S64_Rideability Prediction for HMA Overlay Treatment of Flexible and Composite Pavements for...

7/29/2019 S64_Rideability Prediction for HMA Overlay Treatment of Flexible and Composite Pavements for Louisiana_LTC2013

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Rideability Prediction

of HMA Overlay Treatment ofFlexible and Composite

PavementsBy

Muhammad Jamal KhattakAssociate Professor

University of Louisiana at Lafayette

Muhammad A. Nur and Muhammad R. Bhuyan

Graduate Research Assistant

University of Louisiana at Lafayette
http://www.louisiana.edu/


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Presentation Layout

1. Background Pavement Treatment Study: LTRC-10-4P

Research Objectives

Research Phases

2. Development of IRI Models Data Source

Project Selection

Factors Effecting IRI Statistical Analysis

IRI Model Behavior

3. Conclusions and Recommendations


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Pavement Treatment Study

LTRC-10-4P

Background LADOTD has spent substantial financial resources on

various rehabilitation/maintenance treatments to

minimize the pavement distresses and

improve the pavement life

Effectiveness of any treatment largely depends on thetime of treatment and trigger governed by treatment

performance models. Recent study completed by LTRC regarding PMS

emphasized the importance of developing treatmentperformance models


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LTRC-10-4P

Objective of Study

Develop pavement treatment performance

models in support of cost-effective selectionof pavement treatment type and time oftreatment.


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LTRC-10-4P

Three Phase Study

1. Phase I

- Review and Project Selection

2. Phase II

- Treatment Performance Modeling

- Costs Benefits of Treatments3. Phase III

-Model Integration and Training


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Development of

International Roughness Index(IRI) Model


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IRI Model

RSL-1

IRI

Elapsed Time (Year)

Threshold

RSL-2


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IRI Model

RSL(BT)

RSL(AT)

LE

IRI

Elapsed Time (Year)

LE= RSL (AT)RSL (BT)

Threshold


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Development of IRI Model

Data Source

Project Selection

Factors Effecting IRI

Statistical Analysis

IRI Model Behavior


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Data Source

Historical Data

LADOTDs mainframe database

Material testing system (MATT)

Tracking of Projects (TOPS)

Letting of projects (LETS)

Highway NEEDS, the traffic

volumes data, and the pavementdesign and system preservationdatabase.


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Data Source

Distress Data

Distress data from PMS database

IRI, Rut, Fatigue, Longitudinaland Transverse crackings

Recorded every two years by theautomatic road analyzer (ARAN)for every 1/10th of a mile (1995-

2009)


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Data Source

Climatic Data 20 weather stations throughout

Louisiana-National Climatic Data

Center (NCDC)

Daily maximum, minimum andmean temperature (2000-10)

Daily precipitation values fromyear (2000 -12)

Inverse distance weighting (IDW)method- For data interpolation.


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Project Selection

Project Selection All pavement sections having:

One IRI data point just before treatment

Three or more IRI data points aftertreatments

Acceptance Criteria

Criteria 1- One IRI data point beforetreatment.

Criteria 2- Positive gain in distress based onthe best-fit curve.


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Project Selection

Composite Pavement

78 projects were selected comprising of 451.5 km(280 mile)

Data averaged for each projectsNo. Observation=280

Flexible Criteria

170 projects were selected comprising of 1168.7km (726.2 mile)

Data averaged for each projects

No. Observation=623


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Factor Effecting IRI

Factors Effecting IRI Equivalent Single Axle Load (ESAL)

HMA Overlay thickness (Th)

PCC layer thickness (TPCC)

Functional classification (FC)

IRI just before treatment (IRIp)

Temperature Index (TI)

Precipitation Index (PI)New


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Factors Effecting IRI New Temperature Index (TI) similar to Freezing

Index (FI) is introduced to evaluate the effect of

temperature.

TI represents the variation of temperature of aparticular project over the year with reference to20oC (68oF)

A negative One-degree day---> One day below20oC ---> {20-21=1oC}. 1-day= 1-oC-day


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Dec.31

Jan.31

Feb.28

Mar.31

Apr.30

May.31

Jun.30

Jul.31

Aug.31

Sep.30

Oct.31

Nov.30

Cumulative Degree-days -25 -362 -703 -896 -866 -676 -413 -142 135 335 353 223

-1000

-800

-600

-400

-200

0

200

400

600

C

umulativeDegree-days

TemperatureIn

dex

(degree-days

)

Control Section 850-29-1TI for year 2010
http://www.louisiana.edu/http://www.louisiana.edu/http://www.louisiana.edu/http://www.louisiana.edu/http://www.louisiana.edu/http://www.louisiana.edu/


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600

800

1000

1200

1400

2 3 4 5 7 8 58 61 62

TemperatureIn

dex

(CDays)

LADOTD Districts

Temperature Index (TI)Year 2008


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0

5000

10000

15000

20000

25000

2000 2002 2004 2006 2008 2010

Cumula

tiveTemperatureIndex

(CDays)

Data Collection Year

001-02-1

001-08-1

003-06-1


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Precipitation Index (PI) The PI is the product of cumulative

precipitation/year and number of days/year of

precipitation.

PI= P. Np

P is the total precipitation/year (cm), and Np is

the number of days of precipitation in the year. PI represents the amount and exposure of

pavement to moisture


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20000

24000

28000

32000

36000

2 3 4 5 7 8 58 61 62

PrecipitationIndex(cm-days)

LADOTD Districts

Precipitation Index (PI)

For Year 2008


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0

30000

60000

90000

120000

150000

2000 2002 2004 2006 2008 2010

CumulativePrecipitat

ion

Index

(cm-days)

Data CollectionYear

001-02-1

001-08-1

003-06-1


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Regression Analysis

PIatCTIaFCTT

CESALaIRIaaIRI

PCCh

po ....)/(

)ln(.)ln(.)ln( 4321

HMA on Composite Pavement

)ln(362.0)(310.0732.1 PPo IRISDLn

CESAL= Cummulative Equivalent Single Axle Load

Th= HMA Overlay thickness

TPCC= PCC layer thickness

FC= Functional classificationIRIp= IRI just before treatment

CTI= Cummulative Temperature Index

PI= Precipitation Index

SDo= Standard Deviation

t= Elapsed time after treatment


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Regression Analysis

Regression Statistics

Multiple R 0.79R Square 0.63

Adjusted R Square 0.62Standard Error 0.254Observations 280F-statistics 91.88

Significance-F 1.61x10-56Coefficients Value StandardError t-stats p-values

ao 2.0829 0.204 10.20 6.3x10-21a1 0.00151 0.0004 3.50 0.0005a2 0.2727 0.039 6.98 2.2x10-11a3 2.22x10-06 1.0x10-06 2.17 0.031a4 5.36x10-06 2.8x10-06 1.93 0.055
http://www.louisiana.edu/http://www.louisiana.edu/


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Regression Analysis

3

4

5

6

7

3 4 5 6 7

Pred

ictedLn(IRI),(c

m/km)

Actual Ln(IRI), (cm/km)

n=280R2=0.63


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Regression Analysis

-1

-0.8

-0.6

-0.4

-0.2

00.2

0.4

0.6

0.8

1

3 4 5 6 7

Residual

Predicted Ln(IRI), (cm/km)


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Regression Analysis

HMA on Flexible Pavement

CESAL= Cummulative Equivalent Single Axle Load

Th= HMA Overlay thickness

FC= Functional classification

IRIpp= Existing/Current IRI value

TI= Temperature Index

PI= Cummulative Precipitation Index

SDo= Standard Deviation

t= Elapsed time after treatment

tCPIaTIaT

CESALa

FCaaIRI

h

o...

)(

)ln(.)

1.()ln( 4321

)(513.0)(7231.0304.1 PPo IRILnSDLn


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Regression Analysis

Regression StatisticsMultiple R 0.685R Square 0.47

Adjusted R Square 0.46Standard Error 0.16Observations 623

F-statistics 108.953Significance-F 2.17x10-82

Coefficients Value Standard Error t-stats p-valuesao 3.7032 0.0737 50.270 0.00a1 -0.27981 0.0571 -4.905 1.20x10-06a2 0.12078 0.01436 8.413 2.79x10-16a3 2.66x10-04 6.42x10-05 4.148 3.83x10-05a4 9.19x10-08 1.47x10-08 6.249 7.71x10-10a5 0.1958 0.01214 16.122 4.67x10-49


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Regression Analysis

n=623R2=0.47

4

5

6

4 5 6

Pred

ictedLn(IRI),

(cm/km)

Actual Ln(IRI), (cm/km)


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Regression Analysis

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

4 5 6

Residual

Predicted Ln(IRI), (cm/km)


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Regression Analysis

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0

0.05

0.1

0.15

0.2

0.25

-40 -30 -20 -10 0 10 20 30 40 -40 -30 -20 -10 0 10 20 30 40

Cumulative%

NormalizedFreq

uency

Error (%)

75 % of Dataused for

Modelling

25 % of Dataused for

Predicting


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Model Behavior

0

100

200

300

400

0 5 10 15 20

IRI,(cm

/km)

Time, (Years)

001-03-1 008-07-1 052-06-1

Threshold 001-03-1 008-07-1

Composite


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Model Behavior

Flexible

0

100

200

300

400

0 5 10 15 20

IRI,(cm/km

)

Time, (Years)

084-01-1 027-01-1 176-01-1 Threshold

084-01-1 027-01-1 176-01-1
http://www.louisiana.edu/http://www.louisiana.edu/


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Conclusions

IRI largely affected by cumulative ESAL, thickness of thepavement, temperature and precipitation.

IRI of the overlay was a function of the pretreatmentcondition of the road and highway functional classification.

IRI prediction models were developed which exhibited goodagreements between the measured and predicted IRI values.

Newly developed temperature index (TI) and precipitationindex (PI) showed strong statistical significance forpredicting IRI.

It is postulated that the developed models will be a goodpavement management tool for predicting the IRI of theoverlay treatment, which will facilitate timely maintenanceand rehabilitation action.


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Thanks!

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