Layer Unit-Price 74 67 29 92

Aggregate Unit-Price - - - -

AC Surface Unit-Price 5 9 13 1

AC BinderUnit-Price 7 17 23 3

JPCP Layer Unit-Price 19 56 32 85

Aggregate Unit-Price 13 1 1 -

AC Surface Unit-Price 20 4 3 4

AC BinderUnit-Price 31 4 11 2

AC Base Unit-Price 2 23 55 4

Local

Traffic

Highway

Traffic

Interstate

Traffic

MO AZ CO FL

Parameter Percent Contribution to Variance in Case Studies

Thoughts on implementing LCCA

• Key needs

– Established methodology

– Data

– Buy-in from stakeholders and support leaders

• Developing method

– Learn from others, but carry out stakeholder input process

• Invest in data collection

• Support those charged with the task

Initial cost variation questions

• Can we reduce variation?

– Incorporate new factors

• Quantity: Weight, volume, area, thickness

• Location: variability within a state

• Competition: Bids, AD/AB

– Evaluate alternative methodologies

• Weighted Least Square

• Regression trees

Variation is larger for small projects!

Common methods assume it’s constant

4

5

6

7

0 4 8 12

Lo

g U

nit

-Pri

ce (

$/C

Y)

Log Quantity (Cubic Yards)

One Standard

Deviation ~

70% of data

points are

within this

band

71% actually

within the

band

47% 63% 92%

Clearly overestimating variation for larger

projects (where LCCA is typically implemented)

Techniques used in order to better quantify variation by the

CSHub

How can we reduce

variation?

Methodological

Weighted Least Squares

Regression Trees

Consider other explanatory

variables

Quantity Location Competition

Requires strong

background in

statistics, difficult

to understand

Requires little

background in

statistics, easy to

estimate

- Weight

- Volume

- Area

- Thickness

In-state

variability

- Bids

- AD/AB

0

2

4

6

8

0 4 8 12

Log U

nit-P

rice (

$/C

Y)


Illustrative Example: Missouri Concrete Price Prediction

Fit Statistics:

- R2 = 0.65

- Root Mean Square Error = 0.24

High Variation

Low Volume

Low Variation

High VolumeMedium Variation

Medium Volume

Is there a trend

within this sub-

population?

Missouri Concrete Example: Develop a multi-variate

regression model

Unit-Cost vs. Volume Consider other parameters:

- Bids*

- AD/AB*

- In-state variability*

- Thickness* = significant

-.5

0.5

1

Diff

ere

nce

Betw

ee

n A

ctua

l an

d P

red

icte

d

0 3 6 9 12

Log Quantity

Variation still higher for small projects

0

2

4

6

8

0 4 8 12

Log U

nit-P

rice (

$/C

Y)


Regression Trees: Divide the data into partitions

Quantity

Location

Competition

Inputs

All Data

Small

Volume

Other

Volumes

Medium

Volume

Large

Volume

Other

Locations

In-state

location A

Split all data into two regions achieving best-fit choosing:

a) input variable

b) split point

... and repeat

- Weight

- Volume

- Area

- Thickness

In-state

variability

- Bids

- AD/AB

Regression Tree: Missouri Concrete

All Data

Log (Q) <4.8 Log (Q) >4.8

Log (Q) <6.8 Log (Q) >6.8

Log (Q) >8.4Log (Q) <8.4

Small

Medium-Small

Medium - Large Large

Quantity

Location

Competition

Inputs

- Weight

- Volume

- Area

- Thickness

In-state

variability

- Bids

- AD/AB

0

2

4

6

8

0 4 8 12

Log U

nit-P

rice (

$/C

Y)


Regression Tree for Missouri Concrete

Small Medium-Small Medium-Large Large

Group Significant Inputs RMSE ΔRMSE

Small None 0.36 +50%

Medium-Small Location 0.19 -21%

Medium-Large Location, Bids 0.11 -56%

Large None 0.12 -51%

Initial Fit Statistics:

- R2 = 0.65

- RMSE = 0.24Variation reduction

between 20%-50%

Takeaways from Missouri concrete analysis

• Averages prices and variation increase for smaller

projects

• Incorporating other factors using typical regression still

overestimates variation for large projects

• Partitioning the data is an effective way to account for this

– Leads to evenly distributed variation for each group

– Smaller variation for large projects (the types which use LCCA)

How can we reduce

variation?

Methodological

Weighted Least Squares

Regression Trees

Consider other explanatory

variables

Quantity Location Competition

Broadening analysis for many bid items

- Missouri

- Colorado

- Louisiana

- Indiana

- Kentucky

Evaluate for 5 states for different bid items

Broadening analysis for many bid items

How can we reduce variation?

MethodologicalConsider other

explanatory variables

State Material Effective?

Missouri Concrete

Asphalt

Colorado Concrete

Asphalt

Indiana Concrete

Asphalt

Kentucky Concrete

Asphalt

Louisiana Concrete

Asphalt N/A

Quantity Location Comp.

-

-

/-

-

-

/-

N/A N/A N/A

Not significant

Sometimes

Significant

Significant

Concrete: 3/5

instances it is

significant

Asphalt: 2/4

instances where it

matters across any

bid items in a state

Notes:

Quantity: Volume tends

to be most useful

Concrete vs. Asphalt:

Concrete is only one bid

item in each state, asphalt

can be many

Initial Cost Modeling Takeaways

• Initial cost variation is higher for small projects

• Regression trees can be used to optimally segment the

data

– Leads to significantly less uncertainty for large projects

• Quantity and location are always significant inputs

• Competition (bidding, primarily) only matters for some

inputs

Asset Management

Pavement deterioration modeling: IRI

0

20

40

60

80

100

120

140

160

180

200

0 200 400 600

Pavement Age / months

IRI (in/m

ile)

Ma

inte

nan

ce

Δt

ΔIRI

Final IRI: IRI(t1)

Initial IRI: IRI(t0)

PVI roughness components are

functions of time:

• IRI (time dependent)

• AADT, AADTT (time dependent)

• M&R history

Models:

• Simplest: function of time:∆𝐼𝑅𝐼

∆𝑡=𝐼𝑅𝐼 𝑡1 − 𝐼𝑅𝐼(𝑡0)

𝑡1− 𝑡0

• Function of time and traffic:∆𝐼𝑅𝐼

∆𝑡. 𝐴𝐴𝐷𝑇𝑇=𝐼𝑅𝐼 𝑡1 − 𝐼𝑅𝐼(𝑡0)

𝐴𝐴𝐷𝑇𝑇. (𝑡1− 𝑡0)

• Current model: function of time,

traffic, thickness, age:

∆𝐼𝑅𝐼 = 𝐶1 × 𝐴𝐴𝐷𝑇𝑇𝐶2 ×𝑇ℎ𝑖𝑐𝑘𝑛𝑒𝑠𝑠−1 × 𝐴𝑔𝑒

*Zabaar and Chatti (2010)

Deterioration in FHWA LTPP General 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

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

Deterioration in FHWA LTPP General Study Sections (GPS)

Findings:

• Order of magnitude difference in performance

• Opportunity to slow deterioration for all pavement types

• Evaluate deterioration with Iowa

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

MIT probabilistic network allocation model

Allocation

Optimization

Tool

Current state of

system

Projection models

of future

conditions

A set of

objectives/goals

Available MRR

actions

Objective: find allocation policy

that maximizes expected

performance or minimizes cost

Project Resource

Allocation

Computationally

efficient algorithm

Allocation Decision Process

0

50

100

0

50

100

150

0 200 400 600 800

AA

DT

Th

ou

san

ds

IRI (

in/m

ile)

Segment 1

0

50

100

0

50

100

150

0 200 400 600 800

AA

DT

Tho

usa

nd

s

IRI (

in/m

ile)

Segment 2

1997 2002 2007 2012 2017

1997 2002 2007 2012 2017

Segment 3, ….

Segment Analysis Network Analysis

Allocation Process: Segment Analysis

0

50

100

0

50

100

150

0 200 400 600 800

AA

DT

Tho

usa

nd

s

IRI (

in/m

ile)

Segment 1

0

50

100

0

50

100

150

0 200 400 600 800

AA

DT

Tho

usa

nd

s

IRI (

in/m

ile)

Segment 2

1997 2002 2007 2012 2017

1997 2002 2007 2012 2017

Segment 3, ….

Decision

Do nothing

MRR A

MRR B

Decision

Do nothing

MRR A

MRR B

Segment Analysis: return on investment

Decision Year 1

Do nothing Cost=0 Benefit=0

MRR A Cost A Benefit A

MRR B Cost B Benefit B

Segment analysis period

0

50

100

150

0 200 400 600

IRI (i

n/m

ile

)

0 10 20 30 40 50

0

50

100

150

0 200 400 600

IRI (i

n/m

ile

)

0 10 20 30 40 50

200

150

100

50

Network analysis period

Decision Year 1

Do nothing Cost=0 Benefit=0

MRR A Cost A Benefit A

MRR B Cost B Benefit B

Segment 1

Segment 2

Segment 3, ….

Segment Analysis: selection of best MRR

Decision Year 1

Do nothing Cost=0 Benefit=0 -

MRR A Cost A Benefit A B/C=3

MRR B Cost B Benefit B B/C=1

Segment analysis period

0

50

100

150

0 200 400 600

IRI (i

n/m

ile

)

0 10 20 30 40 50

0

50

100

150

0 200 400 600

IRI (i

n/m

ile

)

0 10 20 30 40 50

200

150

100

50

Network analysis period

Decision Year 1

Do nothing Cost=0 Benefit=0 -

MRR A Cost A Benefit A B/C=0.5


Segment 1

Segment 2

Segment 3, ….

Network Analysis: Segment Selection

Washington, D.C.

Richmond

Virginia Beach

< 80 < 100 < 120 < 140 < 160 >160

Washington,D.C.

Richmond

Virginia Beach

< 25 < 50 < 100 < 150 < 200 >200

Current Traffic Volume of System

(AADT x1000)

Current Condition of Roadway Facilities

– Average IRI (in/mile)

Criteria for segment selection:

improve Given Metric at

network level for next year

• IRI

• Traffic Weighted IRI

• Remaining Service Life

• % Good or bad

• …

Network analysis: segment selection

Segment 1 Year 1

MRR A Cost A Benefit A B/C=3

0

50

100

150

0 200 400 600

IRI (i

n/m

ile

)

0 10 20 30 40 50

0

50

100

150

0 200 400 600

IRI (i

n/m

ile

)

0 10 20 30 40 50

200

150

100

50Segment 2 Year 1


Segment 1

Segment 2

Total $

Total $ ≤ Annual BUDGETCriteria for segment selection:

reduce Traffic-weighted IRI for

the Network Optimization period

Segment 3, ….

Segment 3 Year 1

MRR A Cost A Benefit B B/C=1

Segment 4 Year 1


Change in TWIRI

Change in TWIRI

Change in TWIRI

Change in TWIRI


Segment 1 Year 1 Year 2 Year 3

MRR A B/C=3 B/C=3 B/C=3

0

50

100

150

0 200 400 600

IRI (i

n/m

ile

)

0 10 20 30 40 50

0

50

100

150

0 200 400 600

IRI (i

n/m

ile

)

0 10 20 30 40 50

200

150

100

50Segment 2 Year 1 Year 2 Year 3

MRR B B/C=1 B/C=3 B/C=3

Segment 1

Segment 2

Total $ (1)

Total $ ≤ Annual BUDGETCriteria for segment selection:

reduce Traffic-weighted IRI for

the Network Optimization period

Segment 3, ….





Total $ (2) Total $ (3)




0

50

100

150

0 200 400 600

IRI (i

n/m

ile

)

0 10 20 30 40 50

0

50

100

150

0 200 400 600

IRI (i

n/m

ile

)

0 10 20 30 40 50

200

150

100

50Segment 2 Year 1 Year 2 Year 3


Segment 1

Segment 2

Total $ (1)





Total $ (2) Total $ (3)Segment analysis period

Network analysis period Network

optimization period

doing more with less: economically-efficient management of ...doing more with less:...

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