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Evaluation of intelligent compaction for asphalt materials
Automation in Construction 30 (2013) 104–112
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Automation in Construction journal homepage: www.elsevier.com/locate/autcon
Evaluation of intelligent compaction for asphalt materials Qinwu Xu ⁎, George K. Chang The Transtec Group Inc., 6111 Balcones Dr., Austin, TX 78731, United States
a r t i c l e
i n f o
Article history: Accepted 7 November 2012 Available online 12 December 2012 Keywords: Intelligent Compaction Asphalt Moduli Density Geostatistical semivariogram Correlations
a b s t r a c t This paper aims to evaluate the effectiveness of IC technology for the asphalt compaction. A framework of field construction and in-situ test control using IC technology was presented and implemented in one engineering project. A computer-aided data analysis method was proposed and implemented, including the univariate and geo-statistics, compaction curve and uniformity. Results show that IC technology can effectively improve the roller patter to achieve more uniform compaction, and the compaction curve identifies the optimum roller pass to help avoid under/over compaction. The trend of Sakai Compaction Control Value (CCV) – a relative index of material stiffness – is consistent with CCV from mapping the underlying subbase layer. This indicates the reflecting effect of underneath layers on upper layers. CCVs from asphalt compaction have a linear relationship with the light-weight-deflectometer moduli of subbase. The compaction uniformity trends indicated by univariate statistics are consistent with that indicated by semivariograms for this case study. © 2012 Elsevier B.V. All rights reserved.
1. Introduction For the conventional compaction on roadway materials, achieving a desired density of asphalt materials has been used as the acceptance criteria for flexible pavement construction. Usually the in-situ spot test of material density is required for the quality control (QC) and assurance (QA). However, there are issues associated with the conventional density control [1–3]: 1) the spot tests are performed at limited locations, and therefore they cannot represent the entire construction area; 2) there may be some weak areas or unqualified compaction regions not identified by limited spot testing; 3) density as a material property does not necessarily indicate the structural capacity (e.g. stiffness and strength) of the pavement layer; and 4) measured density of the top asphalt layer does not represent the property of the entire pavement system, including that of the underlying layers, to guarantee the quality of the whole pavement system. Meanwhile, the conventional compaction method generally maintains roller passes manually by roller operators. However, this compaction effort may sometimes result in over-compacted or under-compacted pavement materials since the support and stiffness of materials maybe very different at different locations. As a result, premature distress of pavements may appear [1, 2], and will in turn result in worse long-term pavement performances and higher life-cycle costs. Intelligent construction systems have been studied in the past to improve the efficiency and safety, including the intelligent navigation
⁎ Corresponding author. Tel.: +1 512 451 6233; fax: +1 512 451 6234. E-mail addresses: [email protected] (Q. Xu), [email protected] (G.K. Chang). 0926-5805/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.autcon.2012.11.015
strategies [4], autonomous systems with artificial intelligence approaches [5], and the instrumented roller compactor system monitoring vibration behavior [6]. Other intelligent systems studied for the asphalt paving industries include that using the neural network modeling [7, 8], and the on-board density reading system [7, 9]. Intelligent Compaction (IC) technology has been developed to address issues existing in the conventional compaction and QC/QA methods, as used for road materials including soils, aggregates, and asphalt mixtures. IC uses accelerometer-based vibratory rollers, equipped with an in-situ measurement system and feedback controls [3, 10]. It includes two cores of the hardware system [1, 3]: 1) the accelerometer mounted to the vibratory roller that measures and records the roller vibration information such as harmonic frequencies and accelerations, as used to determine the IC measurement value (ICMV); and 2) the GPS system that tracks the roller pass and location in real time. The compaction operators manually, or the IC rollers automatically, adjust the compaction efforts in real-time during the compaction process in order to achieve a more uniform compaction. IC rollers maintain a continuous record of GPS-map-based color-coded plots that include the number of roller passes, ICMV, and locations of the roller, roller speed and vibration frequency, with 100-percentage converge of the entire compaction area in real time. IC rollers can also monitor the asphalt pavement surface temperature by the instrumented infrared thermometer, which provides essential information for characterizing HMA material properties. The IC technology uses ICMV as the primary index to evaluate the compaction effect, although ICMVs from various vendors are defined in different forms. ICMV is an index value correlated to the stiffness or modulus of the pavement layer system under compaction. With 100-percent coverage on the compacted area, ICMV aims to achieve a higher quality and uniformity of pavement
Q. Xu, G.K. Chang / Automation in Construction 30 (2013) 104–112
materials, which in turn better ensures the long-lasting performance. ICMV is determined from the real-time information of compaction efforts such as roller vibration, amplitudes, frequencies, and responses from the material being compacted that are monitored and recorded by the compaction accelerometers. ICMVs are recorded in real-time for each roller pass in order to identify the stiff or weak spots of the pavement layer under compaction, which provides necessary information for the operator or the machine itself to adjust the compaction effects in order to achieve a more uniform ICMV distribution. IC technologies have been implemented in Europe and Japan for years, and they were introduced to the USA in late 2000's [1, 11–13]. For the HMA compaction industry, the IC is a relatively new, still immature and evolving construction technology [1, 12]. There are still some challenging issues for HMA IC technology being studied, such as how to decouple the ICMV to the single layer of pavement including the top HMA layer, and how to quantify the HMA's temperature effect on ICMV [1–3]. Meanwhile, IC produces a large database even for each single project, and presented in various forms for different rollers of different manufacturers. An efficient and systematic data analysis method or tool would be in needs to help accelerate the implementation of IC technology for asphalt paving industry [14]. With the research needs, the objective of this research is to evaluate the effectiveness of IC technology on asphalt materials, under the Federal Highway Administration (FHWA)'s pooled funding project that targets to help realize the blueprint of the IC technology implementation in the US [1, 3]. The field construction and in-situ test control for asphalt materials compaction using IC technology was discussed and practiced. A computer-aided systematic data analysis method based on both the univariate and geostatistical methods are studied and implemented, in order to evaluate the effectiveness of IC technology for asphalt materials. One demonstration project under the FHWA's pooled fund IC study was used for validation and evaluation. 2. IC project background The IC demonstration project studied in this paper was performed on highway Route 4 in Minnesota, USA [3]. It is a new full-depth asphalt pavement project. The centerline length of the Route 4 was approximately 5.1 km (3.2 mi.), which was paved with 64 mm (2.5 in.) of HMA base course (containing 30% reclaimed asphalt pavement) and 38 mm (1.5 in.) of HMA wearing course (containing 20% reclaimed asphalt pavement) on the top of aggregate subbase, as shown in Fig. 1. The Sakai double drum IC roller, Model SW880, was used for these demonstration projects (Fig. 1). The roller is equipped with the Sakai Exact Compact Feature which controls the roller travel speed at the selected vibration frequencies of 2500, 3000, or 4000 vpm (vibrations per minute). The Compaction Information System (CIS) includes IC hardware and software that measure roller passes, temperatures, and stiffness of the compacted surface of pavement layers. The CIS is installed on a Sakai single-drum vibratory soil roller or double drum asphalt rollers. The ICMV for Sakai, Compaction Control Value (CCV), is a unitless index value relating to the compaction level and material stiffness [15]. Note that the CCV measures the relative stiffness of the multilayered pavement system with an influence depth, including the stiffness contributions from the underlying layers rather than a single layer under compaction. The concept behind the CCV is that as the ground stiffness increases, the roller drum starts to enter into a “jumping” motion, which results in vibration accelerations at various frequency components [15]. CCV is calculated by using the acceleration data of amplitudes (A) from the first sub-harmonic (0.5 Ω), fundamental frequency (Ω), and higher-order harmonics (1.5 Ω, 2 Ω, 2.5 Ω, 3 Ω) as presented in Eq. (1) [15]. The vibration acceleration signal from the accelerometer is transformed through the Fast Fourier Transform
105
38 mm HMA wearin
GPS receiver
64 mm HMA base Aggregate subbase
Soil
Infrared thermometer
Accelerometer
Fig. 1. Pavement structure and IC roller.
(FFT) method and then filtered through band pass filters to detect the acceleration amplitude spectrum [15]: CCV ¼
A0:5
Ω
þ A1:5
þ A2 Ω þ A2:5 A0:5Ω þ AΩ
Ω
Ω
þ A3
Ω
100:
ð1Þ
3. Methodologies 3.1. Framework of construction and in-situ test control Fig. 2 presents a framework for the construction and in-situ test control using IC technology for asphalt materials. Before the construction starts, the GPS base station was set up, which communicates with satellites and transmits signals to both the GPS receiver of IC roller and GPS hand rover. With the GPS base station, the real-time kinematic (RTK) GPS-system achieves a “fixed” precision of 1–3 cm, while without it the GPS system only achieves a “float” precision of meters to submeter [1, 3, 14]. Consequently, the IC roller GPS precision shall be verified before compaction starts, since all the compaction data collected later are accounted on the precise GPS records. Here a practical method is proposed as follows: take the GPS receiver (head) off the GPS hand rover and place on the top (head) of the roller GPS receiver, to assure that they have the same coordinates by measuring at least three spots with different distances and angles. The unit of meter rather than geographic degree–minute–second is recommended for data recording, since the later one is hard to differentiate the spots. After assuring the roller GPS accuracy, IC roller is ready to roll over and map the existing underlying layers prior to HMA paving to evaluate the existing pavement conditions. If the IC color-coded records do not come out in a right way, adjustments and re-set shall be conducted immediately. Consequently, in-situ spot tests on underlying layers are measured as used for correlation to IC measurements later. The GPS hand rover is then followed to measure the coordinates of each test spot. After everything is ready, paving starts, as followed by the compaction in the series of breakdown, intermediate, and then finishing compacting. When the IC roller moved forward, the spot tests of HMA density (e.g. nuclear gauge density) at random spots were measured for quality control. The GPS hand rover is again used to record coordinates of those random spots. After finishing the compaction of each test bed, the IC measurement data shall be extracted from the roller CIS and stored into a USB driver as soon before moving to the next test bed and resetting the machine. After the finishing roller passed, field cores were taken to measure core density for the QA purpose. For mapping the existing granular subbase layer, a roller vibration amplitude of 0.6 mm and frequencies of 2500 and 3000 vpm (vibration per minute) was used. During the mapping process, the front drum was in vibration mode while the rear drum was in static mode in order not
Q. Xu, G.K. Chang / Automation in Construction 30 (2013) 104–112
Before compaction
106
Roller GPS verification
Mapping existing layer
Paving & Compaction
GPS base station set up
After Compaction
Paving
Finishing Compaction
Intelligent Compaction
Density
LWD/FWD Spot in-situ test
Spot GPS shot
Download and store data
Fig. 2. Framework of construction and in-situ test control with IC technology.
to damage the existing materials. The machine was operated at a constant nominal speed of about 4.8 km/h (3 mph). For the HMA compaction, a roller vibration amplitude of 0.6 mm and a frequency of 3000 vpm were used. Both front and rear drums were in vibration mode during the HMA compaction in order to achieve more compaction energy. In this project the Zorn Lightweight Deflectometer (LWD) was used to measure the moduli of the subbase layer on selected spots to correlate with the IC measurements. 3.2. Data analysis method A data process and analysis method was proposed as shown in Fig. 3. The univariate statistics including mean, standard deviation (STD), coefficient of variance (COV), and linear regression between IC measurement (ICMV) and in-situ spot tests are studied. The compaction curve is developed to measure the mean value of ICMV for each roller pass. From the compaction cure the peak ICMV can be identified at the optimum roller pass, which can be used as a reference to avoid the over or under compaction. The geostatistics was used to evaluate the compaction uniformity as discussed later. Due to the large database (three-dimensional geospatial data with 100-percentage coverage of compaction area in real-time) with various forms as produced by different IC rollers of different manufactures, the research team and the Transtec Group developed the software, Veda® [16], in order to view, analyze, and report the IC-measured geospatial data (see Fig. 4). The univariate statistics and geostatistics models are implemented into Veda®. The IC data file was loaded into the Veda® software, and IC measurements were reflected on the Google map through GPS-coordinate identifications and plotted in a color-coded fashion (see Fig. 4a). According to the roller pass number, GPS coordinates, and time stream, Veda® analyzes the IC data for the entire project
or any specific compaction section under specific roller pass number as user defined (see Fig. 4b) [16]. The geostatistical semivariogram is also computed to evaluate the compaction uniformity. Semivariogram is a function describing the degree of spatial dependence of a random field or stochastic process. It is defined as the expected, squared increment of the values between two adjacent locations [17, 18]. It is a common tool used in geostatistics to describe spatial variation or uniformity. For a given geo-space with a defined direction (see Fig. 5), the experimental variogram, r(h), for the separation or lag distance, h, is defined as the average squared
IC measurements
Google map Color code map
Data process In-situ spot test data
Temperature Frequency ICMV Univariate statistics
Geostatistical analysis
Statistical regression
Basic statistics
Semi-variogram
Correlation of ICMV and in-situ test
Fig. 3. Flow chart of IC data process and analysis.
Q. Xu, G.K. Chang / Automation in Construction 30 (2013) 104–112
a N W
E S
21.0 18.0 15.0 12.0 9.0 6.0 3.0 0.0
CCV distribution
b Sill: 0.37
Range: 14.4 Nugget: 0.00
CCV Section 1
Vibration frequency distribution
Section 2
Temperature distribution
Range: 17.01 Sill:
0.43
Nugget: 0.00
Fig. 4. Veda® screenshot and reported ICMV and univariate and geo-statistics.
107
108
Q. Xu, G.K. Chang / Automation in Construction 30 (2013) 104–112
difference of values, Z(u), separated approximately by h for all possible location, u [17]: n o 2 2γ ðhÞ ¼ E ½Z ðuÞ−Z ðu þ hÞ
the results from the Veda® software [16] and the commercial software, Surfer® [20].
ð2Þ 4. Results and analysis
where, 4.1. Roller pattern and compaction curve u h
is the location; is the lag distance (it could be a constant for the same lag areas); is the value such as moduli and pavement responses.
Z(h)
One moves from one node (IC data point) to the next to determine the value of Z(u) − Z(u + h) for the calculation of variogram. Eq. (2) can be re-expressed as follows for sample data: 2γ ðhÞ ¼
1 2 ∑ ½Z ðuÞ−Z ðu þ hÞ NðhÞ NðhÞ
ð3Þ
where, N(h)
is the number of pairs for lag distance h of a specific lag area.
The semivariogram is then determined as half of the variogram: γ ðhÞ ¼
1 2 ∑ ½Z ðuÞ−Z ðu þ hÞ : 2NðhÞ NðhÞ
ð4Þ
Consequently, theoretical semivariogram models can be fitted to the experimental semivariogram with models such as: power model, exponential model, Gaussian model, etc. [19]. The fitted parameters from the theoretical semivariogram are indicators of uniformity: sill (C+ C0), range (R), and nugget (C0) (see Fig. 5). Sill is defined as the plateau that the semivariogram reaches, which represent the value of variance by considering the geospatial dependency. A lower “sill” and/or a greater range value indicate a better uniformity, while the opposite indicates a less uniform condition [17,19]. The exponential model fit was found to fit the semivariogram data better than others based on the current IC data: 3h r ðhÞ ¼ c 1− exp − : a
Fig. 6 shows the rolling patterns before and after the usage of the IC CIS display. The red, yellow, light blue, and dark blue color-coding correspond to the first, second, third, and fourth passes, respectively. For the asphalt mixture material and thickness of HMA layer used in this project, a roller pass of two or three was required. As indicated in the contrast of the two rolling patterns, the operator greatly improved the rolling pattern by monitoring the color-coded map on the CIS display and then adjusting the passes to meet the requirements. It should be noted that the edges of the pavement in Fig. 6 had only a single pass. This color-mapped display of tracked roller passes is very important for the operator to adjust compaction patterns in order to ensure the required number of passes, as well as to eliminate the unnecessary over passes. As a result, IC technology would help reduce the chances for under or over compaction. Fig. 7 presents the compaction curves for the HMA base and wearing courses. The 2-times polynomial function was used to fit the Sakai CCV vs. roller passes. It was fund that generally the CCV increases first and then decreases with increasing the roller pass number, or it increases continuously until arriving at a certain value. The compaction curve offers important information as it identifies the optimum roller pass number for achieving good compaction quality and prevent over or under compaction. E.g. the optimum roller pass number of four (4) is observed for most cases in this project. By applying the optimum roller pass determined from a small experimental site to the larger production areas, it would help improve construction quality.
Roller pass
ð5Þ
Based on Eq. (5), the nugget value is zero for the exponential model (r(h) = 0, at h = 0). This approach was verified by comparing
Semivariogram
Exponential Semivariogram
Range, R
Experimental Semivariogram
Scale, C Sill C + C0
Nugget, C 0
Before using IC CIS After using IC CIS
Separation Distance (m) Fig. 5. Semivariogram experiment and model fit.
Fig. 6. The rolling pattern before and after using IC CIS.
Q. Xu, G.K. Chang / Automation in Construction 30 (2013) 104–112
4.2. Correlations 4.2.1. Corrections between CCVs of different layers Fig. 8 shows CCV maps of the HMA base course layer and the underlying subbase layer. Results show that a weaker subbase (with lower CCVs) correlates to a softer HMA base layer, and vice versa. This indicates that the CCV measured on a pavement layer is dependent on the stiffness of the underlying layer, since CCV represents the relative stiffness of an entire pavement system rather than a single layer being measured. Fig. 8 also compares CCV measurements with the 5-m moving average on the HMA base course and the subbase layer over a 1300-m section along the south bound (SB) direction. Results show that the CCVs measured on the subbase and HMA base have a similar trend, indicating the reflecting effect of the subbase layer on the upper HMA base layer. It also shows that CCVs are dependent on compaction set conditions including the roller vibration frequency and vibration amplitude. Generally a higher vibration frequency and/or higher amplitude result in a higher CCV value. It shows that at the distance between 0 and 760 m, the subbase layer under the vibration with amplitude of 0.3 mm and a frequency of 3000 vpm has higher CCVs than that
under the vibration with amplitude of 0.6 mm and a frequency of 2500 vpm. However, this trend reverses for the CCVs at the distance between 760 m and 1300 m due to the comprehensive effects of roller vibration amplitude and frequency on the CCVs. It is interesting to note that at the distance of around 600 m in the CCVs are significantly lower than those at other distances, and the premature failure of HMA base is also observed at these distances (see Fig. 8). Though extra compaction efforts were performed at this weak zone, the CCVs cannot be effectively improved, which would be primarily due to the weak support from the soil foundation. Therefore, replacing the materials or performing additional treatments, such as cement stabilization, is needed to achieve a successful compaction for this weak area. This result has proved that by using the IC technology, the weak zones can be identified, and then additional treatments or extra compaction efforts can be performed in order to avoid the premature distress and ensure long-lasting performance. The relationship of CCVs between the HMA base course and subbase course are regressed and presented in Fig. 9. Results indicate a naturalZ"D>cvD"cvllogarithmic linear relationship (with a R2value of 0.69) between CCV measurements that were obtained on the HMA base course
CCV
CCV
HMA base 1
Pass
Pass
CCV
CCV
HMA base 2
Pass
Pass
HMA wearing course
CCV
CCV
109
Pass
Pass Fig. 7. Compaction curves.
110
Q. Xu, G.K. Chang / Automation in Construction 30 (2013) 104–112
HMA base
Fig. 8. Comparison of CCV measurements on HMA base course layer and subbase layer.
layer and the subbase layer at a vibration amplitude of 0.6 mm. However, CCV measurements obtained on the subbase layer at an amplitude of 0.3 mm show relatively poorer linear correlation to CCV measurements on the HMA base layer. The regression analysis was also performed for the CCVs attained at the in-situ test spots for the HMA base and HMA wearing course. As results, Fig. 10 shows a strong linear relationship between the CCVs that were measured on the HMA base course and the HMA wearing course, indicting the reflecting effect of the CCVs of underlying layer on the upper layer. 4.2.2. Correlations between CCVs and in-situ spot tests The measured LWD moduli of subbases are correlated with the CCVs measured on the HMA base and the HMA wearing course. Fig. 11 shows the correlation between the CCVs on the asphalt base/wearing courses and the LWD moduli of subbase layer. It seems that they have good linear relationships, and the CCV increases with increasing moduli since CCV represents the relative stiffness of materials. This result also further proves that the CCV measured on the upper layer is reflected by the stiffness of the underlying layer. Fig. 12 shows the relationships between the CCVs and densities (Gmm%) of the HMA base and wearing courses. It should be noted that in this project only very limited number of data points are valid and available due to the imprecisely recorded GPS locations for some spots. Results show inconsistent relationships. E.g. CCV has a strong linear relationship with Gmm% (see Fig. 12a), while a two-time polynomial
relationship with the NG (nuclear-gauge) density for the HMA base course (see Fig. 12b). For the HMA wearing course, the correlation between the lab densities (Gmm%) and CCVs are not as strong as those for the base course, as shown in Fig. 12c. Again, the above “correlation” is based on a fairly limited number of data points, and one should be cautious about the regression relationships of the results. Meanwhile, it should be noted that the measured density only represents the property of a single HMA base or wearing course layer, while the CCVs measured on the HMA base and wearing courses represent the stiffness of the entire pavement system including that of the underneath subbase course and subgrade. (a) Lab core density Gmm% vs. CCVs measured on HMA base course (b) NG density Gmm% vs. CCVs measured on HMA base course (c) Lab core density Gmm% vs. CCVs measured on HMA wearing course. 4.2.3. Correlation statistics The correlation statistic values among measurements of CCVs and Gmm% are summarized in Table 1. The closer the absolute correlation value is to 1, the more linear dependent the two variables are. I.e. 0 indicates an independent relationship, and 1 indicates a linear dependent relationship. In Table 1, it can be seen that the Gmm% has a lower correlation with all other measurements, while the rest (other than Gmm%) exhibit various levels of correlation. It is an indication that
Fig. 9. Regression relationships between CCVs on subbase and HMA base course layer.
Q. Xu, G.K. Chang / Automation in Construction 30 (2013) 104–112
18
The main findings for evaluating the effectiveness of IC technology on the HMA compaction are summarized as follows:
16
HMA wearingCCVV
111
14
• The IC system can help improve the roller pattern (e.g. more uniform roller pass was achieved with the assistance of IC ICS system); the compaction curve can identify the optimum roller pass, which may help avoid the over or under compaction; • The weak or soft areas in the pavement layers can be identified through the mapping of IC CCV. E.g. in this case study the weak spots of subbase layer were identified and then the premature distress of asphalt layer paved on the top was observed;
12 10 8 y = 1.0152x + 1.8028
6
2
R = 0.7229
4 2 0 3
5
7
9
11
13
a) Lab core density Gmm% vs. CCVs measured on
15
HMA base CCV
HMA base course 96.0
Fig. 10. Correlation of CCVs between HMA wearing and base courses at spot test points.
95.0
4.3. Variances and uniformity
94.0
%Gmm
the “correlation” between CCV and non-modulus type of test results (i.e. Gmm%) should be interpreted with care.
93.0 92.0
Table 2 summarizes the semivariogram parameters (nugget, range, and sill) and univariate statistics (mean, standard deviation, and coefficient of variance). It is seen that the mean CCV increases from “ground up” (i.e. from subbase to base, and then wearing course), as stiffer materials are always construed on upper layers for pavements. Results show that the aggregate subbase layer has better uniformity than other layers (with longer range but lower sill values), while HMA base has the lowest uniformity (with shortest range while largest sill values). It is also found that the uniformity measured by semivariogram is consistent with that of the univariate statistics for this project. For example, the aggregate subbase has the lowest COV and sill values (least variation), and HMA base has the largest COV and sill values (greater variation compared to others).
y = 1.5375x + 84.921 R2 = 0.9979
91.0 90.0 3
3.5
4
4.5
5
5.5
6
6.5
7
CCV
b) NG density Gmm% vs. CCVs measured on HMA base course 100 95
In this research, a framework for IC construction and in-situ test control using IC technology is proposed and practiced in an engineering project. The computer-aided data analysis method including the univariate statistics, geostatical semivariograms, and compaction curve, was proposed and studied. This study would help the engineering practice for accelerating the implementation, and providing data analysis methods using IC technology on asphalt materials, as a relatively new technology.
Gmm%
90
5. Conclusions and recommendations
y = -1.4385x2 + 17.186x + 44.45 R2 = 0.904
85 80 75 70 4
4.5
5
5.5
6
6.5
7
7.5
8
CCV
c) Lab core density Gmm% vs. CCVs measured on
Aggregate subbase ELWD vs. HMA CCV
HMA wearing course
180 96.0 160
y = 8.7213x + 41.439 R2 = 0.6032
y = -0.0412x 2 + 0.5633x + 91.717 R2 = 0.143
95.0
120
94.0
100
%Gmm
ELWD((MPa))
140
y = 7.9897x + 32.998 R2 = 0.5968
80
93.0 92.0
60 40
91.0
HMA base
20
HMA wearing
90.0
0
3
3
4
5
6
7
8
9
10
11
12
13
14
15
4
5
6
7
8
9
10
11
12
CCV
Sakai CCV Fig. 11. Correlation of CCVs between HMA and subbase at the spot test points.
Fig. 12. Correlations between Sakai CCVs and densities for HMA base and wearing courses.
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Table 1 Correlation pairs among various measurements in terms of correlation statistics. Variable Gmm% CCV_wearing CCV_base CCV_subbase
Gmm 1.000 −0.500 −0.584 −0.372
CCV_wearing −0.500 1.000 0.926 0.944
CCV_base −0.584 0.926 1.000 0.915
CCV_subbase −0.372 0.944 0.915 1.000
Table 2 Univariate statistics and geostatistical semivariogram parameters. Method
Variable
Aggregate base
HMA base
HMA wearing
Geostatistics
Nugget Range (m) Sill Mean STD COV
0 15.71 0.4 6.54 3.95 0.61
0 5.22 3.28 7.25 10.27 1.48
0 14.37 2.24 10.64 13.27 1.25
Univariate statistics
• The CCVs measured on the asphalt base and surface layers and on the subbase layer show to have similar trend of variations, and a linear or logarithmic-scaled linear relationship can regress their relationships well; • The CCVs measured on the HMA base show to have a linear relationship with the LWD modulus of the subbase course; • The uniformity trend indicated by the univariate statistics (COV) and that indicated by the geostatistical semivariogram parameters are consistent for this case study; • The CCVs measured on the HMA base do not have a consistent relationship with the nuclear gauge and core densities of HMA base and wear courses, but the available data points are fairly limited and further research is needed. Acknowledgment This project was supported by the Federal Highway Administration (FHWA) Transportation Pooled Fund project, “Accelerated Implementation of Intelligent Compaction Technology for Embankment Subgrade Soils, Aggregate Base, and Asphalt Pavement Materials”. The IC field demonstration for this case study was made possible by the Minnesota Department of Transportation, Kandiyohi County engineers, Sakai America, Duininck Brothers ompany, CSI construction, Iowa State University, and RoadTec.
References [1] G.K. Chang, Q. Xu, R.D. Horan, L. Michael, D. White, P. Vennapusa, Accelerated Implementation of Intelligent Compaction Technology for “Embankment Subgrade Soils, Aggregate Base, and Asphalt Pavement Materials”, Final Report, FHWA, 2011. [2] Q. Xu, K.G. Chang, V.L. Gallivan, R.D. Horan, Data analysis for hot mix asphalt intelligent compaction, in: Proceedings of the 90th Annual Transportation Research Board Meeting, No. 11–0854, Washington, D.C., 2011. [3] Intelligent Compaction Website, http://www.intelligentcompaction.com2011. [4] S.k. Kim, J. Seo, J.S. Russell, Intelligent navigation strategies for an automated earthwork system, Automation in Construction 21 (2012) 132–147. [5] S.k. Kim, J.S. Russell, Framework for an intelligent earthwork system: part I. System architecture, Automation in Construction 12 (2003) 1–13. [6] R.V. Rinehart, M.A. Mooney, Instrumentation of a roller compactor to monitor vibration behavior during earthwork compaction, Automation in Construction 17 (2008) 144–150. [7] S. Commuri, A. Mai, Neural network-based intelligent compaction analyzer for estimating compaction quality of hot asphalt mixes, Journal of Construction Engineering and Management 137 (2011) 633–715. [8] Y. Ju, G. Lin, Y. Fan, Z. Liu, Intelligent compaction control based on fuzzy neural network, in: PDCAT '05 Proceedings of the Sixth International Conference on Parallel and Distributed Computing Applications and Technologies, IEEE Computer Society, Dalian, China, 2005. [9] R.E. Minchin, D.C. Swanson, A.F. Gruss, H.R. Thomas, Computer applications in intelligent compaction, Journal of Computing in Civil Engineering 22 (2008) 243–251. [10] Y. Nohse, M. Kitano, Development of a new type of single drum vibratory roller, in: Proc. 14th Intl. Conf. of the Intl. Soc. For Terrain-Vehicle Systems, Vicksburg, MS, 2002, pp. 1–10. [11] D.J. White, M. Thompson, P. Vennapusa, Field validation of intelligent compaction monitoring technology for unbound materials, in: Mn/DOT Report No. MN/RC 2007–10, Minnesota Department of Transportation, 2007. [12] J. Scherocman, S. Rakowski, K. Uchiyama, Intelligent compaction, does it exist? in: 2007 Canadian Technical Asphalt Association (CTAA) Conference, Victoria, BC, 2007, pp. 1–25. [13] M.A. Mooney, R.V. Rinehart, N.W. Facas, O.M. Musimbi, D.J. White, P. Vennapusa, Intelligent soil compaction systems, in: Final Report, NCHRP 676, Transportation Research Board, Washington, D.C., 2010. [14] G.K. Chang, Q. Xu, R. Rasmussen, D.K. Merritt, L. Michael, D.J. White, R.D. Horan, IC Data Management Guide, Federal Highway Administration, 2011. (http://www. intelligentcompaction.com/downloads/Reports/IC_Data%20Management_V2_all. pdf). [15] Sakai Heavy Industries, Ltd., Vibrating roller type soil compaction quality controller CCV (compaction control value), in: Operating & Maintenance Instructions, 2007, pp. 1–5. [16] Veda® software homepage on Intelligent Compaction website, http://www. intelligentcompaction.com/index.php?q=node/240, 2011. [17] E.H. Isaaks, R.M. Srivastava, An Introduction to Applied Geostatistics, Oxford Univ. Press, New York, 1989. [18] H. Wackernagel, Multivariate Geostatistics, Springer, 2003. [19] I. Clark, W. Harper, Practical Geostatistics, 3rd edition Ecosse North America LLC, Columbus, OH, 2002. [20] Surfer® software, http://www.goldensoftware.com/products/surfer/surfer.shtml, 2011.
Contents lists available at SciVerse ScienceDirect
Automation in Construction journal homepage: www.elsevier.com/locate/autcon
Evaluation of intelligent compaction for asphalt materials Qinwu Xu ⁎, George K. Chang The Transtec Group Inc., 6111 Balcones Dr., Austin, TX 78731, United States
a r t i c l e
i n f o
Article history: Accepted 7 November 2012 Available online 12 December 2012 Keywords: Intelligent Compaction Asphalt Moduli Density Geostatistical semivariogram Correlations
a b s t r a c t This paper aims to evaluate the effectiveness of IC technology for the asphalt compaction. A framework of field construction and in-situ test control using IC technology was presented and implemented in one engineering project. A computer-aided data analysis method was proposed and implemented, including the univariate and geo-statistics, compaction curve and uniformity. Results show that IC technology can effectively improve the roller patter to achieve more uniform compaction, and the compaction curve identifies the optimum roller pass to help avoid under/over compaction. The trend of Sakai Compaction Control Value (CCV) – a relative index of material stiffness – is consistent with CCV from mapping the underlying subbase layer. This indicates the reflecting effect of underneath layers on upper layers. CCVs from asphalt compaction have a linear relationship with the light-weight-deflectometer moduli of subbase. The compaction uniformity trends indicated by univariate statistics are consistent with that indicated by semivariograms for this case study. © 2012 Elsevier B.V. All rights reserved.
1. Introduction For the conventional compaction on roadway materials, achieving a desired density of asphalt materials has been used as the acceptance criteria for flexible pavement construction. Usually the in-situ spot test of material density is required for the quality control (QC) and assurance (QA). However, there are issues associated with the conventional density control [1–3]: 1) the spot tests are performed at limited locations, and therefore they cannot represent the entire construction area; 2) there may be some weak areas or unqualified compaction regions not identified by limited spot testing; 3) density as a material property does not necessarily indicate the structural capacity (e.g. stiffness and strength) of the pavement layer; and 4) measured density of the top asphalt layer does not represent the property of the entire pavement system, including that of the underlying layers, to guarantee the quality of the whole pavement system. Meanwhile, the conventional compaction method generally maintains roller passes manually by roller operators. However, this compaction effort may sometimes result in over-compacted or under-compacted pavement materials since the support and stiffness of materials maybe very different at different locations. As a result, premature distress of pavements may appear [1, 2], and will in turn result in worse long-term pavement performances and higher life-cycle costs. Intelligent construction systems have been studied in the past to improve the efficiency and safety, including the intelligent navigation
⁎ Corresponding author. Tel.: +1 512 451 6233; fax: +1 512 451 6234. E-mail addresses: [email protected] (Q. Xu), [email protected] (G.K. Chang). 0926-5805/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.autcon.2012.11.015
strategies [4], autonomous systems with artificial intelligence approaches [5], and the instrumented roller compactor system monitoring vibration behavior [6]. Other intelligent systems studied for the asphalt paving industries include that using the neural network modeling [7, 8], and the on-board density reading system [7, 9]. Intelligent Compaction (IC) technology has been developed to address issues existing in the conventional compaction and QC/QA methods, as used for road materials including soils, aggregates, and asphalt mixtures. IC uses accelerometer-based vibratory rollers, equipped with an in-situ measurement system and feedback controls [3, 10]. It includes two cores of the hardware system [1, 3]: 1) the accelerometer mounted to the vibratory roller that measures and records the roller vibration information such as harmonic frequencies and accelerations, as used to determine the IC measurement value (ICMV); and 2) the GPS system that tracks the roller pass and location in real time. The compaction operators manually, or the IC rollers automatically, adjust the compaction efforts in real-time during the compaction process in order to achieve a more uniform compaction. IC rollers maintain a continuous record of GPS-map-based color-coded plots that include the number of roller passes, ICMV, and locations of the roller, roller speed and vibration frequency, with 100-percentage converge of the entire compaction area in real time. IC rollers can also monitor the asphalt pavement surface temperature by the instrumented infrared thermometer, which provides essential information for characterizing HMA material properties. The IC technology uses ICMV as the primary index to evaluate the compaction effect, although ICMVs from various vendors are defined in different forms. ICMV is an index value correlated to the stiffness or modulus of the pavement layer system under compaction. With 100-percent coverage on the compacted area, ICMV aims to achieve a higher quality and uniformity of pavement
Q. Xu, G.K. Chang / Automation in Construction 30 (2013) 104–112
materials, which in turn better ensures the long-lasting performance. ICMV is determined from the real-time information of compaction efforts such as roller vibration, amplitudes, frequencies, and responses from the material being compacted that are monitored and recorded by the compaction accelerometers. ICMVs are recorded in real-time for each roller pass in order to identify the stiff or weak spots of the pavement layer under compaction, which provides necessary information for the operator or the machine itself to adjust the compaction effects in order to achieve a more uniform ICMV distribution. IC technologies have been implemented in Europe and Japan for years, and they were introduced to the USA in late 2000's [1, 11–13]. For the HMA compaction industry, the IC is a relatively new, still immature and evolving construction technology [1, 12]. There are still some challenging issues for HMA IC technology being studied, such as how to decouple the ICMV to the single layer of pavement including the top HMA layer, and how to quantify the HMA's temperature effect on ICMV [1–3]. Meanwhile, IC produces a large database even for each single project, and presented in various forms for different rollers of different manufacturers. An efficient and systematic data analysis method or tool would be in needs to help accelerate the implementation of IC technology for asphalt paving industry [14]. With the research needs, the objective of this research is to evaluate the effectiveness of IC technology on asphalt materials, under the Federal Highway Administration (FHWA)'s pooled funding project that targets to help realize the blueprint of the IC technology implementation in the US [1, 3]. The field construction and in-situ test control for asphalt materials compaction using IC technology was discussed and practiced. A computer-aided systematic data analysis method based on both the univariate and geostatistical methods are studied and implemented, in order to evaluate the effectiveness of IC technology for asphalt materials. One demonstration project under the FHWA's pooled fund IC study was used for validation and evaluation. 2. IC project background The IC demonstration project studied in this paper was performed on highway Route 4 in Minnesota, USA [3]. It is a new full-depth asphalt pavement project. The centerline length of the Route 4 was approximately 5.1 km (3.2 mi.), which was paved with 64 mm (2.5 in.) of HMA base course (containing 30% reclaimed asphalt pavement) and 38 mm (1.5 in.) of HMA wearing course (containing 20% reclaimed asphalt pavement) on the top of aggregate subbase, as shown in Fig. 1. The Sakai double drum IC roller, Model SW880, was used for these demonstration projects (Fig. 1). The roller is equipped with the Sakai Exact Compact Feature which controls the roller travel speed at the selected vibration frequencies of 2500, 3000, or 4000 vpm (vibrations per minute). The Compaction Information System (CIS) includes IC hardware and software that measure roller passes, temperatures, and stiffness of the compacted surface of pavement layers. The CIS is installed on a Sakai single-drum vibratory soil roller or double drum asphalt rollers. The ICMV for Sakai, Compaction Control Value (CCV), is a unitless index value relating to the compaction level and material stiffness [15]. Note that the CCV measures the relative stiffness of the multilayered pavement system with an influence depth, including the stiffness contributions from the underlying layers rather than a single layer under compaction. The concept behind the CCV is that as the ground stiffness increases, the roller drum starts to enter into a “jumping” motion, which results in vibration accelerations at various frequency components [15]. CCV is calculated by using the acceleration data of amplitudes (A) from the first sub-harmonic (0.5 Ω), fundamental frequency (Ω), and higher-order harmonics (1.5 Ω, 2 Ω, 2.5 Ω, 3 Ω) as presented in Eq. (1) [15]. The vibration acceleration signal from the accelerometer is transformed through the Fast Fourier Transform
105
38 mm HMA wearin
GPS receiver
64 mm HMA base Aggregate subbase
Soil
Infrared thermometer
Accelerometer
Fig. 1. Pavement structure and IC roller.
(FFT) method and then filtered through band pass filters to detect the acceleration amplitude spectrum [15]: CCV ¼
A0:5
Ω
þ A1:5
þ A2 Ω þ A2:5 A0:5Ω þ AΩ
Ω
Ω
þ A3
Ω
100:
ð1Þ
3. Methodologies 3.1. Framework of construction and in-situ test control Fig. 2 presents a framework for the construction and in-situ test control using IC technology for asphalt materials. Before the construction starts, the GPS base station was set up, which communicates with satellites and transmits signals to both the GPS receiver of IC roller and GPS hand rover. With the GPS base station, the real-time kinematic (RTK) GPS-system achieves a “fixed” precision of 1–3 cm, while without it the GPS system only achieves a “float” precision of meters to submeter [1, 3, 14]. Consequently, the IC roller GPS precision shall be verified before compaction starts, since all the compaction data collected later are accounted on the precise GPS records. Here a practical method is proposed as follows: take the GPS receiver (head) off the GPS hand rover and place on the top (head) of the roller GPS receiver, to assure that they have the same coordinates by measuring at least three spots with different distances and angles. The unit of meter rather than geographic degree–minute–second is recommended for data recording, since the later one is hard to differentiate the spots. After assuring the roller GPS accuracy, IC roller is ready to roll over and map the existing underlying layers prior to HMA paving to evaluate the existing pavement conditions. If the IC color-coded records do not come out in a right way, adjustments and re-set shall be conducted immediately. Consequently, in-situ spot tests on underlying layers are measured as used for correlation to IC measurements later. The GPS hand rover is then followed to measure the coordinates of each test spot. After everything is ready, paving starts, as followed by the compaction in the series of breakdown, intermediate, and then finishing compacting. When the IC roller moved forward, the spot tests of HMA density (e.g. nuclear gauge density) at random spots were measured for quality control. The GPS hand rover is again used to record coordinates of those random spots. After finishing the compaction of each test bed, the IC measurement data shall be extracted from the roller CIS and stored into a USB driver as soon before moving to the next test bed and resetting the machine. After the finishing roller passed, field cores were taken to measure core density for the QA purpose. For mapping the existing granular subbase layer, a roller vibration amplitude of 0.6 mm and frequencies of 2500 and 3000 vpm (vibration per minute) was used. During the mapping process, the front drum was in vibration mode while the rear drum was in static mode in order not
Q. Xu, G.K. Chang / Automation in Construction 30 (2013) 104–112
Before compaction
106
Roller GPS verification
Mapping existing layer
Paving & Compaction
GPS base station set up
After Compaction
Paving
Finishing Compaction
Intelligent Compaction
Density
LWD/FWD Spot in-situ test
Spot GPS shot
Download and store data
Fig. 2. Framework of construction and in-situ test control with IC technology.
to damage the existing materials. The machine was operated at a constant nominal speed of about 4.8 km/h (3 mph). For the HMA compaction, a roller vibration amplitude of 0.6 mm and a frequency of 3000 vpm were used. Both front and rear drums were in vibration mode during the HMA compaction in order to achieve more compaction energy. In this project the Zorn Lightweight Deflectometer (LWD) was used to measure the moduli of the subbase layer on selected spots to correlate with the IC measurements. 3.2. Data analysis method A data process and analysis method was proposed as shown in Fig. 3. The univariate statistics including mean, standard deviation (STD), coefficient of variance (COV), and linear regression between IC measurement (ICMV) and in-situ spot tests are studied. The compaction curve is developed to measure the mean value of ICMV for each roller pass. From the compaction cure the peak ICMV can be identified at the optimum roller pass, which can be used as a reference to avoid the over or under compaction. The geostatistics was used to evaluate the compaction uniformity as discussed later. Due to the large database (three-dimensional geospatial data with 100-percentage coverage of compaction area in real-time) with various forms as produced by different IC rollers of different manufactures, the research team and the Transtec Group developed the software, Veda® [16], in order to view, analyze, and report the IC-measured geospatial data (see Fig. 4). The univariate statistics and geostatistics models are implemented into Veda®. The IC data file was loaded into the Veda® software, and IC measurements were reflected on the Google map through GPS-coordinate identifications and plotted in a color-coded fashion (see Fig. 4a). According to the roller pass number, GPS coordinates, and time stream, Veda® analyzes the IC data for the entire project
or any specific compaction section under specific roller pass number as user defined (see Fig. 4b) [16]. The geostatistical semivariogram is also computed to evaluate the compaction uniformity. Semivariogram is a function describing the degree of spatial dependence of a random field or stochastic process. It is defined as the expected, squared increment of the values between two adjacent locations [17, 18]. It is a common tool used in geostatistics to describe spatial variation or uniformity. For a given geo-space with a defined direction (see Fig. 5), the experimental variogram, r(h), for the separation or lag distance, h, is defined as the average squared
IC measurements
Google map Color code map
Data process In-situ spot test data
Temperature Frequency ICMV Univariate statistics
Geostatistical analysis
Statistical regression
Basic statistics
Semi-variogram
Correlation of ICMV and in-situ test
Fig. 3. Flow chart of IC data process and analysis.
Q. Xu, G.K. Chang / Automation in Construction 30 (2013) 104–112
a N W
E S
21.0 18.0 15.0 12.0 9.0 6.0 3.0 0.0
CCV distribution
b Sill: 0.37
Range: 14.4 Nugget: 0.00
CCV Section 1
Vibration frequency distribution
Section 2
Temperature distribution
Range: 17.01 Sill:
0.43
Nugget: 0.00
Fig. 4. Veda® screenshot and reported ICMV and univariate and geo-statistics.
107
108
Q. Xu, G.K. Chang / Automation in Construction 30 (2013) 104–112
difference of values, Z(u), separated approximately by h for all possible location, u [17]: n o 2 2γ ðhÞ ¼ E ½Z ðuÞ−Z ðu þ hÞ
the results from the Veda® software [16] and the commercial software, Surfer® [20].
ð2Þ 4. Results and analysis
where, 4.1. Roller pattern and compaction curve u h
is the location; is the lag distance (it could be a constant for the same lag areas); is the value such as moduli and pavement responses.
Z(h)
One moves from one node (IC data point) to the next to determine the value of Z(u) − Z(u + h) for the calculation of variogram. Eq. (2) can be re-expressed as follows for sample data: 2γ ðhÞ ¼
1 2 ∑ ½Z ðuÞ−Z ðu þ hÞ NðhÞ NðhÞ
ð3Þ
where, N(h)
is the number of pairs for lag distance h of a specific lag area.
The semivariogram is then determined as half of the variogram: γ ðhÞ ¼
1 2 ∑ ½Z ðuÞ−Z ðu þ hÞ : 2NðhÞ NðhÞ
ð4Þ
Consequently, theoretical semivariogram models can be fitted to the experimental semivariogram with models such as: power model, exponential model, Gaussian model, etc. [19]. The fitted parameters from the theoretical semivariogram are indicators of uniformity: sill (C+ C0), range (R), and nugget (C0) (see Fig. 5). Sill is defined as the plateau that the semivariogram reaches, which represent the value of variance by considering the geospatial dependency. A lower “sill” and/or a greater range value indicate a better uniformity, while the opposite indicates a less uniform condition [17,19]. The exponential model fit was found to fit the semivariogram data better than others based on the current IC data: 3h r ðhÞ ¼ c 1− exp − : a
Fig. 6 shows the rolling patterns before and after the usage of the IC CIS display. The red, yellow, light blue, and dark blue color-coding correspond to the first, second, third, and fourth passes, respectively. For the asphalt mixture material and thickness of HMA layer used in this project, a roller pass of two or three was required. As indicated in the contrast of the two rolling patterns, the operator greatly improved the rolling pattern by monitoring the color-coded map on the CIS display and then adjusting the passes to meet the requirements. It should be noted that the edges of the pavement in Fig. 6 had only a single pass. This color-mapped display of tracked roller passes is very important for the operator to adjust compaction patterns in order to ensure the required number of passes, as well as to eliminate the unnecessary over passes. As a result, IC technology would help reduce the chances for under or over compaction. Fig. 7 presents the compaction curves for the HMA base and wearing courses. The 2-times polynomial function was used to fit the Sakai CCV vs. roller passes. It was fund that generally the CCV increases first and then decreases with increasing the roller pass number, or it increases continuously until arriving at a certain value. The compaction curve offers important information as it identifies the optimum roller pass number for achieving good compaction quality and prevent over or under compaction. E.g. the optimum roller pass number of four (4) is observed for most cases in this project. By applying the optimum roller pass determined from a small experimental site to the larger production areas, it would help improve construction quality.
Roller pass
ð5Þ
Based on Eq. (5), the nugget value is zero for the exponential model (r(h) = 0, at h = 0). This approach was verified by comparing
Semivariogram
Exponential Semivariogram
Range, R
Experimental Semivariogram
Scale, C Sill C + C0
Nugget, C 0
Before using IC CIS After using IC CIS
Separation Distance (m) Fig. 5. Semivariogram experiment and model fit.
Fig. 6. The rolling pattern before and after using IC CIS.
Q. Xu, G.K. Chang / Automation in Construction 30 (2013) 104–112
4.2. Correlations 4.2.1. Corrections between CCVs of different layers Fig. 8 shows CCV maps of the HMA base course layer and the underlying subbase layer. Results show that a weaker subbase (with lower CCVs) correlates to a softer HMA base layer, and vice versa. This indicates that the CCV measured on a pavement layer is dependent on the stiffness of the underlying layer, since CCV represents the relative stiffness of an entire pavement system rather than a single layer being measured. Fig. 8 also compares CCV measurements with the 5-m moving average on the HMA base course and the subbase layer over a 1300-m section along the south bound (SB) direction. Results show that the CCVs measured on the subbase and HMA base have a similar trend, indicating the reflecting effect of the subbase layer on the upper HMA base layer. It also shows that CCVs are dependent on compaction set conditions including the roller vibration frequency and vibration amplitude. Generally a higher vibration frequency and/or higher amplitude result in a higher CCV value. It shows that at the distance between 0 and 760 m, the subbase layer under the vibration with amplitude of 0.3 mm and a frequency of 3000 vpm has higher CCVs than that
under the vibration with amplitude of 0.6 mm and a frequency of 2500 vpm. However, this trend reverses for the CCVs at the distance between 760 m and 1300 m due to the comprehensive effects of roller vibration amplitude and frequency on the CCVs. It is interesting to note that at the distance of around 600 m in the CCVs are significantly lower than those at other distances, and the premature failure of HMA base is also observed at these distances (see Fig. 8). Though extra compaction efforts were performed at this weak zone, the CCVs cannot be effectively improved, which would be primarily due to the weak support from the soil foundation. Therefore, replacing the materials or performing additional treatments, such as cement stabilization, is needed to achieve a successful compaction for this weak area. This result has proved that by using the IC technology, the weak zones can be identified, and then additional treatments or extra compaction efforts can be performed in order to avoid the premature distress and ensure long-lasting performance. The relationship of CCVs between the HMA base course and subbase course are regressed and presented in Fig. 9. Results indicate a naturalZ"D>cvD"cvllogarithmic linear relationship (with a R2value of 0.69) between CCV measurements that were obtained on the HMA base course
CCV
CCV
HMA base 1
Pass
Pass
CCV
CCV
HMA base 2
Pass
Pass
HMA wearing course
CCV
CCV
109
Pass
Pass Fig. 7. Compaction curves.
110
Q. Xu, G.K. Chang / Automation in Construction 30 (2013) 104–112
HMA base
Fig. 8. Comparison of CCV measurements on HMA base course layer and subbase layer.
layer and the subbase layer at a vibration amplitude of 0.6 mm. However, CCV measurements obtained on the subbase layer at an amplitude of 0.3 mm show relatively poorer linear correlation to CCV measurements on the HMA base layer. The regression analysis was also performed for the CCVs attained at the in-situ test spots for the HMA base and HMA wearing course. As results, Fig. 10 shows a strong linear relationship between the CCVs that were measured on the HMA base course and the HMA wearing course, indicting the reflecting effect of the CCVs of underlying layer on the upper layer. 4.2.2. Correlations between CCVs and in-situ spot tests The measured LWD moduli of subbases are correlated with the CCVs measured on the HMA base and the HMA wearing course. Fig. 11 shows the correlation between the CCVs on the asphalt base/wearing courses and the LWD moduli of subbase layer. It seems that they have good linear relationships, and the CCV increases with increasing moduli since CCV represents the relative stiffness of materials. This result also further proves that the CCV measured on the upper layer is reflected by the stiffness of the underlying layer. Fig. 12 shows the relationships between the CCVs and densities (Gmm%) of the HMA base and wearing courses. It should be noted that in this project only very limited number of data points are valid and available due to the imprecisely recorded GPS locations for some spots. Results show inconsistent relationships. E.g. CCV has a strong linear relationship with Gmm% (see Fig. 12a), while a two-time polynomial
relationship with the NG (nuclear-gauge) density for the HMA base course (see Fig. 12b). For the HMA wearing course, the correlation between the lab densities (Gmm%) and CCVs are not as strong as those for the base course, as shown in Fig. 12c. Again, the above “correlation” is based on a fairly limited number of data points, and one should be cautious about the regression relationships of the results. Meanwhile, it should be noted that the measured density only represents the property of a single HMA base or wearing course layer, while the CCVs measured on the HMA base and wearing courses represent the stiffness of the entire pavement system including that of the underneath subbase course and subgrade. (a) Lab core density Gmm% vs. CCVs measured on HMA base course (b) NG density Gmm% vs. CCVs measured on HMA base course (c) Lab core density Gmm% vs. CCVs measured on HMA wearing course. 4.2.3. Correlation statistics The correlation statistic values among measurements of CCVs and Gmm% are summarized in Table 1. The closer the absolute correlation value is to 1, the more linear dependent the two variables are. I.e. 0 indicates an independent relationship, and 1 indicates a linear dependent relationship. In Table 1, it can be seen that the Gmm% has a lower correlation with all other measurements, while the rest (other than Gmm%) exhibit various levels of correlation. It is an indication that
Fig. 9. Regression relationships between CCVs on subbase and HMA base course layer.
Q. Xu, G.K. Chang / Automation in Construction 30 (2013) 104–112
18
The main findings for evaluating the effectiveness of IC technology on the HMA compaction are summarized as follows:
16
HMA wearingCCVV
111
14
• The IC system can help improve the roller pattern (e.g. more uniform roller pass was achieved with the assistance of IC ICS system); the compaction curve can identify the optimum roller pass, which may help avoid the over or under compaction; • The weak or soft areas in the pavement layers can be identified through the mapping of IC CCV. E.g. in this case study the weak spots of subbase layer were identified and then the premature distress of asphalt layer paved on the top was observed;
12 10 8 y = 1.0152x + 1.8028
6
2
R = 0.7229
4 2 0 3
5
7
9
11
13
a) Lab core density Gmm% vs. CCVs measured on
15
HMA base CCV
HMA base course 96.0
Fig. 10. Correlation of CCVs between HMA wearing and base courses at spot test points.
95.0
4.3. Variances and uniformity
94.0
%Gmm
the “correlation” between CCV and non-modulus type of test results (i.e. Gmm%) should be interpreted with care.
93.0 92.0
Table 2 summarizes the semivariogram parameters (nugget, range, and sill) and univariate statistics (mean, standard deviation, and coefficient of variance). It is seen that the mean CCV increases from “ground up” (i.e. from subbase to base, and then wearing course), as stiffer materials are always construed on upper layers for pavements. Results show that the aggregate subbase layer has better uniformity than other layers (with longer range but lower sill values), while HMA base has the lowest uniformity (with shortest range while largest sill values). It is also found that the uniformity measured by semivariogram is consistent with that of the univariate statistics for this project. For example, the aggregate subbase has the lowest COV and sill values (least variation), and HMA base has the largest COV and sill values (greater variation compared to others).
y = 1.5375x + 84.921 R2 = 0.9979
91.0 90.0 3
3.5
4
4.5
5
5.5
6
6.5
7
CCV
b) NG density Gmm% vs. CCVs measured on HMA base course 100 95
In this research, a framework for IC construction and in-situ test control using IC technology is proposed and practiced in an engineering project. The computer-aided data analysis method including the univariate statistics, geostatical semivariograms, and compaction curve, was proposed and studied. This study would help the engineering practice for accelerating the implementation, and providing data analysis methods using IC technology on asphalt materials, as a relatively new technology.
Gmm%
90
5. Conclusions and recommendations
y = -1.4385x2 + 17.186x + 44.45 R2 = 0.904
85 80 75 70 4
4.5
5
5.5
6
6.5
7
7.5
8
CCV
c) Lab core density Gmm% vs. CCVs measured on
Aggregate subbase ELWD vs. HMA CCV
HMA wearing course
180 96.0 160
y = 8.7213x + 41.439 R2 = 0.6032
y = -0.0412x 2 + 0.5633x + 91.717 R2 = 0.143
95.0
120
94.0
100
%Gmm
ELWD((MPa))
140
y = 7.9897x + 32.998 R2 = 0.5968
80
93.0 92.0
60 40
91.0
HMA base
20
HMA wearing
90.0
0
3
3
4
5
6
7
8
9
10
11
12
13
14
15
4
5
6
7
8
9
10
11
12
CCV
Sakai CCV Fig. 11. Correlation of CCVs between HMA and subbase at the spot test points.
Fig. 12. Correlations between Sakai CCVs and densities for HMA base and wearing courses.
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Q. Xu, G.K. Chang / Automation in Construction 30 (2013) 104–112
Table 1 Correlation pairs among various measurements in terms of correlation statistics. Variable Gmm% CCV_wearing CCV_base CCV_subbase
Gmm 1.000 −0.500 −0.584 −0.372
CCV_wearing −0.500 1.000 0.926 0.944
CCV_base −0.584 0.926 1.000 0.915
CCV_subbase −0.372 0.944 0.915 1.000
Table 2 Univariate statistics and geostatistical semivariogram parameters. Method
Variable
Aggregate base
HMA base
HMA wearing
Geostatistics
Nugget Range (m) Sill Mean STD COV
0 15.71 0.4 6.54 3.95 0.61
0 5.22 3.28 7.25 10.27 1.48
0 14.37 2.24 10.64 13.27 1.25
Univariate statistics
• The CCVs measured on the asphalt base and surface layers and on the subbase layer show to have similar trend of variations, and a linear or logarithmic-scaled linear relationship can regress their relationships well; • The CCVs measured on the HMA base show to have a linear relationship with the LWD modulus of the subbase course; • The uniformity trend indicated by the univariate statistics (COV) and that indicated by the geostatistical semivariogram parameters are consistent for this case study; • The CCVs measured on the HMA base do not have a consistent relationship with the nuclear gauge and core densities of HMA base and wear courses, but the available data points are fairly limited and further research is needed. Acknowledgment This project was supported by the Federal Highway Administration (FHWA) Transportation Pooled Fund project, “Accelerated Implementation of Intelligent Compaction Technology for Embankment Subgrade Soils, Aggregate Base, and Asphalt Pavement Materials”. The IC field demonstration for this case study was made possible by the Minnesota Department of Transportation, Kandiyohi County engineers, Sakai America, Duininck Brothers ompany, CSI construction, Iowa State University, and RoadTec.
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