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Center for Sustainable Transportation Infrastructure

Using Profile Data for Supporting Asset Management Decisions

Gerardo W. FlintschDirector, Center for Sustainable transportation InfrastructureProfessor of Civil and Environmental Engineering

Outline

o Asset Management decisionso How do we use profile data to support

these decisions?o What is the level of detail and accuracy

required?o Some potentially relevant questionso Final thoughts



Asset Management decisions & business processes

o Systematic process of maintaining, upgrading, and operating physical assets cost-effectively, efficiently, and comprehensively.

Engineering

Business-like

ObjectivesEconomics

Asset Management

Integration

DATABASE

INV

ENTO

RY

CONDITION

USAGE

MAINTENANCESTRATEGIES

INFORMATION MANAGEMENT

NETWORK-LEVEL ANALYSISTOOLS

PROJECT LEVEL ANALYSIS (Design)

WORK PROGRAM EXECUTION

PERFORMANCEMONITORING

FEEDBACK

CONDITION ASSESSMENT

PRODUCTS

NETWORK-LEVEL REPORTS

Performance AssessmentNetwork NeedsFacility Life-cycle Cost Optimized M&R ProgramPerformance-based Budget

CONSTRUCTION DOCUMENTS

GRAPHICAL DISPLAYS

NEEDSANALYSIS

PRIORITIZATION / OPTIMIZATION

PERFORMANCE PREDICTION

PROGRAMMINGPROJECT SELECTION

Goals & PoliciesSystem PerformanceEconomic / Social &

Environmental

Budget Allocations

STRATEGIC ANALYSISThe Asset Management Process

U.S. Map -21 National GoalsFocus the Federal-aid program on the following national goals:

1. Safety2. Infrastructure condition3. Congestion reduction4. System reliability5. Freight movement and economic vitality6. Environmental sustainability7. Reduced project delivery delays

Center for Sustainable Transportation InfrastructureSource: http://www.fhwa.dot.gov/policyinformation/presentations/

DATABASE

INV

ENTO

RY

CONDITION

USAGE

MAINTENANCESTRATEGIES

INFORMATION MANAGEMENT

NETWORK-LEVEL ANALYSISTOOLS

PROJECT LEVEL ANALYSIS (Design)

WORK PROGRAM EXECUTION

PERFORMANCEMONITORING

FEEDBACK

CONDITION ASSESSMENT

PRODUCTS

NETWORK-LEVEL REPORTS

Performance AssessmentNetwork NeedsFacility Life-cycle Cost Optimized M&R ProgramPerformance-based Budget

CONSTRUCTION DOCUMENTS

GRAPHICAL DISPLAYS

NEEDSANALYSIS

PRIORITIZATION / OPTIMIZATION

PERFORMANCE PREDICTION

PROGRAMMINGPROJECT SELECTION


Environmental

Budget Allocations

STRATEGIC ANALYSISThe Asset Management Process


Environmental


How do we use profile data to support these decisions?

Infrastructure Condition/ Performance Indicators → Pavements

Service and User Perception

Physical Condition

Structural Integrity / Load-Carrying Capacity

Safety and Sufficiency

Environmental Impact

Serviceability (PSI, IRI)

Distress(PCI)

Deflection(FWD)

Friction (FN)/ Macrotexture

Tire/Pav. NoiseRolling Resistance

→ Pavements

Examples

o Strategic level Performance monitoring

o Network level Pavement management

o Project level Smoothness SpecificationResearch LTPP


Construction Acceptance

o Smoothness for quality acceptance Incentives for superior smoothnessDisincentives for roughness that

exceeds targets Virginia DOT: IRI “targets” for Interstate

and Non-Interstate pavements [applied to 0.01-mile (16 m) pay lots]

o Use of Ride Spec “turns back the clock” by as much as 7 years No significant impact on HMA bid price

(McGhee & Gillespie)Center for Sustainable Transportation Infrastructure

Virginia Smoothness Specification


0

20

40

60

80

100

120

0 2 4 6 8 10 12 14 16 18 20

Time in Service (years)

IRI(

inch

es/m

ile)

w/o Spec Provw/ Spec Prov"Terminal" IRI

6 to 8 in/mi decrease

Pavement Management

o Smoothness has been a key parameter for supporting network-level decisions since the genesis of PMSWhat, When, Where USA: AZ and KS started collecting roughness in

the early 70’s Developing counties: key input for the HDM model

o Trigger preservation & rehabilitatiotno Impact on user costs and environmental impacts


VA: Average IRI by County (1997)Primary Highways

Source: 1997 State of the Pavement Report

f1

Bild 14

f1 start here Thflintsch; 2005-09-15

0

20

40

60

80

100

120

140

160

1972 1977 1982 1987 1992 1997 2002Year

Rou

ghne

ss (M

aysm

eter

Uni

ts)

`

RSL

I 10 WB, MP 337 - 344

Preservation Treatment

Threshold Roughness Value for Interstates

Roughness Prediction for AZ DOT

Effect of M&R

Remaining Service Life Estimate

System Performance Monitoringo Federal government in the US has used

smoothness for assessing road performance for many years. FHWA (old) Roughness Objective: “To increase the

percentage of miles on the NHS that meet Owner-Agency managed pavement performance for acceptable ride quality to over 93 percent within 10 years”

IRI less than or equal to 170 inches/mileo Needs reliable data for aligning investments

with desired performance→ HPMS → HERS


Performance Measures Being Considered for MAP 21 (§150(c))

PROGRAM MEASURE CATEGORYNational Highway Performance Program

• Pavement Condition on the Interstates• Pavement Condition on Non-Int. NHS • Bridge Condition on NHS• Performance of Interstate System • Performance of Non-Interstate NHS

Highway Safety Improvement Program

• Serious Injuries per VMT• Fatalities per VMT• Number of Serious Injuries• Number of Fatalities

CMAQ Program • Traffic Congestion• On-road mobile source emissions

Freight Policy • Freight Movement on the Interstate

Source: T. Van, 11th Infrastruture Management Research and Education Workshop, Washington, DC, Jan 2013

Condition of Principal Highways (2009)

Source: http://www.fhwa.dot.gov/policyinformation/pubs/hf/pl11028/chapter7.cfm

Highway Fatality RatesInterstate Pavement Smoothness (IRI) by State

Bridge Deficiencies


What is the level of detail and accuracy required?

1. Organize annual equipment “rodeos” + verification

2. Seasonal monitoring

3. Evaluation & development of new technologies

4. Evaluation of high-friction systems

5. CFME deployment &friction technology transfer

6. Outreach: Pavement Evaluation 2010SURF 2012

Background: Pavement Surface Properties Consortium

Equipment Comparisons / “Rodeos”o Since 2007o Profile, friction, textureo Added Noiseo AASHTO R56 for

certification


AASHTO R56

o Focused on profilers used for quality control and also applicable for network profilers

o Uses cross correlation to evaluate: Repeatability (CC ≥ 92%)

Ten runs Cross correlate with each other and average

Accuracy (CC w/ reference ≥ 90%) Ten runs Cross correlate with reference and average

ProVAL SoftwareCenter for Sustainable Transportation Infrastructure

“Rodeo” concept closely tied to US RPUG


We run ours at the Virginia Smart Road

VTTI

Bridge

Road

Reference Profiler Comparison

Cross-Correlation

Left Right

SECTION 1 JRCP 92.1 94.1

SECTION 2 CRCP G&G 72.8 66.6

SECTION 3 SMA/OGFC 91.7 92

SECTION 4 SM 9.5 91.9 90.2

SECTION 5 SM 9.5/12.5 89.7 92.8

Can we use it smooth pavements?

IRI Comparison – Section 4 SM 9.5

2012

2013

99 10098 10293

9894 9790

10195 9894

10393

98

0

20

40

60

80

100

120

LWP RWP

IRI (

in/m

i)

1019698 9696 9593

10096 98101 989994

10196

100 9798 94

0

20

40

60

80

100

120

LWP RWP

IRI (

in/m

i)

Reference

Repeatability – Section 4 SM 9.5

95 93 93 93 91 9095 94 94 94

97 9794

9197

929491 93 94 93 94 94 93

95 9397 95 94

9196

94

0

10

20

30

40

50

60

70

80

90

100

Left Right Left Right Left Right Left Right Left Right Left Right Left Right Left Right

GA MS SC1 SC2 AMES VTTI VA PENN

%

25 mph 45 mphAverage Left: 93.7 % Average Right: 93.9 %

Reproducibility – Section 4 SM 9.576 75

83 8374 72 67 63

91 9095 93

70 68

88 8879 76

84 8069 67 68 64

90 87 89 87

7166

89 87

0102030405060708090

100



%45 mph

Surpro VT Surpro MSAverage Left: 79.6 % Average Right: 78.4 %

73 7482 82

75 73 73 70

92 90 94 90

68 67

89 88

75 7482 78

71 6977

72

91 88 85 8170

65

89 86

0102030405060708090

100



%

25 mph

Surpro VT Surpro MSAverage Left: 80.0% Average Right: 78.3%


Some potentially relevant questions

Do we need a “hierarchical” specification for profilers?

Project Level

Strategic Level

NetworkLevel

Performance

Structure Condition

IQL-5

IQL-4

IQL-3

IQL-2

IQL-1

SystemPerformanceMonitoring

Planning andPerformance Evaluation

Program Analysis orDetailed Planning

Project Level orDetailed Programming

Project Detail orResearch

HIGH LEVEL DATA

LOW LEVEL DATA

Information Quality Levels

Can we use probe (or regular) vehicles for road infrastructure

health monitoring?

At least for supporting high-end strategic- and network-level decisions?

Pavement Assessment and Management Applications Enabled by the Connected Vehicles

Environment – Proof-of-Concept Objective: To use data collected from probe vehicles to extract information that could be used to remotely and continuously determineroad infrastructure health

Comparison

Is IRI the most appropriate way of summarizing the profile data?

How significant is the impact of smoothness on vehicle operation

costs and GHG emissions?


Can profile data help more “sustainable” network-level pavement management decisions?

1.21.41.61.822.22.42.62.83

x 107

60

70

80

90

100

0

10

20

30

40

50

60

70

80

90

Condition

Energy Consumption (MJ)

Cos

t (Te

ns o

f Tho

usan

ds o

f Dol

lars

)

50th Percentile5th Percentile95th Percentile

Incorporating pavement LCA use-phase into pavement management

MinMax

Min

MinMax

Max

MinMax

MinCostCost

CostCost,

ConditionConditionConditionCondition

,EnergyEnergy

EnergyEnergy

Nat

iona

l Sus

tain

able

Pav

emen

t Con

sort

ium


Final Thoughts

Final Thoughts

o Profile data is a key asset management input→ user perception & level of service

o It is used (and needed) for supporting business processes at various management levels

o We may not need the same degree of detail and accuracy for all levels


Blacksburg, VA

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