Investigation of Field Rut Depth of Asphalt Pavements
Using Hamburg Wheel Tracking Test
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Weiguang Zhang, A.M.ASCE 1; Xiao Chen 2; Shihui Shen, A.M.ASCE 3; Louay. N. Mohammad, F.ASCE 4;
Bingyan Cui 5; Shenghua Wu, A.M.ASCE 6; and Ali Raza Khan 7
Abstract: This paper characterized field rutting performance of asphalt pavement based on Hamburg wheel tracking (HWT) rut depth. The
rut depths were collected from 50 field pavement sections, and cores from the same test areas were obtained to conduct volumetric properties
measurement and HWT test. The relationship between field measurements and HWT rut depth was evaluated; the ranking of HWT results and
field rut depth among mixtures was also compared. An analysis of if the HWT rut depth underpredicted or overpredicted field rut depth, or
they were equivalent was summarized. A field rut depth predictive model that consisted of HWT rut depth was developed. Results indicated
that the HWT rut depth magnitudes were closer to field rut depth if polymer modification was adopted. The rutting observed in the field was
minor compared to what was observed with the laboratory HWT test results for the majority of evaluated pavement sections. Ranking analysis
showed that applying HWT results at the end of the test did not provide a strong comparison in contrast to the field rut depth ranking among
mixtures. The field rut depth predictive model was developed based on the random forest algorithm, which included four input parameters,
namely, HWT rut depth, pavement age, number of high-temperature hours, and annual average daily truck traffic (AADTT). The model was
able to accurately predict field rut depth based on the relatively high coefficient of determination (R2 ¼ 0.79) and low standard error of the
esitimate (SEE ¼ 0.58). The sensitivity analysis indicated that pavement age has the most significant effect on rut depth, followed by HWT
rut depth and AADTT. DOI: 10.1061/JPEODX.0000250. © 2020 American Society of Civil Engineers.
Author keywords: Hamburg wheel tracking (HWT) test; Field rutting performance; Ranking analysis; Random forest algorithm; Predictive
model; Sensitivity analysis.
Introduction
The Hamburg wheel tracking (HWT) test is widely utilized by
DOTs and local agencies as an accelerated test to prevent construction with mixtures that are at risk for rutting and moisture
damage. A couple of DOTs are adopting HWT test as acceptance
criteria of asphalt mixture design (Cooper et al. 2014; Nemati
et al. 2020). When adopting HWT test as a performance test, local
1
Associate Professor, School of Transportation Engineering, Southeast
Univ., Southeast University Rd. #2, Nanjing 211189, China (corresponding
author). Email: wgzhang@seu.edu.cn
2
Research Assistant, School of Transportation Engineering, Southeast
Univ., Southeast University Rd. #2, Nanjing 211189, China. ORCID:
https://orcid.org/0000-0003-4073-6170. Email: xche0051@163.com
3
Professor, Rail Trasnportation Engineering, Pennsylvania State Univ.,
Altoona, PA 16601. Email: szs20@psu.edu
4
Irma Louise Rush Stewart Distinguished Professor, Dept. of Civil and
Environmental Engineering, Louisiana State Univ., Baton Rouge, LA
70803. Email: louaym@lsu.edu
5
Research Assitant, School of Transportation Engineering, Southeast
Univ., Southeast University Rd. #2, Nanjing 211189, China. Email:
cuibingyan0111@163.com
6
Assistant Professor, Dept. of Civil, Coastal, and Environmental Engineering, Univ. of South Alabama, Mobile, AL 36695. ORCID: https://
orcid.org/0000-0001-9625-4520. Email: shenghuawu@southalabama.edu
7
Research Assistant, School of Transportation Engineering, Southeast
Univ., Southeast University Rd. #2, Nanjing 211189, China. Email: engr
.khan30@gmail.com
Note. This manuscript was submitted on November 27, 2019; approved
on September 8, 2020; published online on December 11, 2020. Discussion
period open until May 11, 2021; separate discussions must be submitted for
individual papers. This paper is part of the Journal of Transportation
Engineering, Part B: Pavements, © ASCE, ISSN 2573-5438.
© ASCE
DOTs usually require a minimum number of passes at 12.7 mm
(0.5 in:), and such requirement varies according to the performance grade (PG) of asphalt binder (e.g., California, Illinois,
Louisiana), mix type (e.g., Texas), specimen type (e.g., plantmixed laboratory-compacted versus laboratory-mixed laboratorycompacted, Montana), or design equivalent standard axle load
(ESAL) (e.g., Virginia, Oklahoma). The application of HWT and
other wheel tracking devices were reported to select proper mixtures with good rutting resistance based on the assumption that
higher HWT rut depth corresponds to higher field rut depth measurements, which was also the criteria to select a mix design with superior rutting resistance. However, it is still unconfirmed that the HWT
rut depth can correlate well with field rut depth, or can differentiate
rutting resistance of mixtures at the end of testing.
Some researchers reported that the HWT measured rut depth in
the laboratory matched well with field rut depth, whereas in most
cases such good correlation was observed with preconditions; for
instance, cylindrical samples compacted to 4% air voids results in
rut depths that were more closely related to accelerated loading
facility (ALF) rut depth (Kandhal and Cooley 2003; Zhang et al.
2018c), in contrast to specimens that were compacted to 7%. Other
researchers realized that when the laboratory specimens were prepared at the same initial air void content as achieved in the field, the
laboratory testing results were better correlated with field rutting
performance (Radhakrishnan et al. 2019). It was also found that
when constructing the relationship between HWT test results and
field rut depth, the laboratory-compacted specimens typically resulted in 33% less rut depth than field-compacted specimens based
on HWT results (Mohammad et al. 2016).
However, many other researchers have realized that the correlation between laboratory HWT test results and field rut depth observation was very limited, even using field cores from the same
04020091-1
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field pavement sections (Lu and Harvey 2006; Yildirim and
Kennedy 2001; Zou et al. 2017). The discrepancy between HWTtested and field-measured rut depths could have a couple of causes,
such as the HWT test not considering the effect of support conditions from bottom layers (i.e., base and subgrade layers). Additionally, aging could have biased the HWT test outcome and made it
impossible to correlate successfully with field performance. Third,
by testing a field core, the effects of the Hamburg test on top surface
pavement sections may have been affected by a lower, older lift
of mix. Such discrepancy may increase if the specimens were
not damaged after 20,000 passes (Yildirim and Kennedy 2001) or
when the pavements were located in regions with high temperatures
(Ma et al. 2017; Sun et al. 2017; Zou et al. 2017; Liu et al. 2018a).
Furthermore, the HWT test was reported to be sensitive to asphalt binder and mixture based on laboratory test. It was found that
the HWT results were sensitive to asphalt binder PG, volumetric
properties, and binder modification (Grebenschikov and Prozzi
2011; Rahman and Hossain 2014; Topal et al. 2017). Typically,
lower HWT rut depth was observed with higher binder PG, lower
asphalt content, and the addition of organic additive. For a specific
mix type, an increase of asphalt content from 5.6% to 6.0% could
increase rut depth by 67%. Similarly, under the optimum binder
content, rutting with the HWT was found to increase by 50% when
the mix moved from a finer gradation to a target gradation within
the tolerance limits (Grebenschikov and Prozzi 2011). By evaluating the rutting performance of both laboratory-fabricated specimens and field cores using the HWT test, it was found that the
HWT test may overestimate the performance of mixes containing
conventional binders and understate the performance of mixes containing polymer-modified binders (Lu and Harvey 2006).
A literature review indicated that the correlation between the
HWT-tested and field-measured rut depth was neither conclusive
nor sufficiently studied; most studies were either based on laboratory evaluation or short-term field performance, or the number of
evaluated pavements was limited. There exists a gap in understanding direct correlation between HWT rut depth and field rut depth
from the view of long-term field performance. Also, if the mixtures
that had varied HWT rutting resistance represented the same field
rut depth, the ranking was not validated. Thus, the research objectives of this study were to characterize long-term field rutting performance using HWT results, as well as to determine if the ranking
of HWT results was comparable to the ranking of field rut depth
among different mixtures.
Methology and Scope
The rut depth of asphalt pavement and mixture properties presented
in this study were collected during the National Cooperative Highway Research Program (NCHRP 9-49A) study in 2014 and 2015,
as detailed elsewhere (Shen et al. 2016; Zhang et al. 2018a, b). Fifty
pavement sections from 21 projects with observed field performance
were selected, representing different mix designs, volumetric properties, pavement ages, recycled asphalt pavement (RAP) contents, traffic levels, asphalt modifications, structural thicknesses, and climatic
zones across the United States. Rut depth values were measured for
both inside and outside wheel paths and were averaged. Field cores
were taken outside of the trafficked wheel paths (i.e., non-wheel path
areas) to avoid inclusion of any damage (i.e., crack or segregation).
When shipped back to the laboratory, the field cores were cut and
trimmed to 150-mm diameter and 50-mm height to fit the mold
geometries of HWT. These field cores were first checked on volumetric properties, and were further dried by a fan overnight before
conducting the HWT test.
© ASCE
Among the 21 projects evaluated, four (Montana I-15, Tennessee SR 125, Iowa US 34, and Louisiana US 61) consisted of three
types of specimen: plant-mixed and laboratory-compacted specimen; the first-round cores, which were taken shortly after the construction was completed; and the second-round field cores, which
were drilled 2–3 years after pavement overlaying. The plant-mixed
and laboratory-compacted specimen indicates the gyratory compacted specimen that utilized loose mixtures that were collected
right after paving during field construction. In the laboratory, all
the plant-mixed and laboratory-compacted specimens were fabricated to target air voids of 7% 0.5%. For the other 17 projects,
only one round of field cores was obtained during the field distress
survey process. These 17 projects were constructed between 2005
and 2010, and had been in service for 4–9 years when the field cores
were taken, which indicates that the field cores had experienced
long-term field aging to varied extents. Because detailed background
information about the 17 projects was not available in many cases,
the selection of newly constructed projects (Montana I-15, Tennessee
SR 125, Iowa US 34, and Louisiana US 61) could compensate for
this limitation. Each project had one hot mix asphalt (HMA) as the
control pavement and one or more warm mix asphalt (WMA) pavements (e.g., Sasobit, Evotherm, foaming).
The comparison between HWT-tested and field-measured rut
depth was conducted. A statistical analysis was adopted to evaluate
if HWT rut depth was underpredicted, equal to, or overpredicted the
field rut depth. A ranking analysis was also applied to determine if
the HWT test sensitivity was comparable to the field rut depth difference among mixtures. Every 1-mm rut depth that was created by the
HWT test in the laboratory and the truck traffic [in terms of annual
average daily truck traffic (AADTT)] in the field was also compared.
Finally, a predictive model of field rut depth was developed, taking
the following factors as the potential predictive variables: HWT rut
depth and volumetric properties of field cores, pavement age, number of high-temperature hours, solar radiation, relative humidity,
AADTT, moduli of base layer and subgrade layer, and pavement
structural thickness. The developed and validated model used only
some of these parameters that were identified based on a statistical
machine learning method aided by engineering judgment.
The machine learning method adopted for the development of
the predictive model was called random forest, which is an ensemble machine learning algorithm for classification and regression. It
first constructed a set of homogenous regression decision trees on
randomized samples. Based on input variables, every single tree
will provide a prediction. The output of random forest is the average of all the trees. Some data might be utilized multiple times,
whereas others might never be selected for training purposes
(Rodriguez-Galiano et al. 2015). The remaining data constitute
the out-of-bag subset, which is utilized to evaluate the accuracy
of regression training independently. Random forest uses bagging
(bootstrap aggregating) to develop multiple trees (models) to improve classification or prediction. The advantage of random forest
is the ability to deal with a large number of input variables with a
small number of responses and missing observations (Liu et al.
2018b). As a result, all attributes will have the opportunity to be
used in a number of trees and contribute to the model, thus adding
to its accuracy and stability (Alipour et al. 2017).
Data Collection
Table 1 summarizes a field project, and more information can be
found elsewhere (Shen et al. 2016; Zhang et al. 2018b). As can be
seen, the projects were located in three climatic zones: dry freeze,
wet no freeze, and wet freeze. Among all the pavement sections,
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J. Transp. Eng., Part B: Pavements
Variables
Project
04020091-3
Colorado I-70
Illinois 147
Lousiana 3121
Louisiana 3191
Louisiana 116
Maryland 925
Missouri Hall
Missouri Route CC
Nebraska 14
Nevada
Ohio SR 541
Pennsylvania 2006
Pennsylvania 2012
South Carolina 178
Virginia I-66
Washington I-90
Washington SR12
Montana I-15
Tennessee SR 125
Iowa US 34
Louisiana US 61
Pavement
age (months)
Climatic
zone
Construction/sampling
time
Traffic
(AADT, vehicles/day)
NMAS
(mm)
Asphalt
PG (°C)
Asphalt
content (%)
RAP
(%)
In-place air voids
(min–max) (%)
VMA
(%)
86
52
74
74
62
110
101
84.5
72
49
97
66
59
86
52
53.5
53.5
0, 24b
0, 36b
0, 24b
0, 24b
Dry freeze
Wet freeze
Wet no freeze
Wet no freeze
Wet no freeze
Wet freeze
Wet freeze
Wet freeze
Dry freeze
Dry freeze
Wet freeze
Wet freeze
Wet freeze
Wet no freeze
Wet freeze
Dry freeze
Dry freeze
Dry freeze
Wet no freeze
Wet no freeze
Wet no freeze
July–August 2007/October 2012
June 2010/July 2012
March 2009/May 2013
November 2008/May 2013
March 2010/May 2013
September 2005/June 2012
May 2006/July 2012
2007a/July 2012
2008a/October 2012
August 2010/October 2012
May–June 2009/June 2012
May 2009/June 2012
May–June 2009/June 2012
September 2007/July 2012
July 2010/June 2012
June 2008/August 2012
April 2010/August 2012
August 2011/August 2011
September 2011/October 2011
October 2011/September 2011
May 2012/September 2012
30,000
775
ADT 400
ADT 200
2,600
10,480
21,000
8,618
2,140
5,000
254
523
254
3,880
57,000
13,000
6,550
3,170
3,470
6,450
34,138
12.7
12.7
12.7
12.7
12.7
9.5
12.7
12.7
12.7
12.7
9.5
9.5
9.5
9.5
12.7
12.7
12.7
19.0
12.7
12.7
12.7
58-28
64-22
70-22
70-22
70-22
64-22
70-22
64-22
64-28
64-28
70-22
64-22
64-22
64-22
76-22
76-28
64-28
70-28
70-22
58-22
76-22
6.3
5.0
5.1
5.2
4.4
5.0
5.3
5.4
5.0
4.6
6.1
6.0
5.9
5.0
5.0
5.5
5.2
4.6
6.0
5.4
4.7
None
10
15
15
15
15
10
20
<15
15
15
None
None
None
None
15–20
20
None
10
17
15
3.0–3.6
6.4–8.4
4.8–5.9
3.0–4.8
5.4–6.7
6.0–6.1
3.1–5.1
4.7–4.9
7.1–9.1
2.2–2.9
4.7–6.7
5.1–8.8
5.3–7.9
6.9–7.7
4.7–5.6
3.0–5.0
1.8–3.1
5.7–6.0
6.1–8.0
5.7–9.0
4.8–7.0
16.2–16.8
18.5–20.1
13.5–13.9
14.5–15.4
13.8–14.6
14.8–15.1
14.2–15.4
14.5–14.6
16.4–18.2
15.2–15.8
16.8–18.8
17.5–20.6
18.4–20.6
15.2–15.9
14.8–16.1
13.5
14.7
13.2–16.4
19.3–21.2
15.8–17.1
14.6–17.0
Note: NMAS = nominal maximum aggregate size; VMA = voids in mineral aggregate; AADT = average annual daily traffic; and ADT = average daily traffic.
a
Only year is available.
b
The field cores were taken twice, right after construction, and 2–3 years after the roads were open to traffic.
J. Transp. Eng., Part B: Pavements
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© ASCE
Table 1. Field project information
J. Transp. Eng., Part B: Pavements, 2021, 147(1): 04020091
1=3 were interstate highways and 2=3 were state routes or local
roads.
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HWT Test
The HWT test was conducted on field cores because they matched
well with field test sections in terms of compaction pattern, mix
uniformity, in-place air voids, aging history, and moisture effect.
Regarding the four projects Montana I-15, Tennessee SR 125,
Iowa US 34, and Louisiana US 61, the plant-mixed laboratorycompacted specimens were also tested. For each pavement section,
four specimens were tested simultaneously using an HWT device in
accordance with AASHTO T 324 (AASHTO 2019), and results
were averaged for further analysis. The test determined rutting
characteristics by rolling a steel wheel repeatedly at loading rate
of 52 2 passes per minute for 20,000 passes across the surface
of an asphalt concrete specimen that was submerged under water at
50°C. The constant testing temperature of HWT was initially proposed to better compare rutting resistance of mixtures and to eliminate the effect of varied testing temperautres. The test continued
until 20,000 cycles or 20-mm deformation, whichever was reached
first. A total of 11 measurements (with an accuracy of 0.01 mm)
were taken along the length of the two specimens, the two outermost measurements were discarded and the innermost was shared
between the two specimens, as presented in Fig. 1(a). Most cores
composed of two layers (overlay and the asphalt layer below) and
the HWT rut depths represent the values for both layers. This to
some extent matched with field condition because the field measurements included rut from all the asphalt concrete layers as well.
Field Rut Depth Survey
Field rut depth was manually measured within three 61-m sections
for all the selected pavements every 15 m and was calculated according to the algorithm shown in Fig. 1(b). The measurement procedure
strictly followed the distress identification manual used for the LongTerm Pavement Performance (LTPP) program (Miller and Bellinger
2014). Chip seal was applied in project Montana I-15 1 year after
construction and thus rut depths were recorded as zero. Regarding
the in-service projects, the rut depth measurements were conducted
in 2014 and 2015; by then the pavement projects had been in service
for 4–9 years. Fig. 2 summarizes all the HWT-tested and fieldmeasured rut depths of the in-service pavement projects, divided
based on climatic zones: wet freeze, dry freeze, and wet no freeze.
Other Data
Other data were also collected including mix type, climatic areas,
month of aging, pavement structural thickness, volumetric properties of field core, high-temperature PG, AADTT, and falling weight
deflectometer (FWD) moduli of base and subgrade layers. The
bulk specific gravity (Gmb ), the theoretical maximum specific gravity
(Gmm ), and the in-place air voids were conducted following
AASHTO T 166 (AASHTO 2016), AASHTO T 209 (AASHTO
2020), and AASHTO T 269 (AASHTO 2014), respectively. Hightemperature PG and AADTT were obtained from the job mix formula. The thickness of each pavement layer was obtained from
the local DOT’s pavement management system (PMS) and was further validated by field cores. Climatic data, such as accumulated
high-temperature hours, solar radiation, and relative humidity, were
obtained from the LTPP website because they may affect the magnitude of the field rut depth (Zhang et al. 2017a). The FWD test was
conducted following the LTPP test protocol. The measured deflections were utilized to calculate the on-site modulus of base and subgrade layers using program Modcom V6 (Shen et al. 2017).
Results Analysis
Comparing HWT Results and Field Rut Depth
Fig. 3 compares the field rut depth and HWT rut depth for the four
newly constructed pavements, Montana I-15, Tennessee SR 125,
Iowa US 34, and Louisiana US 61. Three types of specimens were
utilized for the HWT test, including plant-mixed laboratorycompacted specimen, the first-round field cores, and the secondround field cores. For the Montana I-15 project, chip seals were
placed on the pavement surface after 1 year of service for the purpose of protecting the overlay (because severe thermal cracks were
observed), and thus no field rut depth was observed during the field
distress survey. A statistical t-test with a significance level of 0.05
was conducted to check if there was any statistical difference between HWT-tested and field-measured rut depths. The following
observations can be summarized based on the analysis:
• Polymer modification showed a clear effect on the comparison
between HWT-tested and field-measured rut depths. It is seen
that in the Iowa US 34 project in which no binder modification
was applied, although extremely high HWT rut depth was observed, the field rut depths were within acceptable limits (less
than 3 mm) for all three evaluated mixtures: control HMA,
WMA Sasobit, and WMA Evotherm. Such disagreement could
be caused by the fact that the field pavement sections were
located in a cold region with a low magnitude of truck trafficking, whereas the HWT test was conducted at a relatively high
temperature and standard wheel pass number, which did not fully
represent the field service condition. In contrast, the three projects
(Montana I-15, Tennesse SR 125, Louisiana US 61) that adopted
polymer modification did not present dramatic differences between HWT-tested and field-measured rut depth. Thus, selecting
test temperatures and wheel path pattern (i.e., number of passes,
Fig. 1. (a) Points at which HWT deformation was measured; and (b) field rut depth measurement algorithm.
© ASCE
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Fig. 2. HWT-tested and field-measured rut depth of in-service pavement sections: (a) HWT result in dry freeze area; (b) HWT result in wet freeze
area; (c) HWT result in wet nonfreeze area; (d) field rut depth in dry freeze area; (e) field rut depth in wet freeze area; and (f) field rut depth in wet
nonfreeze area.
load magnitude) of the HWT test based on binder modification,
project location, and trafficking level, instead of using a single
high temperature and load level, would be more reasonable to
improve agreement between laboratory HWT-tested and fieldmeasured rut depths.
• The aging influence on HWT test results was different within varied climatic zones. For Iowa US 34 and Montana I-15 in freeze
regions, the second-round field cores showed equivalent or higher
magnitude of rut depth compared to their companions from the
first-round cores. All the cores were taken within a non-wheel
path area and the aging effect on material properties was actually
observed elsewhere such as increased dynamic modulus, whereas
© ASCE
the reduced rutting resistance of the second-round field cores
could be caused by the freeze climate, which needs further validation. In contrast, for Tennessee SR 125 and Louisiana US 61
located in nonfreeze regions, the second-round field cores always
showed statistically lower magnitude of HWT rut depth compared to their companions from the first-round core, which reflected obvious influence of climatic aging.
For the other 17 pavement projects, only one round of field cores
was taken and tested by the HWT device. Fig. 4 summarizes the
statistical comparisons between HWT-tested and field-measured
rut depth. Underpredicted indicates that the HWT rut depth was
statistically lower than the corresponding field measurements;
04020091-5
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J. Transp. Eng., Part B: Pavements
Test Cycle at Which HWT Rut Depth Equals to
Field Rut Depth
Fig. 3. HWT-tested and field-measured rut depth of newly constructed
pavements.
40
Number of Pavements
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equal indicates the HWT rut depth was not statistically different
from the field measurements; and overpredicted indicates that
HWT rut depth was statistically higher than that of field measurements. It is shown that at the termination test cycle, the HWT rut
depth generally overpredicted field rut depth. Such a trend was
within expectations because, as an accelerated test, the testing conditions of HWT were more critical than field environmental and
trafficking conditions; for example, the constant testing temperature of 50°C without rest period was not typically seen in the field
roads.
37
30
20
10
6
7
0
Under-predicted
Equal
Over-Predicted
Fig. 4. Number of pavements that show if HWT rut depth underpredicted, was equivalent to, or overpredicted field rut depth.
Fig. 5 summarizes the test cycle at which the HWT rut depth was
equivalent to the field rut depth. As discussed previously, the overpredicted cases accounted for the majority of the pavement sections, and all their corresponding HWT results equaled the field
rut depth at a low number of passes (less than 5,000 cycles), among
which 22 pavements showed that HWT results equaled to the field
rut depth at 500 cycles or less, representing that the rut depth observed in the filed generally was minor compared to that tested in
the laboratory. Such a low number of test cycles at which HWT rut
depth equals the field rut depth also identified a clear issue: the
progress pattern of HWT rut depth was difficult to validate by
the field measurements, and it was difficult to use the laboratory
HWT results to differentate the rutting resistance of varied mixes
in the field.
Table 2 summarizes the wheel pass/mm (in terms of HWT test),
AADTT/mm (in terms of field measurements), and AADTT/mm
over wheelpath/mm (noted as AADTT/wheelpath) of each inservice pavement. AADTT/wheelpath greater than 1 indicated that
compared to the HWT test cycle, a larger AADTT was required in
the field to produce 1-mm rut depth; similarly, AADTT/wheelpath
less than 1 indicated that compared to HWT test cycle, a lower
AADTT was required in the field to produce 1-mm rut depth. However, results in the table indicate that the AADTT/wheelpath was
distributed in a wide range (from 0.01 to 5,011), with an average
value of 165, and no clear trend was observed.
Fig. 5. Test cycle of HWT at which HWT rut depth is equivalent to field rut depth.
© ASCE
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J. Transp. Eng., Part B: Pavements
Table 2. Field AADTT/mm over HWT wheel pass/mm for each pavement
section
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Project/mix type
Colorado I-70/HMA
Colorado I-70/organic
Colorado I-70/chemical
Colorado I-70/foaming
Illinois 147/HMA
Illinois 147/foaming
Louisiana 116/foaming
Louisiana 116/HMA
Louisiana 3121/HMA
Louisiana 3121/chemical
Louisiana 3121/chemical
Louisiana 3191/HMA
Louisiana 3191/foaming
Maryland 925/HMA
Maryland 925/organic
Missouri Hall St./HMA
Missouri Hall St./organic
Missouri Hall St./chemical
Missouri Hall St./foaming
Missouri Route CC/HMA
Missouri Route CC/chemical
Nebraska 14/HMA
Nebraska 14/chemical
Nebraska 14/HMA
Nevada/HMA
Nevada/foaming
Ohio SR 541/HMA
Ohio SR 541/organic
Ohio SR 541/chemical
Ohio SR 541/foaming
Pennsylvania SR 2006/HMA
Pennsylvania SR 2006/foaming
Pennsylvania SR 2006/organic
Pennsylvania SR 2012/HMA
Pennsylvania SR 2012/foaming
Pennsylvania SR 2012/foaming
South Carolina US 178/HMA
South Carolina US 178/chemical
Tennessee SR 46/HMA
Tennessee SR 46/HMA
Tennessee SR 46/organic
Tennessee SR 46/chemical
Tennessee SR 46/foaming
Tennessee SR 46/foaming
Virginia I-66/HMA
Virginia I-66/foaming
Washington I-90/HMA
Washington I-90/organic
Washington SR 12/HMA
Washington SR 12/foaming
Wheel
pass/mm
AADTT/
mm
AADTT/
wheel pass
3,653
200
134
164
3
10
10
109
16
10
11
2,790
656
82
78
5,003
5,968
421
6,498
6,450
2,914
26
13
19
15
91
231
64
85
110
18
3
16
10
14
20
4,943
12,280
232
3,202
75
451
425
2
473
8
8,191
7,122
6,423
1,240
2,617
2,225
2,098
2,257
1,881
789
5,826
5,243
1,134
3,402
1,701
997
1,714
689
1,115
4,445
4,445
3,604
2,931
2,286
2,176
3,479
6,291
6,291
703
633
129
85
142
78
436
567
284
236
202
202
482
573
52
41
32
49
46
44
11,179
37,603
3,709
3,087
813
1,901
1
11
16
14
561
78
606
48
70
333
150
0.4
3
8
14
1
1
9
0.5
0.4
1
134
470
327
46
7
1
1
2
1
24
193
18
23
14
10
0.1
0.05
0.2
0.01
0.4
0.1
0.1
19
24
5,011
0.5
0.4
0.1
2
Note: Organic additives included Sasobit; chemical additives included
Evotherm and Rediset WMX; foaming additives included Astec
Double Barrel Green (DBG), AquaBlack, Advera, and Aspha-min
zeolite.
Correlation and Ranking Analysis
A direction correlation between HWT-tested and field-measured rut
depth was constructed based on two scenarios: including all 50 data
points, as well as dividing the data points based on climatic zones.
However, results show that all the R2 values between the HWT rut
depth and the field measurements were less than 0.5 and no strong
correlation could be identified.
© ASCE
Additionally, paired ranking analysis was conducted, which assumes the WMA and HMA can be analyzed in pairs because they
share similar traffic level, pavement structure, climate, and construction conditions (Wen et al. 2016). The analysis follows three
steps:
1. Compare HWT rut depth obtained from the laboratory for each
of the HMA-WMA pairs and determine a ranking—HMA >
WMA, HMA ¼ WMA, or HMA < WMA—based on a statistical t-test analysis with significance level of 0.05.
2. Compare the field rut depth for each of the HMA-WMA pairs
and obtain the field rut depth ranking—HMA > WMA, HMA ¼
WMA, or HMA < WMA—based on a statistical t-test analysis
with significance level of 0.05.
3. Determine the number of HMA-WMA pairs that exhibit consistent trends or rankings between the HWT rut depth and the field
measurements.
Results indicate that among all 50 pavements, only 20 showed
consistent rankings between the HWT rut depth and the field measurements, whereas the other pavements showed totally different
trends. One example was that based on the HWT results, the HMA
mixture experienced better rutting resistance than the WMA mixtures (HMA < WMA), whereas the field rut depth indicated that the
HMA mixture experienced either equivalent (HMA ¼ WMA) or
worse (HMA > WMA) rutting resistance compared to the WMA
mixtures. Such discrepancy would negatively affect the mix selection process and the pavement durability. In contrast, when conducting the ranking at the test cycle at which the HWT results
were equivalent to the field rut depth, the number of consistent
rankings increased to 30. Thus, differentiating mixtures’ rutting resistance by applying the HWT rut depth at the end of the test cycle
may not be comparable to the field ranking of rut depth. Such a
discrepancy can be reduced by selecting the proper number of
HWT test cycles for the ranking analysis. Actually, as reported elsewhere (Jiang et al. 2019; Ma et al. 2018), when the HWT rut depth
exceeds a threshold, which was almost never seen in field roads, the
HWT rut depth progress pattern and trends became difficult to
validate.
Model Development
As seen, a direct correlation between HWT rut depth and field rut
depth was usually difficult to achieve. This can be attributed to the
fact that the rutting resistance of asphalt pavement was impacted by
multiple factors such as traffic load, climatic conditions, structural
thickness, materials, or a combination (Chen et al. 2018; Dong
et al. 2020; Gong et al. 2018; Zhang et al. 2015, 2017b, 2019).
The laboratory HWT test alone could not take so many factors into
account. Thus, a statistical model was developed to establish relationship between HWT-tested and field-measured rut depth by incorporating one or several of the following factors: (1) the supporting
condition under asphalt layers, (2) pavement age, (3) pavement structural layer thickness, (4) truck traffic AADTT, (5) climatic condition,
and (6) volumetric properties of HWT test specimens. Other properties such as mixture dynamic modulus and binder G = sinðδÞ were
not included because they were more or less correlated with HWT
results or specimen volumetric properties, and, more importantly, the
effect of HWT rut depth on field measurements can be more effectively evaluated. The collected data and their range are presented in
Table 3.
A t-test was conducted on all the parameters in Table 3 based on
a significance level of 0.05, with null hypothesis that the parameters
were not significant to the predictive responses. The parameters
with p-values less than 0.05 are presented in Table 4, which means
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J. Transp. Eng., Part B: Pavements
Table 3. Predictor variables and their ranges considered in the predictive
model
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Predictor variable
HWT rut depth
Base modulus
Base thickness
Subgrade modulus
Pavement age
Overlay thickness
Total HMA thickness
In-place air voids of
HWT specimen
VMA of HWT specimen
Truck traffic
Number of hours
greater than 25°C
Solar radiation
Relative humidity
Unit
Range
mm
MPa
cm
MPa
month
cm
cm
%
1.2–26.0
9.1–1,379.0
3.8–33.0
46.6–780.8
10.0–108.0
2.0–7.6
5.1–36.3
1.8–9.1
%
AADTT
N/A
13.5–20.6
10.0–3,300.0
87.0–12,774.0
W=m2
%
1,561,271.0–11,510,218.0
587,472.0–4,327,727.0
Y ¼ 0.05X 1 þ 0.00001X 2 þ 0.0017X 3 þ 0.099X 4 − 1.2
Note: VMA = voids in mineral aggregate.
Table 4. p-values of predictive variables
p-value
Predictor variable
Pavement age in months
Number of hours greater than 25°C
AADTT
HWT rut depth
Overlay thickness
Solar radiation
using software R. The final predictive variable selected for the
rut depth model were pavement age (þ), number of hightemperature hours (þ), AADTT (þ), and HWT rut depth (þ),
and their p-values are given in Table 4. The sign in the parentheses
represents the relationship between the input variable and response.
For instance, a positive sign + indicates that higher input parameter
corresponded to higher field rut depth, and vice versa. Eq. (1)
presents the model for predicting the field rut depth using the selected variables. As shown, a lower field rut depth was expected to
correspond to lower values of pavement age, number of hightemperature hours, AADTT, and HWT rut depth
2.512 × 10−7
0.002
8.623 × 10−6
0.021
0.045
0.035
that the null hypothesis should be rejected, and the parameters in
the table significantly affected the predicted rut depths. However,
further check indicated that high correlation existed between solar
radiation and pavement age, as well as between overlay thickness
and HWT rut depth. Because high correlation between parameters
can lead to overfitting issues of the predictive model, the solar radiation and overlay thickness were eventually removed. The other
parameters in Table 3 had p-values greater than 0.05, and thus were
not significant factors to the predictive rut depth.
By considering all these input parameters and field rut depth
values, the statistical method random forest was adopted to identify
the parameters that mostly contributed to the field measurement
ð1Þ
where Y = field-measured rut depth (mm); X 1 = pavement age
(months); X 2 = number of hours greater than 25°C; X 3 = AADTT;
and X 4 = HWT rut depth (mm).
Fig. 6(a) shows the predictive quality based on Eq. (1) including
all the collected data points according to the three climatic zones.
As shown, generally the predictive results matched well with field
measurements with a relatively high coefficient of determination
(R2 ) of 0.79 and a low standard error of the estimate (SEE) of
0.58. The coefficient of determination is a statistical measurement
that examines how well observed responses are replicated by the
model based on the proportion of total variation of outcomes explained by the model. From the viewpoint of engineering, R2 of
0.79 indicates that the regression line does not miss any of the
points by very much, and the model is robust enough to predict
field rut depth accurately.
Fig. 6(b) shows the sensitivity analysis results of each input
parameter. The sensitivity analysis was conducted by adjusting
one parameter at a time (OAT) and maintaining other parameters’
base value. Here, the base value refers to the data points that were
actually measured in the field or in the laboratory. In addition to
base value, a 10% increase (1.1 × base value) and 10% decrease
(0.9 × base value) of base value were also applied and their corresponding predicted responses were calculated. As seen, the
changes in the amount of rut depth are acceptable with the variations (10%) of the pavement age, AADTT, number of hightemperature hours, and HWT rut depth. This also implies that
change of rut depth (in terms of slope) was more sensitive to pavement age, followed by HWT rut depth and AADTT. The number
of high-temperature hours had the least effect on the magnitude of
predictive values.
Fig. 6. (a) Predictive rut depth versus field rut depth; and (b) sensitivity analysis results.
© ASCE
04020091-8
J. Transp. Eng., Part B: Pavements, 2021, 147(1): 04020091
J. Transp. Eng., Part B: Pavements
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In general, Eq. (1) could be transferrable to other states because
the model was developed and validated by data points from a wide
range of climatic zones, traffic loadings, pavement structural types,
and material properties. However, local calibration is still necessary, especially for road types that were not considered in this paper, such as reversed asphalt pavement. Furthermore, the statistical
methods utilized in this paper also provide techniques to allow
users to identify the effect of variables on responses based on varied
scenarios. For instance, large or small sample size, with or without
collinear data, and different types of variables. The developed
framework can also be used to evaluate how much each input is
contributing to an output uncertainty.
indicated that pavement age has the most significant effect on
rut depth based on sensitivity analysis, followed by HWT rut
depth and AADTT. The number of high-temperature hours
has the least influence.
Data Availability Statement
Some or all data, models, or code that support the findings of this
study are available from the corresponding author upon reasonable
request, including the mix design data, the pavement structural
thickness, the back-calculated moduli of the field FWD test, and
the processed HWT rut depth and the field rut depth of each project
evaluated in this article.
Conclusions
This study evaluated the correlation between HWT rut depth and
field rut depth. The field rut depth data were collected and summarized from 50 field pavement sections, and field cores within the
same sections were drilled and tested in the laboratory to obtain
volumetric properties and HWT rut depth. Based on the collected
data, the relationship analysis and ranking analysis between HWTtested and field-measured rut depth were conducted for all the pavement sections. The test cycle at which the HWT rut depth equals the
field rut depth was also identified. Furthermore, a predictive model
of field rut depth was developed based on the statistical machine
learning method (random forest), with the sensitivity of each input
parameter to responses checked. Based on the data points and
analysis methods applied in this study, the following conclusions
can be drawn:
• For newly constructed pavement projects with three types of
specimen, the HWT test results indicate that the increased rutting resistance due to aging was more obvious in nonfreeze
zones than in freeze regions. The rutting resistance of field cores
from freeze zones may even be reduced with increased pavement age. It was noticed that polymer modification had a clear
effect on the comparison: the HWT results can better characterize field rut depth of asphalt pavements with polymer-modified
binders, whereas it generally understated the field rut depth of
pavements without polymer modification.
• Generally, the rutting observed in the field was minor in contrast
to what was observed with the laboratory HWT test results. The
test cycle at which the HWT rut depth equals the field rut depth
was less than 5,000 passes for the majority of pavement sections
evaluated, which indicated that the HWT rutting process was
difficult to validate by field rut depth. Selecting HWT test conditions based on binder modification, local climate, and trafficking level instead of applying a single temperature or load would
be useful in reducing such discrepancy.
• No direct correlation between HWT rut depth and field rut depth
was observed. Ranking analysis indicated that when differentiating mixtures’ rutting resistance by applying the HWT results,
selecting the proper number of HWT test cycles, instead of simply using the values at the end of the test cycle, would be more
appropriate to characterize the field rut ranking among mixtures.
• The HWT test alone may not be able to ensure good field rutting
resistance. Thus, the field rut depth predictive model that includes HWT rut depth was developed based on machine learning methodology. Pavement age, number of high-temperature
hours, AADTT, and HWT rut depth were identified as critical
input parameters for the prediction model. A lower field rut
depth value was expected to correlate with shorter pavement
age, lower number of high-temperature hours, lower AADTT,
and lower magnitude of HWT rut depth. The sensitivity analysis
© ASCE
Acknowledgments
The original data collection and experimental tests were sponsored
by the National Cooperative Highway Research Program (NCHRP)
09-49A. The authors would like to acknowledge and thank Dr. Ed
Harrigan of the NCHRP staff and panel members for their assistance.
Thanks also go to Braun Intertec, Inc., and Bloom Companies, LLC,
who conducted the field activities, and to partner universities and
highway agencies for their generous help. The authors also want
to thank the sponsorship of the CCCC First Highway Consultants
Co., Ltd. (Project No. 8521002546), Fundamental Research Funds
for the Central Universities (Project No. 3221002001C1), and the
Qilu Transportation Group (Grant No. 2018B51) for the data analysis conducted in this study. The authors confirm contribution to the
paper as follows: study conception and design: W. Zhang, X. Chen;
data collection: L. Mohammad, S. Shen, S. Wu; analysis and interpretation of results: A. Khan, B. Cui; draft manuscript preparation:
W. Zhang. All authors reviewed the results and approved the final
version of the manuscript.
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