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
LOCATING SURFACE PROPERTIES STUDIES
IN OUR RESEARCH PROGRAM
MnROAD
Operations
Concrete
Research
Operations
Concrete Pavement Subsurface Drainage;
Response; Overlays Base Materials.
Geosynthetics etc
Instrumentation
Sound Absorption; Pavement Smoothness,
Tire Pavement Noise, Skid Resistance;
Rolling Resistance, Hydroplaning, Surface
Texture,
Asphalt
Research
Operations
M.E. Design
Research
Operations
Concrete
Surface
Materials: Paste
Properties:
Functional Chara. Aggregates,
Pozzolan, Thermo
Sustainability &
Reliability Analysis
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/ea2b4098dc897
c330e037429fc522910/
Rolling Resistance
http://www.lrrb.org/search/results/17f4e45e6333bf
3c81f334ce994f83fb/
http://www.lrrb.org/search/results/030c450c943d5
e63a42c2c9adcce3873/
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
1
Roline on a Boxcar Configuration
CHIP SEAL
FRICTION : Hysteresis & Adhesion
Thermal Issues With Noise
OBSI AASHTO TP 76-13
IL
250
315
400
500
630
800
1000
1250
1600
2000
2500
3150
4000
5000
A-wtd
PI
83.2
81.9
83.7
85.8
91.6
97.6
97.0
94.7
96.2
94.5
90.9
85.7
81.0
77.5
103.9
Coh
-1.1
0.1
1.0
1.1
1.0
0.1
0.3
0.5
0.4
0.4
0.2
0.1
0.8
0.7
IL
PI
Coh
0.5 #NUM!
#NUM!
0.7
75.9
8.2
0.9
79.9
4.2
1.0
85.6
1.6
1.0
89.8
1.0
1.0
95.3
0.2
1.0
97.7
0.8
1.0
95.8
0.7
1.0
95.4
0.6
1.0
93.8
0.7
1.0
91.0
0.4
0.9
86.4
0.2
0.8
82.0
1.0
0.7
78.5
1.4
103.5
IL
0.6
0.7
0.9
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
0.9
0.8
0.7
#NUM!
79.8
82.2
85.7
90.8
96.6
97.4
95.3
95.8
94.2
91.0
86.0
81.5
78.0
103.7
SURFACE TECHNOLOGIES
CTM & CTM PARSER
CTM 7 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
Test tires AAV4 (left), SRTT
(center), MCPR (right).
TUG MARK 4 USED AT MNROAD 2011 & 2014
RR FREE BODY DIAGRAM
2013 RR RESULTS
RR 2013 RESULTS
RR FUEL CONSUMPTION IMPLICATION
Constant speed driving
CRR
0.005
0.006
0.007
0.008
0.009
0.010
0.011
0.012
0.013
0.014
0.015
Urban
FTP-75
30 km/h
50 km/h
70 km/h
90 km/h
110 km/h
130 km/h
150 km/h
0.77
0.78
0.81
0.85
0.88
0.90
0.92
0.89
0.81
0.82
0.85
0.88
0.90
0.92
0.94
0.91
0.86
0.87
0.89
0.91
0.93
0.94
0.95
0.93
0.91
0.91
0.92
0.94
0.95
0.96
0.97
0.96
0.95
0.96
0.96
0.97
0.98
0.98
0.98
0.98
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.05
1.04
1.04
1.03
1.02
1.02
1.02
1.02
1.09
1.09
1.08
1.06
1.05
1.04
1.03
1.04
1.14
1.13
1.11
1.09
1.07
1.06
1.05
1.07
1.19
1.18
1.15
1.12
1.10
1.08
1.06
1.09
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/2014/2014
29.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
MAJOR IMPLICATION &
APPLICATION
AASHTO TP 76-13
MODEL CONCEPTUALIZED
SO WHAT? WHICH TEXTURE IS THE QUIETEST?
LAYOUT AND PROBABILITY DENSITY FUNCTION OF SPIKY AND NONSPIKY TEXTURES
TEXTURE EFFECTS (TPI-)
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
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
TEXTURE ORIENTATION
Texture Amplitude PDF (Positive Skew)
a) Configuration and PDF of a Spiky Texture
Texture Amplitude PDF (Negative Skew)
b) Configuration and PDF of a Non-Spiky Texture
1
VALIDATION IN 2 STATE PROJECTS
DULUTH (I-35)
Northbound
Southbound
ST. CLOUD (I-94)
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
99.7
99.7
99.3
99.3
99.7
99.3
98.7
98.2
Grind OBSI (dBA)
Measured Post
Grind OBSI (dBA)
Target OBSI was 0.8m /km
**Target OBSI was 1m/km
MODEL: RELATIVE INFLUENCE OF SIGNIFICANT VARIABLES
COMPONENT
MIN
0.25
1.68 SP
MAX
RANGE
1.87
1.62
-0.78 2.11
2.89
SOURCE
0.65 < IRI <
4.8m/km
265 <T <305 K
0
1.68
1.68
SP = 0/1
0.15
5.1
4.96
DIR : 0/1
NOTE
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
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+ + TPITexture 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
THE END
LESS
ROAD OF LEARNING