reliability in rigid pavement design - transportation.org · m-e pdg design reliability output –...

Further Calibration of the Distress Further Calibration of the Distress

Prediction Models & Reliability EffectsPrediction Models & Reliability Effects

ME PDG Rigid Pavement

Design Reliability Update

Expanding the Realm of Possibility

NCHRP 1NCHRP 1--40B & 140B & 1--40D Team40D Team

Applied Research Associates

� Michael Darter

� Jagannath Mallela

� Harold Von Quintus

� Gregg Larson

� Leslie Titus-Glover

� Chetana Rao

� Dulce Rufino

� Alex Gotlif

U of Minnesota

� Lev Khazanovich

� Graduate students

NCHRP

� Ed Harrigan & Panel

ASU

� Matt Witczak

� Mohamed El-Basyoumy

� Claudia Zapata

� Graduate students


PresentationPresentation

�Reliability Definition M-E PDG

�2004 Calibration

�2005 Independent validation

�2006 Recalibration

� Illustrations of project predictions

�Conclusions


MM--E PDG Design Reliability E PDG Design Reliability

DefinitionDefinition–– Rigid PavementRigid Pavement

� JPCP

� RF = P [Fault < Critical Fault]

� RC = P [Crack < Critical Crack]

� RIRI = P [IRI < Critical IRI]

� CRCP

� RPO = P [Punchout < Critical PO]

� RIRI = P [IRI < Critical IRI]


MM--E PDG Design Reliability E PDG Design Reliability

Output Output –– Rigid PavementRigid Pavement

0

40

80

120

160

200

240

280

320

360

400

0 2 4 6 8 10 12 14 16 18 20 22

Pavement age, years

IRI,

in

/mil

e

IRI at 50%

IRI at 95%

Reliability,

Residual

Critical IRI


MM--E PDG Design Reliability Output E PDG Design Reliability Output

–– Rigid PavementRigid Pavement

Distress

Target

Reliability

Target

Distress

Predicted

Reliability

Predicted Acceptable

172 95 130 83.15 Fail

15 95 1.6 99.98 Pass

0.15 95 0.089 93.13 Fail

Transverse Cracking (% slabs cracked)

Mean Joint Faulting (in)

Performance Criteria

Terminal IRI (in/mi)


Design Reliability ApproachDesign Reliability Approach

� IRI model: closed form variance solution:

Var {IRI} = Var {Initial IRI} + Var {Distress} +

Var {Site Factors} + Var {Residual}

� Distress models: used model residuals (predicted – measured) determined from calibration results


Residuals from Calibration Residuals from Calibration

� Residual = predicted –measured

� Causes� Random (replicate) variation

� Input selection error

� Distress/IRI measure error

� Difference between project mean and 500-ft section

� Model deficiency residual

� Relative magnitude of each?

0

0.05

0.1

0.15

0.2

0 3 6 9 12 15 18

Age, years

Mean tra

nsverse joint

faulting, in

LTPP data 1-37A model

Section Faulting History

Resid


Residuals from Calibration Residuals from Calibration

� Residuals represent the

knowledge that existsof the accuracy of the distress prediction

model.

� Large model deficiency residuals indicates we

are not explaining the

physical phenomenon

well.

Predicted Vs Measured Slab Crack

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

Measured percent slabs cracked

Pre

dic

ted

pe

rce

nt

sla

bs

cra

ck

ed

R2 = 0.86

SEE = 5.02 percent

N = 1585


y = 0.9698x

R2 = 0.8445

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100


Pre

dic

ted

pe

rce

nt

sla

bs

cra

ck

ed

Derivation of Cracking Standard Deviation FunctionDerivation of Cracking Standard Deviation Function

21 3 4


Predicted vs. Standard Deviation of Measured Predicted vs. Standard Deviation of Measured

CrackingCracking

STDC = -0.00172*CRACK2 + 0.3447*CRACK + 4.6772

0

5

10

15

20

25

0 20 40 60 80 100

Predicted cracking, percent

Sta

nd

ard

dev

iati

on

s, p

erce

nt

cra

ckin

g

measured

predicted


JPCP Cracking ReliabilityJPCP Cracking Reliability

CRACKR = predicted cracking at reliability level R,

percent slabs

CRACK50= predicted cracking based on mean inputs

(50% reliability), percent slabs

STDC = standard deviation of cracking at predicted

level of mean cracking

ZR = standard normal deviate (one-tailed)

CRACKR

= CRACK50

+ STDC

• ZR


Residuals of PredictionResiduals of Prediction

� Variations/Uncertainties NOT included:� Future design traffic estimation error

� Calibration database limitations (short sections, missing some design and materials, etc.)

� Others???


2004 Sections Used 12004 Sections Used 1--37A37A

22 States55CRCP LTPP GPS-5

20-years, US 40, IL6CRCP Vandalia, IL

Chicago, Illinois11CRCP Other

23 States196JPCP LTPP GPS-3 & SPS-2

14 States

10 States

26

42

Rehab JPCP CPR, OL

Rehab CRCP OL

9 States36JPCP FHWA RPPR

LocationNo. of SectionsProjects


2005 Validation: NCHRP 12005 Validation: NCHRP 1--40B40B

�Objective was to evaluate adequacy of models developed under NCHRP 1-37A for ME PDG system.

�Criteria

� Pavement sections NOT used in original calibration under NCHRP 1-37A.

� Pavement sections had mostly level 1 and 2 data available for analysis.


2005 Test Sections NCHRP 12005 Test Sections NCHRP 1--40B40B

Minnesota9MnRoad

CT, IA, MI, TX4LTPP GPS-5 (CRCP)

South Carolina12I-77 (CRCP)

Illinois25

Extended AASHO

(Interstate 80)

Virginia4“Smart” Road (CRCP)

Iowa12LTPP SPS-2

Arkansas12LTPP SPS-2

Arizona8

LTPP SPS-2 (Supplemental)



2006 Recalibration: NCHRP 12006 Recalibration: NCHRP 1--40D40D

� Objective was to recalibrate models included in the original M-E PDG.

� DATA: Major increase in quantity & quality of performance data.

� Original sections: Updated observed distress/IRI from 2000 to 2005.

� Added new JPCP, CRCP, and Rehab sections.

� Combined database: 2004, 2005, & 2006

LTPP GPS-3 JPCP Sections LTPP SPS-2, MnROAD, & AASHO JPCP Sections


LTPP GPS-5 CRCP Sections

Illinois, South Carolina, & Virginia CRCP Sections

Expanding the Realm of PossibilityLTPP, NCHRP 10-41, ACPA Diamond Grinding Sections SPS-6 Sections


2006 Recalibration: JPCP2006 Recalibration: JPCP

Minnesota9MN Road

Illinois25Extended AASHO

(AASHO + I-80)

25 States260LTPP GPS-3, SPS-2


Note: Most sections have multiple observations over time


2006 Recalibration: CRCP2006 Recalibration: CRCP

22 States59LTPP GPS-5 (CRCP)

South Carolina12I-77 (CRCP)

Virginia4 (crack spacing)“Smart” Road (CRCP)

Chicago, Illinois10 (heavy traffic)Illinois Interstate

1947-67 US 40

Vandalia, IL 6Vandalia, IL




2006 Recalibration: Rehab2006 Recalibration: Rehab

15 States30

JPCP CPR and OL

(SPS-6, GPS-9, NCHRP

10-41, Others)

To be completedLTPP SPS-6 (HMA

overlays)

10 states42CRCP Overlays (GPS-9,

NCHRP 10-40, others)

LocationNo. of Data Points

Projects




�Calibration process

� Preparation of expanded database (huge effort)

� Improvement of models and algorithms & software modification

� Calibration process, models

� Preparation of final calibrated software



�Calibration process documentation

�NCHRP Research Results Digest 308: >300 software and engineering improvements Version 0.900

�Many additional improvements for Version 1.000 (Feb 2007)


JPCP Cracking Model 2006JPCP Cracking Model 2006

2

11

1(%)

CFDC

Cracking+

=

Model Coefficients

1-37A: C1 = 1.0 ; C2 = -1.68

1-40D: C1 = 1.0; C2 = -2.00

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100


Pre

dic

ted

pe

rce

nt sla

bs c

racke

dR2 = 0.86

SEE = 5.02 percent

N = 1585


JPCP Unbonded Overlay Cracking Model 2006JPCP Unbonded Overlay Cracking Model 2006

2

11

1(%)

CFDC

Cracking+

=

Model Coefficients

1-37A: C1 = 1.0 ; C2 = -1.68

1-40D: C1 = 1.0; C2 = -2.00

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100


Pre

dic

ted p

erc

en

t sla

bs c

racke

dR

2 = 0.72

SEE = 3.95 percent

N = 60


JPCP CPR Cracking Model 2006JPCP CPR Cracking Model 2006

2

11

1(%)

CFDC

Cracking+

=

Model Coefficients

1-37A: C1 = 1.0 ; C2 = -1.68

1-40D: C1 = 1.0; C2 = -2.00

0

10

20

30

40

50

60

70

80

90

100

0 20 40 60 80 100


Pre

dic

ted

perc

en

t sla

bs c

racked

R2 = 0.90

N = 94

SEE = 6.5 percent


JPCP Joint Faulting Model 2006JPCP Joint Faulting Model 2006

0

0.05

0.1

0.15

0.2

0.25

0.3

0 0.05 0.1 0.15 0.2 0.25 0.3

Measured mean transverse joint faulting, in

Pre

dic

ted

me

an

tra

nsve

rse

jo

int fa

ultin

g, in

R2 = 0.62

SEE = 0.0276 in

N = 1260


JPCP CPR Faulting Model 2006JPCP CPR Faulting Model 2006

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14

Measured transverse joint faulting, in

Pre

dic

ted

tra

nsv

ers

e j

oin

t fa

ult

ing

, in

R2 = 0.61

N = 40

SEE = 0.02 in


CRCP Punchout Model 2006CRCP Punchout Model 2006

3

2

1

1C

FDC

CPO

+

=

Model Coefficients

1-37A: C1 = 105 ; C2 = 4; C3 = -0.38

1-40D: C1 = 196; C2 = 20; C3 = -0.50

y = 0.9993x

R2 = 0.7395

0

10

20

30

40

50

60

0 10 20 30 40 50 60

Predicted punchouts, #/mile

Mea

sure

d p

un

cho

uts

, #

/mil

e

R2 = 0.74

SEE= 3.6

N = 345


Comparison of Measured and Comparison of Measured and

Predicted JPCP IRIPredicted JPCP IRI

0

50

100

150

200

250

0 50 100 150 200 250

Measured IRI, in/mile

Pre

dic

ted

IR

I, i

n/m

ile N = 1148

R2 = 0.64

SEE = 13.7 in/mi


LTPP 6LTPP 6--3030, CA3030, CA

0

20

40

60

80

100

0 5 10 15 20 25 30 35

Pavement Age, years

Pe

rce

nt

Sla

bs

Cra

ck

ed

Measured (12-ft) Predicted (12-ft)

Measured (19-ft) Predicted (19-ft)


LTPP 12LTPP 12--3811, FL3811, FL

0

20

40

60

80

100

0 5 10 15 20 25 30 35

Pavement Age, years

Perc

en

t S

lab

s C

racked

Measured Predicted


LTPP 5LTPP 5--0214, AR0214, AR

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0 2 4 6 8 10

Pavement Age, years

Mean

Jo

int

Fau

ltin

g,

in

Measured Predicted


LTPP 53LTPP 53--3013, WA3013, WA

0

0.05

0.1

0.15

0.2

0.25

0 5 10 15 20 25 30 35

Pavement Age, years

Mean

Jo

int

Fau

ltin

g,

in

Measured Predicted


Crack Spacing in VA Smart RoadCrack Spacing in VA Smart Road

0

20

40

60

80

100

1 2 3 4

Cra

ck

Sp

ac

ing

, in

ch

M-E PDG prediction - friction limit and set temperature

Four year field data

ATB 12 CTB 14 ATB 12 CTB 14


Crack Width Crack Width at Steel Depthat Steel Depth

VA Smart RoadVA Smart Road

0

5

10

15

20

25

1 2 3 4

Cra

ck W

idth

, m

ils

ME PDG - at friction limit, set temperature

ME PDG - field crack spacing

Measured at surface

ATB 12 CTB 14 ATB 12 CTB 14


Punchout Prediction for LTPPPunchout Prediction for LTPP Texas 48Texas 48__53105310

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20 25 30

Pavement age, years

Pu

nch

ou

t p

er

mil

e Measured punchout

Predicted punchout


Punchout Prediction for LTPPPunchout Prediction for LTPP Connecticut 9Connecticut 9__05010501

0

20

40

60

80

100

0 5 10 15 20 25 30

Pavement age, years

Pu

nch

ou

t p

er

mil

e

Measured punchout

Predicted punchout


IRI Prediction for LTPP CRCP IRI Prediction for LTPP CRCP Iowa 19_5046Iowa 19_5046

25

50

75

100

125

150

175

200

225

250

0 5 10 15 20 25 30

Pavement age, years

IRI,

in

/mi

Predicted IRI

Measured IRI


Comparison 1Comparison 1--37A with 137A with 1--40D40D

N,

R2

Punchouts

Faulting

Cracking

Distress

345

0.74

993

0.64

1132

0.81

1-40D

512

0.69

220

0.67

564

0.74

1-37A


Local Calibration PotentialLocal Calibration Potential

� All models can be adjusted (Tools, Calibration, Coefs.)

� Key effect: Eliminate “bias”of prediction (significant over

prediction or under prediction

of distress).

� Possible effect: Reduce

residual of prediction

(depends on quality of data).

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.00 0.05 0.10 0.15 0.20 0.25 0.30

Measured mean transverse joint faulting, in

Pre

dic

ted

mean tra

nsvers

e

join

t fa

ultin

g, in

R2 = 0.74

SEE = 0.025 inN = 43


ConclusionsConclusions

� The design reliability methodology used in the

ME-PDG is radically different than that used in

the 1986-93 version.

� Overcomes the major problem that exists with the old

AASHTO version for heavy traffic where the AASHTO model must be entered with extremely large ESALs

which extrapolates far, far, far beyond the accuracy of

the model.

� The result is extreme conservatism in thickness which results in large over design (and excessive thickness

often doesn’t extend life).



� Reliability design methodology in ME-PDG is practical to use (not complicated). But, requires consideration of more than thickness.

� JPCP: thickness, dowel diameter, & joint spacing must be considered together. Other factors may be varied as well.

� CRCP: thickness and reinforcement content must be considered together. Other factors may be varied as well.

� Methodology appears to produce reasonable results over range of reliability


Reliability Level Impact for JPCP ProjectReliability Level Impact for JPCP Project

Design Reliability Vs Slab Thickness

7

8

9

10

11

12

13

14

15

60 80 90 95 99 99.9

Design Reliability

Sla

b T

hic

kn

ess, in


Reliability Level Impact for JPCP ProjectReliability Level Impact for JPCP Project

Design Reliability Vs Dowel Diameter

0.7

0.9

1.1

1.3

1.5

1.7

1.9

60 80 90 95 99 99.9

Design Reliability

Do

wel D

ia., in



� Concepts applied uniformly between pavement

types and overlays and thus should produce

equitable designs.

� The value of “Reliability” (e.g., 90% or 95%)

should not be considered as exact probability, but

relative probability only.



� Selection of Design Reliability values for

designs of low to heavy traffic requires local

consideration and should be a policy issue. It

should be based on consequences of success

or failure of the pavement to perform as

designed & on cost implications. Do NOT use

the old AASHTO recommendations for the new

ME-PDG reliability.



� At the end of the day, the prediction models gain validity for:

�predicting in situ pavement performance, &

�showing reasonable practical sensitivity (see CA, TX, AR, IA, MN, & KS studies).

� This validation activity is far larger and more comprehensive than any other ever conducted to validate predictive engineering models.

� This widespread validation adds to improvement in design reliability.


Thank You!

Any Questions?

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