MNDOT SURFACE CHARACTERISTICS STUDIES

By

Bernard Igbafen Izevbekhai, P.E., Ph.D. MnDOT Research Operations Engineer

Presented Monday September 28 2015 At the MnROAD Facility to a

Visiting Team From Sweden

Concrete Pavement Response; Overlays Instrumentation

Concrete Materials: Paste Aggregates, Pozzolan, Thermo

Subsurface Drainage; Base Materials. Geosynthetics etc

Surface Properties: Functional Chara.

Concrete Research Operations

Sustainability & Reliability Analysis

Asphalt Research Operations

M.E. Design Research Operations

MnROAD Operations

LOCATING SURFACE PROPERTIES STUDIES IN OUR RESEARCH PROGRAM

Sound Absorption; Pavement Smoothness, Tire Pavement Noise, Skid Resistance; Rolling Resistance, Hydroplaning, Surface Texture,

PHILOSOPHY OF SURFACE PROPERTIES

• Most Pavement Related Acceptance &Other Decisions are

Based on Functional Characteristics (Ride Quality Skid

Resistance Hydroplaning Potential and Noise) Instead of

Structural Characteristics

• Optimization of Surface Properties is the goal: Not

sacrificing any at the altar of the other.

• Secondary Characteristics Such as Texture Orientation,

Texture Direction and Texture Wavelength are the actual

governing Variables

LINKS TO MOST MNDOT SURFACE CHARACTERISTICS RESEARCH REPORTS

Various

http://www.lrrb.org/search/results/ea2b4098dc897c330e037429fc522910/

Rolling Resistance

http://www.lrrb.org/search/results/17f4e45e6333bf3c81f334ce994f83fb/

http://www.lrrb.org/search/results/030c450c943d5e63a42c2c9adcce3873/

Final Diamond Grinding Research Report

http://www.lrrb.org/media/reports/201318.pdf

ADVANCED PROFILOMETRY: ROBOTEX

IMPEDANCE TUBE

LIGHTWEIGHT PROFILER

LATERAL WANDER IN BOX-CAR CONFIGURATIONS

LATERAL WANDER IN BOX-CAR CONFIGURATIONS

TriODs on a box car configuration Roline on a Boxcar Configuration

1

FRICTION : Hysteresis & Adhesion

CH

IP S

EAL

Thermal Issues With Noise

IL PI Coh IL PI Coh IL

250 83.2 -1.1 0.5 #NUM! #NUM! 0.6 #NUM!

315 81.9 0.1 0.7 75.9 8.2 0.7 79.8

400 83.7 1.0 0.9 79.9 4.2 0.9 82.2

500 85.8 1.1 1.0 85.6 1.6 1.0 85.7

630 91.6 1.0 1.0 89.8 1.0 1.0 90.8

800 97.6 0.1 1.0 95.3 0.2 1.0 96.6

1000 97.0 0.3 1.0 97.7 0.8 1.0 97.4

1250 94.7 0.5 1.0 95.8 0.7 1.0 95.3

1600 96.2 0.4 1.0 95.4 0.6 1.0 95.8

2000 94.5 0.4 1.0 93.8 0.7 1.0 94.2

2500 90.9 0.2 1.0 91.0 0.4 1.0 91.0

3150 85.7 0.1 0.9 86.4 0.2 0.9 86.0

4000 81.0 0.8 0.8 82.0 1.0 0.8 81.5

5000 77.5 0.7 0.7 78.5 1.4 0.7 78.0

A-wtd 103.9 103.5 103.7

OBSI AASHTO TP 76-13

SURFACE TECHNOLOGIES

CTM 7 CTM PARSER

CTM & CTM PARSER

OUTPUT: TEXTURE PROFILE, MPD, SKEWNESS, WAVELENGTH

SURFACE TECHNOLOGIES

TEXTURE SCANNER OUTPUT

OUTPUT: MPD SKEW KURTOSIS

New Horizon: Aggregate Avoidance index

WARP & CURL EVALUATION MnDOT ALPS 2 Built 2008-2010 Instant, Diurnal & Built In Warp n

Curl

R:\Concrete\Concrete Researchers\SC Olson\2013 ALPS Raw Data\ALPSII 2013 DATA\ALPS II MNROAD WARP&CURL 2013 CONSTRUCTION.xlsx

Field Equipment Description

A one-ton articulated device, with a housing for standard tire, (with compensation

for pavement smoothness, and other variables) that allows an angular displacement

due to resistance between tire and pavement and translates this into a rolling

resistance number through mechanics of motion.

DISMANTLING 1 TON CARGO AND TEST SET UP

TON

TEST SET UP

RR TUG MARK IV DEVICE

TUG MARK 4 USED AT MNROAD 2011 & 2014

Test tires AAV4 (left), SRTT (center), MCPR (right).

RR FREE BODY DIAGRAM

2013 RR RESULTS

RR 2013 RESULTS

RR FUEL CONSUMPTION IMPLICATION

CRR

Constant speed driving

Urban FTP-75

30 km/h 50 km/h 70 km/h 90 km/h 110 km/h 130 km/h 150 km/h

0.005 0.77 0.78 0.81 0.85 0.88 0.90 0.92 0.89

0.006 0.81 0.82 0.85 0.88 0.90 0.92 0.94 0.91

0.007 0.86 0.87 0.89 0.91 0.93 0.94 0.95 0.93

0.008 0.91 0.91 0.92 0.94 0.95 0.96 0.97 0.96

0.009 0.95 0.96 0.96 0.97 0.98 0.98 0.98 0.98

0.010 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

0.011 1.05 1.04 1.04 1.03 1.02 1.02 1.02 1.02

0.012 1.09 1.09 1.08 1.06 1.05 1.04 1.03 1.04

0.013 1.14 1.13 1.11 1.09 1.07 1.06 1.05 1.07

0.014 1.19 1.18 1.15 1.12 1.10 1.08 1.06 1.09

0.015 1.23 1.22 1.19 1.15 1.12 1.10 1.08 1.11

Relative Changes of Energy Consumption Averaged for six conventional vehicles.

MNROAD & NETWORK SITE SUMMARY

MNROAD & NETWORK SITE SUMMARY • The coefficient of rolling resistance of the truck tires varied from 0.0044 to

0.0072 on the Mainline cells. • Fuel consumed by the rolling resistance force at 30 MPH varied between 0.006 liter and 0.009 liter per cell, for an average consumption of 5 liter/100 km. • Rolling resistance was 0.0072 on bituminous TH 66 and 0.0061 on concrete TH

10, for a vehicle speed of 55 MPH.

• Spectral analysis of accelerometer data was performed to examine how different pavement types contribute to dynamic rolling resistance. The spectral analysis revealed vibrational modes unique to either bituminous or concrete pavements. In particular, joints between concrete panels gave rise to vibrations at 2.9 Hz corresponding to panel length of 15’ on the Mainline or 27’ on TH10.

• The fuel consumption component attributed to dynamic rolling resistance was

computed to be 0.3 liter/100 km higher on the TH 10 section compared to the TH 66 section

Conclusion • In general, pavement surfaces with higher rolling resistance

coefficients are those with greater surface texture such as porous

materials, conventional diamond grinding, and exposed aggregate.

This finding is supported by the analysis conducted in the report on

the first round of rolling resistance measurements (1).

• The lower resistance surfaces tend to be bituminous pavements

with dense graded aggregates, and concrete pavements with

broom or turf drag surfaces.

• There is little difference in rolling resistance coefficients at speeds

of 50 and 70 km/h, but at 110 km/h the coefficients increased

significantly on all surfaces tested (the MnROAD mainline cells).

CONCLUSION

• As speed increases, the relative effect on energy consumption

diminishes, as other impacts such as wind resistance are much

more prominent.

• Using the 12.5 mm Dense Graded bituminous surface and a

transverse-tined concrete surface as standards, the analysis

estimated up to a 2.3% decrease in energy consumption and up

to a 6.1% increase in energy consumption attributable to the

various pavement surfaces.

• The porous surfaces had the highest increase in predicted energy

consumption, while the PCC broom and turf drag surfaces were

predicted to have the highest decrease in consumption.

MNDOT RR RESEARCH PUBLICATIONS

MNDOT TUG COLLABORATION

• http://www.lrrb.org/media/reports/201207.pdf

• http://www.dot.state.mn.us/research/TS/2014/201429.pdf

MNDOT TRANSTEC EVALUATION

• http://www.lrrb.org/media/reports/201316.pdf

MNDOT FUEL MINER MECHANISTIC APPROACH

• http://www.lrrb.org/media/reports/201539.pdf

SENSITIVITY TO SPIKINESS

Ignoring spikiness compresses the prediction range

APPLICATION

MAJOR IMPLICATION &

AASHTO TP 76-13

MODEL CONCEPTUALIZED

SO WHAT? WHICH TEXTURE IS THE QUIETEST?

LAYOUT AND PROBABILITY DENSITY FUNCTION OF SPIKY AND NON-

SPIKY TEXTURES

Direction (DIR)

• The texture on the pavement can be aligned with the direction of travel

DIR=0 or transverse to the direction of travel DIR=1.

• Concatenations are increased when DIR=1, air compression relief

TEXTURE EFFECTS (TPI-)

HYPOTHESIS & RATIONALE

FITTED NORMAL DISTRIBUTION OF NOISE LEVELS 2007-2011

a) Tread Block Impact Mechanism in Tire Pavement Interaction Causes “Rubber Mallet” Impact Noise

b) Air Compression and Rarefaction Mechanism in Tire Pavement Interaction causes Whistling and Clapping Noise

1

a) Configuration and PDF of a Spiky Texture

Texture Amplitude PDF (Negative Skew)

b) Configuration and PDF of a Non-Spiky Texture

1

Texture Amplitude PDF (Positive Skew)

TEXTURE ORIENTATION

DULUTH (I-35) ST. CLOUD (I-94)

Northbound Southbound Northbound Southbound

ASP (mm) 16 16 16 16

DIR 0 0 0 0

SP 0 0 0 0

IRI (m/km) 0.75* 0.75 1.2** 1.05

Temp (0K) 290 290 298 298

Predicted Post

Grind OBSI (dBA)

99.7 99.7 99.3 99.3

Measured Post

Grind OBSI (dBA)

99.7 99.3 98.7 98.2

Target OBSI was 0.8m /km

**Target OBSI was 1m/km

VALIDATION IN 2 STATE PROJECTS

MODEL: RELATIVE INFLUENCE OF SIGNIFICANT VARIABLES

COMPONENT MIN MAX RANGE SOURCE NOTE

0.25 1.87 1.62 0.65 < IRI <

4.8m/km

Observed range of

Influence of Texture

alone based on the

model is 6.7 dBA

Temp + IRI = 4.51.

Overall spread of 11

dBA theoretically

implied

Economic Quiet

Pavement design is

therefore feasible

-0.78 2.11 2.89 265 <T <305 K

1.68 SP 0 1.68 1.68 SP = 0/1

0.15 5.1 4.96 DIR : 0/1

ASPHALT PAVT NOISE SUMMARY

The porous asphalt surfaces (Cells 86 and 88) are the quietest, while the chip seal (Cell27) and some of the dense graded asphalt mixtures (Cells 4 and 24) are the loudest. OBSI levels are lowest in the summer when the pavement surface is warm; they are highest in cold weather. There is a general upward trend of noise levels over time with the porous asphalt showing a more gradual trend and dense graded surfaces showing a sharper increase. In some cases (e.g., NovaChip) the difference between cool and warm weather results is remarkable, while in other cases (e.g. porous asphalt) the differences in OBSI levels between seasons are much less.

SUMMARY & CONTRIBUTIONS

• Conceptualization of the broad variable groups of texture IRI

and Temperature and how components physically affect noise

• Successful development of model Forms by first successfully

identifying significant variables

• A phenomenological tire pavement noise prediction model

• Relative importance of model components that can facilitate

investment in quiet pavements

• Quiet Pavement: Through this research an award-winning quiet

pavement with durable asperity intervals has been developed.

SUMMARY & contributions

• OBSI = ITN + TPI+ +TPI- is validated

• A tenable near field measurement process. And a large data

base with many texture types

• A knowledge base that may be used in Quiet Pavement

design.

• It validated this model in two major state rehabilitation

projects by successfully predicting OBSI to within 1dBA of

measured value

CONCLUSION

• Research conducted an extensive measurement campaign of OBSI and variables

physically considered to be associated.

• Model Validates the Initial lemma that OBSI = ITN + TPI+ + TPI-

Texture variables are important but not sufficient.

• Texture types arranged ion order of Quietness

• Relative Influence of components have been deduced: Asperity interval and

Direction; Temperature IRI and Spikiness in decreasing order.

• Non Significant Components Identified MPD, DIRSP and DIRMPD P>>0.05 actually

P>0.13

• Environmental (Temperature) and Ride Quality (IRI are Important)

• Model Used in Texture design of 2 MN Projects

• A tool for Quiet Pavement Design

ROAD OF LEARNING

THE ENDLESS