MNDOT SURFACE CHARACTERISTICS STUDIES By Bernard …...Conclusion • In general, pavement surfaces...
Transcript of MNDOT SURFACE CHARACTERISTICS STUDIES By Bernard …...Conclusion • In general, pavement surfaces...
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