Estimation of Stone-loss on
network condition surveys by use
of multiple texture lasers
Thomas Wahlman, thomas.wahlman@ramboll.se
Peter Ekdahl, peter.ekdahl@ramboll.se
OUTLINE
•5 years of experience in Finland
•Ravelling project in Netherlands
•Homogeneity
Indication of Ravelling
Finland
…detected by point lasers
Basic information of collected data
The Laser works at a frequency of 32 000 pulses per second
Speed
km/h
30
50
70
90
Interval
between
values
0.3
0.4
0.6
0.8
mm
mm
mm
mm
Spotsize
0.5 mm
Vertical
resolution
0.1 mm
4
What will the collected values look like?
Porous Asphalt, 16 years old
1100
1120
1140
1160
1180
1200
1220
1240
1260
1280
1300
1340
1320
1360
1380
1400
10
Max stone size
5
What is what?
Crack or Loss of stone?
-5
-10
mm-data
-15
Even Surface
Dense Asphalt, 12 years old
-20
276.2
276.3
276.4
276.5
5
0
-5
Depth
Depth
0
-10
-15
mm-data
-20
Indication of Ravelling - Model
Input
Read all RMST value of each 100mm
Step 1
Divide into homogenous sections
-similar surface type
Step 2
Look for possible ravelling in each 5m-interval
Output
Validate result and state Ravelling/Not Ravelling per 5m
6
2%
4%
2%
2%
4%
2%
2%
2%
10%
8%
4%
8%
2%
8%
2%
8%
8%
2%
4%
6%
2%
16%
10%
4%
4%
8%
4%
6%
2%
2%
4%
2%
4%
2%
2%
2%
4%
12%
42%
36%
52%
60%
32%
22%
28%
28%
50%
58%
26%
34%
30%
40%
42%
42%
26%
44%
60%
44%
46%
36%
22%
20%
14%
20%
22%
46%
54%
46%
36%
54%
34%
50%
56%
44%
38%
62%
44%
54%
54%
40%
46%
50%
50%
40%
48%
44%
54%
46%
42%
56%
44%
10%
6%
4%
2%
4%
12%
30%
16%
14%
4%
12%
22%
16%
4%
16%
10%
22%
6%
6%
10%
8%
30%
22%
22%
30%
2%
8%
16%
10%
8%
8%
8%
12%
10%
10%
20%
20%
12%
4%
4%
10%
24%
14%
18%
16%
24%
20%
6%
2%
4%
6%
2%
4%
2%
2%
2%
2%
4%
6%
2%
2.0
4%
10%
8%
8%
2%
2%
6%
6%
8%
14%
2%
8%
6%
8%
8%
4%
4%
2%
4%
8%
4%
2%
2%
4%
4%
14%
6%
16%
14%
12%
6%
8%
10%
1.9
10%
1.8
10%
12%
18%
18%
18%
22%
8%
12%
16%
18%
26%
8%
12%
24%
16%
18%
18%
26%
12%
24%
20%
24%
26%
22%
20%
20%
6%
30%
20%
1.7
20%
20%
28%
26%
24%
18%
32%
26%
22%
24%
26%
22%
32%
12%
24%
26%
22%
22%
36%
24%
32%
14%
8%
12%
12%
24%
14%
14%
4%
1.6
32%
24%
12%
18%
16%
12%
36%
18%
20%
12%
18%
26%
18%
34%
22%
14%
14%
18%
20%
12%
10%
10%
14%
10%
10%
10%
12%
20%
2%
1.5
26%
28%
10%
12%
10%
18%
12%
24%
8%
18%
6%
20%
18%
12%
6%
12%
10%
8%
12%
16%
22%
8%
8%
6%
2%
2%
14%
2%
2%
1.4
1.3
1.05
1.06
1.21
1.32
1.22
1.31
0.95
1.17
1.15
1.14
1.17
1.15
1.11
1.07
1.09
1.28
1.12
1.1
1.1
1.2
1.2
1.3
1.3
1.3
1.4
1.3
1.4
1.2
1.3
0.5
0.4
0.4
0.4
0.4
0.6
0.5
0.4
0.4
0.4
0.4
0.5
0.4
0.4
0.5
0.5
0.5
0.4
0.4
0.4
0.4
0.4
0.5
0.6
0.5
0.5
1.2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0.7
0.7
0.6
0.6
0.6
0.7
0.7
0.7
0.8
0.6
0.7
0.3
0.2
0.2
0.2
0.2
0.2
0.3
0.2
0.3
0.2
0.2
0.3
0.3
0.2
0.2
0.3
0.2
0.3
0.2
0.2
0.2
0.2
0.2
0.3
0.3
0.3
0.3
1.1
PERC 95%
0.6
0.8
0.9
1
0.8
0.9
0.7
0.6
0.8
0.9
1
0.8
0.8
0.6
0.7
1
0.9
0.6
0.7
0.7
0.8
1.0
0.8
0.9
0.8
1.0
1.0
0.6
1.2
0.4
0.4
0.2
0.3
0.3
0.5
0.4
0.3
0.4
0.2
0.3
0.3
0.2
0.3
0.4
0.3
0.4
0.3
0.2
0.3
0.2
0.3
0.4
0.5
0.5
0.5
1.0
PERC 5%
0.9
1.3
0
1.1
0.5
0.3
0.7
0.7
0.2
0.7
0.6
0.5
0.4
0.3
0.1
0.4
0.3
##
0.2
0.2
0.5
0.1
0.0
0.3
0.1
0.7
0.2
0.1
0.2
1.3
0.3
0.2
0.7
0.0
0.7
0.6
0.0
1.7
0.0
0.2
0.5
##
0.9
1.4
0.7
0.4
0.7
0.0
0.6
0.2
0.6
1.2
1.2
1.3
1.2
0.9
Range
0.6
2.6
##
1.5
##
##
1.9
##
##
0.9
2.0
0.4
0.2
##
##
0.1
0.5
##
0.5
##
0.3
##
##
##
##
0.5
##
##
##
2.7
2.5
##
0.6
##
0.5
0.5
##
6.2
0.2
0.2
##
##
1.3
3.2
0.1
0.9
0.8
##
0.5
##
0.7
3.3
0.9
3.3
2.7
0.8
Skewness
0.1
0.2
0.2
0.2
0.2
0.2
0.1
0.2
0.2
0.2
0.2
0.2
0.2
0.1
0.2
0.2
0.2
0.1
0.1
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.1
0.4
0.1
0.1
0.0
0.1
0.1
0.1
0.1
0.1
0.1
0.0
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.0
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.7
Kurtosis
1.2
1.3
1.3
1.5
1.4
1.3
1.2
1.2
1.3
1.5
1.5
1.3
1.3
1.2
1.2
1.4
1.4
1.2
1.2
1.2
1.3
1.5
1.4
1.6
1.4
1.6
1.5
1.2
1.5
0.6
0.5
0.4
0.5
0.5
0.7
0.6
0.5
0.6
0.4
0.5
0.5
0.5
0.5
0.6
0.5
0.6
0.5
0.4
0.5
0.5
0.5
0.7
0.7
0.7
0.7
0.6
Std dev
0.8
0.8
0.9
0.8
0.9
0.6
0.9
0.7
0.7
0.9
0.9
0.7
0.9
0.8
0.8
0.8
0.8
1.0
0.8
0.9
0.7
0.9
1.0
0.8
0.9
1.0
1.1
0.9
0.9
0.3
0.3
0.3
0.2
0.4
0.4
0.3
0.3
0.3
0.3
0.4
0.3
0.4
0.4
0.3
0.3
0.4
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.4
0.4
0.5
Max
0.8
0.8
0.9
0.9
0.9
0.9
0.8
0.8
0.9
0.9
0.9
0.8
0.8
0.9
0.8
0.9
0.9
0.9
0.9
0.8
0.8
1.0
1.0
1.0
1.0
1.0
1.0
0.9
0.7
0.3
0.3
0.3
0.3
0.3
0.4
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.4
0.3
0.3
0.3
0.3
0.3
0.4
0.4
0.4
0.4
0.4
Mode
3820
3825
3830
3835
3840
3845
3850
3855
3860
3865
3870
3875
3880
3885
3890
3895
3900
3905
3910
3915
3920
3925
3930
3935
3940
3945
3950
3955
3960
3965
3970
3975
3980
3985
3990
3995
4000
4005
4010
4015
4020
4025
4030
4035
4040
4045
4050
4055
4060
4065
4070
4075
4080
4085
4090
Average
Distance
Indication of Ravelling.
Change in Surface type
2%
2%
2%
2%
2%
4%
2%
2%
8%
Porous Asphalt
2%
2%
2%
6%
4%
6%
8%
14%
10%
10%
6%
6%
4%
6%
4%
4%
6%
4%
10%
2%
6%
2%
2%
6%
2%
2%
2%
CHANGE IN SURFACE TYPE
4%
2%
8%
4%
2%
Dense Asphalt
2%
2%
2%
2%
2%
2%
6%
6%
4%
2%
8%
2%
2%
2%
7
Indication of Ravelling.
Make Statistics within same Surface Type (1)
Histogram RRMS
Typical Distributions
60
55
Dense Asphalt
Minor Ravelling
50
45
Percentage
40
Dense Asphalt
Extensive ravelling
35
30
Porous Asphalt
Extensive Stone loss
25
20
15
10
5
0
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
RMST Intervall (mm)
8
Indication of Ravelling.
Pre-test Road 1102
RMS 10m
väg 1102, sektion 1 (uu505b)
1.50
1.40
1.30
1.20
1.10
1.00
0.80
0.70
0.60
0.50
Right Wheel Average RMS
0.40
Middle Position Average RMS
50-percentile
0.30
90-percentile
0.20
Possible Ravelling
Moderate Ravelling
0.10
Severe Ravelling
4000
3950
3900
3850
3800
3750
3700
3650
3600
3550
3500
3450
3400
3350
3300
3250
3200
3150
3100
3050
3000
2950
2900
2850
2800
2750
2700
2650
2600
2550
2500
2450
2400
2350
2300
2250
2200
2150
2100
2050
0.00
2000
RRMS
0.90
Distance
9
Ravelling - Netherlands
Based on experience from 30 000 km survey over 6 years on the
secondary road network in Netherlands
Purpose: Establish a methodology that can identify surfaces to be
renewed within 2 years due to raveling.
Set-up
• Information from road A348 over the years 2007, 2008, 2009,
2010 and 2011
• Measurement system: RST29, 32 kHz point lasers (3).
• Texture measured in the middle (1) and the right wheel position
(2). In the years 2010 and 2011 the texture was also measured in
the left wheel track.
11
Methodology – frame work
• The 95-percentile RMS texture value from year 1 (new asphalt) is
reference.
• The change in texture is yearly monitored through survey.
• When the 95-percentile value has increased to “X” times
compared to reference, the surface will be indicated as open.
• When the 95-percentile value has increased to “Y” times to the
reference, the surface will be indicated as raveled and
recommended for resurfacing.
12
Average RMS texture
Average value of RMS texture.
Laser 1 in between wheel paths
Laser 2 in the right wheel path
95-percentile RMS texture
The distribution is narrow which
can be explained by the surface
type with the dominating stone
sizes of 5 mm.
13
Change in distribution over time
Texture in the middle of the lane (laser 1) seems to be more
open than the texture in the right wheel path.
The change over time is visible.
Increase in RMS texture 2007-2011
Laser 1: + 37.1%
Laser 2: + 35.8%
Figure: % of 5m sections with RMS texture over the 2007 year’s 95-percentile
15
Conclusions
• It seems possible to detect changes over time with RMS texture.
• It is possible to quantify this change and therefore possible to
use it for indication of raveling.
• For detection of raveling the number of measurement point
should be increased from 2-3 to 5 in order to cover also the
edges of the lane.
• Driving pattern of the survey vehicle is essential (driver
education).
16
Homogeneity as quality control
Examples
Poor texture/homogeneity
Proper texture/homogeneity
Purpose with a control of homogeneity
The quality will rise by using demands on surface structure and
homogeneity
Proper structure/texture
• Better friction
• Influence on fuel consumption, tire wear and noice
Proper homogeneity
• Increase possibility for achieving the desired service life
• Reduced risk for future stone loss and separations
• Reduced risk for plastic deformations
Present control methods. Non-destructive
DOR (Density On Run)
+ Good parameter (density)
+ Cover areas not lines
- Low speed (0.9 km/h)
Radar (GPR)
+
Parameter on variation in void
content
- Requires some cores samples
- Less good repeatability on short
sections
Thermal camera (FLIR)
Shows the asphalt temperature directly after paving
Sections with too low temperature are classified as risk sections
Texture
Makrotexture (wave lenght
0.5 – 50.0 mm)
Parameters: MPD (Mean
Profile Depth), RMS
Point lasers in three lines
Poor quality - example
Separation due to high binder content on the right hand side.
3.0
MPD VLeft
MPD M
Mid
MPD HRight
2.5
MPD (mm)
2.0
1.5
1.0
0.5
0.0
3480
3490
3500
3510
3520
Distans (m)
3530
3540
3550
3560
Correlation RMS Texture/density
Objekt 2
2400-3600 meter
Mätdrag Höger
2.4
Densitet (g/cm3)
2.35
-0.1
RRMS (mm)
-0.3
2.3
-0.5
-0.7
2.2
-0.9
2.15
-1.1
2.1
2.05
-1.3
2
-1.5
2400
2600
2800
3000
Distans (m)
3200
3400
3600
mm
g/cm3
2.25
Model
• Collect data over 100 mm
• Calculate distribution for each 5 m
• Determine deviation from normal distribution
100
Black
90
Normal distribution for
the surface type
80
70
Blue
Dense surface, binder on
the surface
(%)
60
50
40
30
20
Red
Open surface, risk for
ravelling
10
0
0.0
0.5
1.0
1.5
2.0
Textur (mm)
2.5
3.0
3.5
Conclusions - homogeneity
• Surface texture can be a proper indicator of open and dense
suraces (quality)
• Acceptable correlation between DOR and laser survey
• Knowledge of expected values for different surface types is
needed
Further work
• Determine ”master
curve” for various
asphalt types
100
90
80
70
60
(%)
• Define how
deviations (grey
area) shall be
determined
50
40
30
• How large deviations
can be accepted?
20
10
0
0.0
0.5
1.0
2.0
1.5
Textur (mm)
2.5
3.0
3.5
Conclusions
• Point lasers can be used for indication of ravelling/stone loss and
homogeneity (MPD under implementation in Sweden).
• The method should be based on macro texture and statistical
distributions
• Changes over time are essential to track
• Reference data for new asphalt - nice to have “mastercurves”
• At least 5 lasers should be used
• Driving pattern of the survey vehicle is essential (driver
education).
27
Thank you!
28