The Ultra-Light InerThe Ultra Light InerApproach in the Stu

Dr. AlessandraNational Center for A

Dr. Andre de Center for Transpo

1

rtial Profiler: a Newrtial Profiler: a New udy of Macrotexture

a Bianchini, PE,Asphalt Technology

Fortier Smitortation Research

at Auburn University

1

Out

• What is the ULIP?• Type of Data and ProType of Data and Pro• Comparison with CT

Additional Applicatio• Additional Applicatio

2

line

ocessingocessing M methodnsns

at Auburn University

2

Ultra Light InertiaLaser scanning pavement sFrequency 10,000 Hz (eveq y , (FHWA Software: MPD eve

al Profiler (ULIP)surfacery 0.5 mm)y )ry 0.5 m of profile

ULIP SurfaApplication: Texture identification Processing: Data filtering to remo

4

ace ProfileMean Profile Depth

ove ‘spikes’ and pavement slope

spikes/sp es/dropouts

at Auburn University

4

Circular TexturMean Profile

5ASTM E2157 ASTM E2157

re Meter (CTM)Depth (MPD)

at Auburn University

5

MPD Compariso

2.5y = 1.008x -

R2 = 0.92

TMet

er)

1

1.5

[mm

] (C

T

0.5MP

D

00 0.5 1

MPD

n: ULIP vs. CTM

- 0.02769654

1.5 2 2.5D [mm] (ULIPt)[ ] ( )

Applicapp

• Micro-milling macrotex• Noise predictionNoise prediction• Segregation prediction

7

ations

xture quality

n

at Auburn University

7

GDOT Old Rehab

¾ in OGFC¾ in OGFC

1 ½ in SMA1 ½ in SMA

Existing PavementExisting Pavement

8

bilitation Strategygy

11 –– 1 ¼ in PEM1 ¼ in PEM1 1 1 ¼ in PEM1 ¼ in PEM

1 ½ in in1 ½ in in1 ½ in in 1 ½ in in New SMANew SMA

1 1 -- 1 ¼ in 1 ¼ in Existing SMAExisting SMA

Existing PavementExisting Pavement

at Auburn University

8

GDOT New Reha

¾ in OGFC¾ in OGFC¾ in OGFC¾ in OGFC

1 ½ in SMA1 ½ in SMA1 ½ in SMA1 ½ in SMA

Existing PavementExisting Pavement

9

bilitation Strategygy

1 1 –– 1 ¼ in PEM1 ¼ in PEM

11 -- 1 ¼ in1 ¼ in1 1 -- 1 ¼ in 1 ¼ in Existing SMAExisting SMA

Existing PavementExisting Pavement

at Auburn University

9

ULIP as QA/QC

• Ridge-to-valley texture de

GDOT Micromilling Proje• Ridge-to-valley texture de

(RVD) measurements• 1 6 mm of RVD required• 1.6 mm of RVD required

specs • Correlation with CTM• Correlation with CTM

Example: Profile – Direction of Millin

1(Courtesy of G

Tool for Texture

epth

ectepth

byby

ng

at Auburn University

0GDOT)

Noise Measuremennts at the Test TrackT t N i• Texture -- Noise– Dense Graded– Stone Matrix Asphalt (SMA)– Stone Matrix Asphalt (SMA)– Open Graded Friction

Courses (OGFC/PFC)– Coarse Porous European

Mixture (PEM)

Noise an• Macrotexture characterizatio

nd MPDn – MPD

Noise and TexMicrotexture: wavelength λ < 0Macrotexture: 0.5 mm < λ < 50Profile Fourier Analysis WProfile Fourier Analysis W

Texture Wavelength

1(

xture Spectrum0.5 mm0 mmWavelengthWavelength

at Auburn University

3(Sandberg and Ejsmont, 2002)

ISO 1347SO 3

Characterization of pavCharacterization of pavof surface profiles

analysis of suanalysis of su

1

3-4:20083 008

vement texture by usevement texture by use s -- Part 4: Spectral urface profilesurface profiles

at Auburn University

4

Test Track SectULIP Signal Ana

Test Track Sect

Filt

1Fourier Analysis – wavelen

ion W4 - OGFCalysis: Exampleion W4 - OGFC

ULIP-Acquired Profile

tered data – baseline length

at Auburn University

5ngth spectrum

ISO 10844: AcousISO 10844: Acousof test tracks for

measuring noisevehicles anvehicles an

Annex E: Calculation oby Noise level Differeylevel variation of the

1

tics - Specificationtics - Specification r the purpose of

e emitted by road d their tiresd their tires

of the Expected pass-ence from Texture road surface (ENDT)

at Auburn University

6

Noise & Texture90

95

100

90

95

100

65

70

75

80

85

dB(A

)

70

75

80

85

90

dB(A

)

60

65

Frequency

GDYR 74.5 76.8 78.8 81.0 84.4 91.7 89.2UNIR 70.9 71.0 74.1 77.6 81.6 84.5 86.7

316 398 501 631 794 1000 125960

65

Frequency

GDYR 74.5 76.8 78.8 81.0 84.4 91.7 89.2 87.7 83.2 80.6UNIR 70.9 71.0 74.1 77.6 81.6 84.5 86.7 86.4 81.4 79.8

316 398 501 631 794 1000 1259 1585 1995 2512

85

90

95

100

A)

60

65

70

75

80

dB(A

1

60

Frequency

GDYR 76.5 79.2 81.3 83.0 86.2 92.1 89.0 85.6 80.7 79.0UNIR 73.1 72.5 75.9 79.0 82.1 85.7 85.6 84.2 79.8 78.2

316 398 501 631 794 1000 1259 1585 1995 2512

e Spectra (DGA)p ( )45

50

ENDT =ENDT = --2 22 2

25

30

35

40

extu

re le

vel,

dB R

MS

.

ENDT = ENDT = 2.22.2

87.7 83.2 80.6 77.0 74.686.4 81.4 79.8 76.9 71.8

1585 1995 2512 3162 3981

10

15

20

1

1.6

2.54

6.31016254063100

160

250

400

630

T t l th

Te

77.0 74.676.9 71.8

3162 3981

Texture wavelength, mm

35

40

45

50

RM

S.

ENDT = +1.9ENDT = +1.9

15

20

25

30

Text

ure

leve

l, dB

at Auburn University

710

15

1

1.6

2.54

6.31016254063100

160

250

400

630

Texture wavelength, mm

75.3 72.475.3 70.4

3162 3981

Noise and Texture Spe

1

ectra: OGFC SectionsN5N5

N6N6

N9

at Auburn University

8

Positive & Negg

• NCAT research indicarelationship between mp

• Models do not directlyinfluence of positive &influence of positive &

• Proposal – look at whaP iti t t– Positive texture

– Negative texture

gative textureg

ates a complex macrotexture and noisey account for the & negative texture& negative textureat the tire “sees”:

at Auburn University

Definition of popos/neg texture?g

at Auburn University

Differentiate popos/neg textureg

at Auburn University

Texture wavelength texttext

spectrum of positive tureture

at Auburn University

Stiffness = slope of Ctexttext

Cumulative Positive tureture

at Auburn University

Porosity = slope of Ctexttext

Cumulative Negative tureture

at Auburn University

Segregatig g on profilep

at Auburn University

QuestQuest

2

tions?tions?

at Auburn University

6