Embedment Acceptance Testing for Chip Seals
Embedment Acceptance Testing for Chip Seals M. Emin Kutay, Ph.D., P.E. Associate Professor, Michigan State University, East Lansing, MI
Yogesh Kumbargeri Research Assistant, Michigan State University, East Lansing, MI
Ilker Boz, Ph.D. Research Associate , Michigan State University, East Lansing, MI Midwestern Pavement Preservation Partnership Traverse City, Michigan August 28th - 30th, 2017
Acknowledgements Larry Galehouse, National Center for Pavement Preservation (NCPP) Michigan Department of Transportation (MDOT)
“Development
of an Acceptance Test for Chip Seal Project” & “Establishing Percent Embedment Limits to Improve Chip Seal Performance”
Research Advisory Panel: Erin Chelotti, Robert Green, Andrew Bennett, Curtis Bleech, Thomas Hynes, Reza Zolfaghari, Tim Crook, Mark Polsdofer US Department of Transportation (USDOT) for the University Transportation Center for Highway Pavement Preservation (UTCHPP) 2
Today’s visit • An image-based acceptance test method for chip seal embedment • Percent Embedment • Aggregate Orientation • Binder Application Rate • Aggregate Application Rate
• This procedure can also be used as • A quality control measure for contractors and • A quality assurance tool for road agencies • An objective tool for forensic investigations • Future conflict resolutions
3
Introduction Embedment Depth & Distresses Hb
Hs
Aggregate Asphalt Binder / Emulsion residue
𝑻𝑻𝑻𝑻𝑻𝑻 𝒍𝒍𝒍𝒍𝒍𝒍
Aggregate Loss
𝑻𝑻𝑻𝑻𝑻𝑻 𝒉𝒉𝒉𝒉𝒉𝒉𝒉𝒉
𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷 𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬 % = where
𝐻𝐻𝑏𝑏 x 100 𝐻𝐻𝑠𝑠
𝑯𝑯𝒃𝒃 = 𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 𝑜𝑜𝑜𝑜 𝑡𝑡𝑡𝑡𝑡 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 𝑯𝑯𝒔𝒔 = 𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 𝑜𝑜𝑜𝑜 𝑡𝑡𝑡𝑡𝑡 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆
Bleeding / Flushing 4
Introduction; cont’d Current methods Sand patch test Laser scanning
Sand Patch Test
Laser Scan
5
Sand Patch Test
MTD VGS d
4 x VGS 𝑀𝑀𝑀𝑀𝑀𝑀 = π x d2
= mean texture depth = volume of glass beads = diameter of the sand patch at the surface 6
Sand Patch Test MTD Hs Hb Aggregate Asphalt Binder / Emulsion residue
𝐻𝐻𝑏𝑏 𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷 𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬 = x 100 Hs
Hb = Hs − MTD Hb Hs MTD
= Binder height = Aggregate height (average least dimension) = mean texture depth
7
Problems with Sand Patch
Assumes: • A single size aggregate • Compacted to the flattest side • No embedment of aggregates into substrate • No leakage of binder into the substrate cracks 8
Problems with Sand Patch Glass beads or sand used in sand patch method
(a)
Asphalt binder
Aggregate embedment into substrate
Substrate
(b)
Substrate surface profile
9
Methodology 1 - Sample preparation (a) Field coring
(b) Horizontal cutting
(c) Vertical slicing
(d) Core slices
10
Methodology 2 - Image acquisition (e) Image acquisition of the core slice
Desired image properties: - Top of the chip seal covered with a distinctly colored substance (e.g., playdough) - Good contrast between the aggregate and the binder - No light reflection on the binder or the aggregate
11
CIPS Software
θ (a)
(b)
12
Example raw image
13
Finding peaks and valleys
14
Identifying aggregates
15
Converting to black/white image
16
Finding PE – Peak/Valley method
17
Finding PE of each aggregate
18
Methodology
Image processing steps •
Peak & Valley Method
hs hb
𝑷𝑷𝒆𝒆 (%) =
𝒉𝒉𝒃𝒃 𝒙𝒙𝒙𝒙𝒙𝒙𝒙𝒙 𝒉𝒉𝒔𝒔
19
Methodology
Image processing steps •
Each-Aggregate Embedment Method
hb1 hs1
𝐏𝐏𝐏𝐏𝟏𝟏 (%) =
hb2 hs2
𝐏𝐏𝐏𝐏𝟐𝟐 (%) =
𝐡𝐡𝐛𝐛𝐛𝐛 𝐱𝐱𝐱𝐱𝐱𝐱𝐱𝐱 𝐡𝐡𝐬𝐬𝐬𝐬
hb3 hs3
𝐡𝐡𝐛𝐛𝟐𝟐 𝐱𝐱𝐱𝐱𝐱𝐱𝐱𝐱 𝐡𝐡𝐬𝐬𝟐𝟐 … 𝐡𝐡𝐛𝐛𝟑𝟑 𝐏𝐏𝐏𝐏𝟑𝟑 (%) = 𝐱𝐱𝐱𝐱𝐱𝐱𝐱𝐱 𝐡𝐡𝐬𝐬𝟑𝟑
𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏 𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄 % =
∑𝐧𝐧(𝐏𝐏𝐏𝐏)𝐧𝐧 𝐧𝐧 20
Methodology
Image processing steps •
Surface Coverage Method
AS =
∑i(
ABS i x100) APS i N
AS = aggregate surface coverage percentage ABS = aggregate perimeter/surface covered with binder APS = total aggregate perimeter/surface N = total number of aggregates
21
Verification of the algorithms (a) – Verification of Peak-Valley (PEPV) algorithm
(b) Verification of Each Aggregate (PEEA) algorithm
(c) Verification of Surface Coverage (PCEA) algorithm
22
Sand patch/laser vs image analysis
Inaccuracies of sand patch & laser Embedment of the cover aggregate into the substrate is ignored. Glass beads
Asphalt emulsion that penetrate into the cracks and voids are ignored. Glass beads
Binder
Binder Aggregate
Aggregate
Image based algorithms Aggregate embedment into the Penetration of emulsion is considered substrate is taken into consideration. for calculation of embedment.
Embedment of cover aggregate into the substrate
Penetration of asphalt emulsion 23
CIPS output for an image slice
24
Multiple slices: Percent Within Limits
Histogram for PE1:Peak Valley method
30
Number of samples
25 20 15 10 5 0
0
10
20
30
40
50
60
70
Percent Embedment
80
90
100 25
Percent Within Limits (PWL) results
26
Analysis of field cores from MI
Field section
Number of cores
M-57 near Pompeii M-20 near New Era M-33 from Alger to Rose City M-86 east of Plainwell M-43 in Woodland US-31 in Bear Lake M-57 near Clio (double chip seal) M-57 near Carson City Total number of cores
5 8 8 8 4 6 5 4 48 27
PEs computed using various methods Average Embedment Depth Aggregate surface Embedment Depth - Peak/valley coverage - Each Aggregate (PEPV) (PCEA) (PEEA) M-57 M-20 M-33 M-86 M-43 US-31
M-20 M-33 M-86 M-43 US-31
Laser Texture Scan (PELT)
53.2 51.0 81.9 56.7 69.5 63.1 60.3 78.2 56.8 72.3 70.5 61.3 79.8 65.9 72.5 67.2 64.7 81.5 76.4 77.1 79.0 84.3 91.1 43.5 53.1 65.8 54.3 73.9 83.3 83.0 Coefficient of Variation (COV) – Sample to sample variability Embedment Depth Aggregate surface Embedment Depth - Peak/valley coverage - Each Aggregate (PEPV) (PCEA) (PEEA)
M-57
Sand Patch Test (PESP)
11.1% 8.9% 8.2% 12.8% 15.5% 14.4%
3.6% 6.8% 5.1% 6.1% 15.1% 26.7%
3.2% 3.5% 4.1% 5.3% 7.1% 9.3%
Sand Patch Test (PESP)
Laser Texture Scan (PELT)
8.4% 3.7% 16.4% 8.9% 33.3% 7.8%
4.1% 3.9% 10.6% 6.4% 27.2% 6.1%
28
Aggregate loss versus PE
Aggregate loss, % (Sweep CAL, %test)
20
y = 275.4e-0.056x R² = 0.88
15
10
5 (e) 0 40
50
60
TPE, %
70
80
Percent Embedment 29
The End!
30
PE Each Aggregate Method
Examples of fully embedded aggregates:
Fully embedded aggregates
31
Yogesh Kumbargeri Research Assistant, Michigan State University, East Lansing, MI
Ilker Boz, Ph.D. Research Associate , Michigan State University, East Lansing, MI Midwestern Pavement Preservation Partnership Traverse City, Michigan August 28th - 30th, 2017
Acknowledgements Larry Galehouse, National Center for Pavement Preservation (NCPP) Michigan Department of Transportation (MDOT)
“Development
of an Acceptance Test for Chip Seal Project” & “Establishing Percent Embedment Limits to Improve Chip Seal Performance”
Research Advisory Panel: Erin Chelotti, Robert Green, Andrew Bennett, Curtis Bleech, Thomas Hynes, Reza Zolfaghari, Tim Crook, Mark Polsdofer US Department of Transportation (USDOT) for the University Transportation Center for Highway Pavement Preservation (UTCHPP) 2
Today’s visit • An image-based acceptance test method for chip seal embedment • Percent Embedment • Aggregate Orientation • Binder Application Rate • Aggregate Application Rate
• This procedure can also be used as • A quality control measure for contractors and • A quality assurance tool for road agencies • An objective tool for forensic investigations • Future conflict resolutions
3
Introduction Embedment Depth & Distresses Hb
Hs
Aggregate Asphalt Binder / Emulsion residue
𝑻𝑻𝑻𝑻𝑻𝑻 𝒍𝒍𝒍𝒍𝒍𝒍
Aggregate Loss
𝑻𝑻𝑻𝑻𝑻𝑻 𝒉𝒉𝒉𝒉𝒉𝒉𝒉𝒉
𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷 𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬 % = where
𝐻𝐻𝑏𝑏 x 100 𝐻𝐻𝑠𝑠
𝑯𝑯𝒃𝒃 = 𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 𝑜𝑜𝑜𝑜 𝑡𝑡𝑡𝑡𝑡 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 𝑯𝑯𝒔𝒔 = 𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 𝑜𝑜𝑜𝑜 𝑡𝑡𝑡𝑡𝑡 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆
Bleeding / Flushing 4
Introduction; cont’d Current methods Sand patch test Laser scanning
Sand Patch Test
Laser Scan
5
Sand Patch Test
MTD VGS d
4 x VGS 𝑀𝑀𝑀𝑀𝑀𝑀 = π x d2
= mean texture depth = volume of glass beads = diameter of the sand patch at the surface 6
Sand Patch Test MTD Hs Hb Aggregate Asphalt Binder / Emulsion residue
𝐻𝐻𝑏𝑏 𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷 𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬 = x 100 Hs
Hb = Hs − MTD Hb Hs MTD
= Binder height = Aggregate height (average least dimension) = mean texture depth
7
Problems with Sand Patch
Assumes: • A single size aggregate • Compacted to the flattest side • No embedment of aggregates into substrate • No leakage of binder into the substrate cracks 8
Problems with Sand Patch Glass beads or sand used in sand patch method
(a)
Asphalt binder
Aggregate embedment into substrate
Substrate
(b)
Substrate surface profile
9
Methodology 1 - Sample preparation (a) Field coring
(b) Horizontal cutting
(c) Vertical slicing
(d) Core slices
10
Methodology 2 - Image acquisition (e) Image acquisition of the core slice
Desired image properties: - Top of the chip seal covered with a distinctly colored substance (e.g., playdough) - Good contrast between the aggregate and the binder - No light reflection on the binder or the aggregate
11
CIPS Software
θ (a)
(b)
12
Example raw image
13
Finding peaks and valleys
14
Identifying aggregates
15
Converting to black/white image
16
Finding PE – Peak/Valley method
17
Finding PE of each aggregate
18
Methodology
Image processing steps •
Peak & Valley Method
hs hb
𝑷𝑷𝒆𝒆 (%) =
𝒉𝒉𝒃𝒃 𝒙𝒙𝒙𝒙𝒙𝒙𝒙𝒙 𝒉𝒉𝒔𝒔
19
Methodology
Image processing steps •
Each-Aggregate Embedment Method
hb1 hs1
𝐏𝐏𝐏𝐏𝟏𝟏 (%) =
hb2 hs2
𝐏𝐏𝐏𝐏𝟐𝟐 (%) =
𝐡𝐡𝐛𝐛𝐛𝐛 𝐱𝐱𝐱𝐱𝐱𝐱𝐱𝐱 𝐡𝐡𝐬𝐬𝐬𝐬
hb3 hs3
𝐡𝐡𝐛𝐛𝟐𝟐 𝐱𝐱𝐱𝐱𝐱𝐱𝐱𝐱 𝐡𝐡𝐬𝐬𝟐𝟐 … 𝐡𝐡𝐛𝐛𝟑𝟑 𝐏𝐏𝐏𝐏𝟑𝟑 (%) = 𝐱𝐱𝐱𝐱𝐱𝐱𝐱𝐱 𝐡𝐡𝐬𝐬𝟑𝟑
𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏 𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄𝐄 % =
∑𝐧𝐧(𝐏𝐏𝐏𝐏)𝐧𝐧 𝐧𝐧 20
Methodology
Image processing steps •
Surface Coverage Method
AS =
∑i(
ABS i x100) APS i N
AS = aggregate surface coverage percentage ABS = aggregate perimeter/surface covered with binder APS = total aggregate perimeter/surface N = total number of aggregates
21
Verification of the algorithms (a) – Verification of Peak-Valley (PEPV) algorithm
(b) Verification of Each Aggregate (PEEA) algorithm
(c) Verification of Surface Coverage (PCEA) algorithm
22
Sand patch/laser vs image analysis
Inaccuracies of sand patch & laser Embedment of the cover aggregate into the substrate is ignored. Glass beads
Asphalt emulsion that penetrate into the cracks and voids are ignored. Glass beads
Binder
Binder Aggregate
Aggregate
Image based algorithms Aggregate embedment into the Penetration of emulsion is considered substrate is taken into consideration. for calculation of embedment.
Embedment of cover aggregate into the substrate
Penetration of asphalt emulsion 23
CIPS output for an image slice
24
Multiple slices: Percent Within Limits
Histogram for PE1:Peak Valley method
30
Number of samples
25 20 15 10 5 0
0
10
20
30
40
50
60
70
Percent Embedment
80
90
100 25
Percent Within Limits (PWL) results
26
Analysis of field cores from MI
Field section
Number of cores
M-57 near Pompeii M-20 near New Era M-33 from Alger to Rose City M-86 east of Plainwell M-43 in Woodland US-31 in Bear Lake M-57 near Clio (double chip seal) M-57 near Carson City Total number of cores
5 8 8 8 4 6 5 4 48 27
PEs computed using various methods Average Embedment Depth Aggregate surface Embedment Depth - Peak/valley coverage - Each Aggregate (PEPV) (PCEA) (PEEA) M-57 M-20 M-33 M-86 M-43 US-31
M-20 M-33 M-86 M-43 US-31
Laser Texture Scan (PELT)
53.2 51.0 81.9 56.7 69.5 63.1 60.3 78.2 56.8 72.3 70.5 61.3 79.8 65.9 72.5 67.2 64.7 81.5 76.4 77.1 79.0 84.3 91.1 43.5 53.1 65.8 54.3 73.9 83.3 83.0 Coefficient of Variation (COV) – Sample to sample variability Embedment Depth Aggregate surface Embedment Depth - Peak/valley coverage - Each Aggregate (PEPV) (PCEA) (PEEA)
M-57
Sand Patch Test (PESP)
11.1% 8.9% 8.2% 12.8% 15.5% 14.4%
3.6% 6.8% 5.1% 6.1% 15.1% 26.7%
3.2% 3.5% 4.1% 5.3% 7.1% 9.3%
Sand Patch Test (PESP)
Laser Texture Scan (PELT)
8.4% 3.7% 16.4% 8.9% 33.3% 7.8%
4.1% 3.9% 10.6% 6.4% 27.2% 6.1%
28
Aggregate loss versus PE
Aggregate loss, % (Sweep CAL, %test)
20
y = 275.4e-0.056x R² = 0.88
15
10
5 (e) 0 40
50
60
TPE, %
70
80
Percent Embedment 29
The End!
30
PE Each Aggregate Method
Examples of fully embedded aggregates:
Fully embedded aggregates
31
