Estimating the hotmix asphalt air voids from ground penetrating radar

NDT&E International 68 (2014) 120–127

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Estimating the hotmix asphalt air voids from ground penetrating radar Dar-Hao Chen a,b,n, Feng Hong b, Wujun Zhou c, Peng Ying a a b c

Central South University of Forestry and Technology, Shao Shan South road #498, Changsha, Hunan, China Texas Department of Transportation, 4203 Bull Creek #39, Austin, TX 78731, USA Huazhong University of Science and Technology, School of Civil Engineering & Mechanics, Wuhan, Hubei, 430074, China

art ic l e i nf o

a b s t r a c t

Article history: Received 25 February 2014 Received in revised form 20 August 2014 Accepted 22 August 2014 Available online 30 August 2014

Traditionally, in-place air voids are obtained based on field cores. Coring is a destructive and timeconsuming process. This study presents a high speed Non-Destructive Testing (NDT) technique with Ground Penetrating Radar (GPR) to characterize the in-place air voids. A total of 92 cores were retrieved from three field projects to establish relationship between the air voids and the measured dielectric by GPR. A statistical model was developed to express the air void value as a function of dielectric and other variables. Contour air void maps could also be produced for the entire pavement sections. The results from the underlying studies have been used as the basis for the repair strategy selections. & 2014 Elsevier Ltd. All rights reserved.

Keywords: Air void Dielectric Electromagnetic wave GPR

1. Introduction Uniform material is one of the key factors to the long lasting pavements. This is especially true for pavement with a thin Hot Mix Asphalt Concrete (HMAC) (e.g. less than 100 mm (4 in.)). Localized non-uniform zones of mix, commonly called segregation, often become low-density areas in the mat. Segregation continues to be a major construction-related problem with a significant adverse impact on pavement service life [14,16]. The Asphalt Institute [3] reported that when air voids reach 8% or higher, there will be interconnected voids in the mix which allow air and moisture to permeate the pavement which reduces the durability of the pavement. The most common form of HMAC segregation, truck-end segregation, occurs where the HMAC at the end of a truckload is colder and sometimes coarser in gradation [14]. These locations manifest in the HMAC mat as regularly spaced defects at approximately 45.7 m (150 ft) intervals along the roadway, as an example shown in Fig. 1(A). These segregated locations usually deteriorate early, typically because of their lower density and higher susceptibility to raveling and fatigue cracking. Over time, these locations, with the ingress of water and the influence of traffic loads, fail prematurely. This early distress not only results in poorer ride quality for the traveling public but also requires highway agencies to use resources earlier than planned to maintain the pavement in a good condition. For example, Fig. 2 n

Corresponding author. E-mail addresses: [email protected] (D.-H. Chen), [email protected] (F. Hong), [email protected] (W. Zhou), [email protected] (P. Ying). http://dx.doi.org/10.1016/j.ndteint.2014.08.008 0963-8695/& 2014 Elsevier Ltd. All rights reserved.

shows a typical distress of alligator cracking that failed in one year due to high air void or low density in HMAC [5]. The cause of the premature failure was determined from a forensic study [5]. In addition, transportation agencies practically adjust pay factor for field placement of HMAC based on the density or air voids. However, these coring programs are spot specific and very likely to miss localized areas of low quality. In recent years, GroundPenetrating Radar (GPR) has been used to estimate pavement layer thickness. As a nondestructive testing (NDT) device, the GPR measures an electrical property (dielectric) of a material such as HMAC that has been shown to correlate well with the density [4,11]. By calibrating the relationship between the dielectric and the density of the HMAC, the radar NDT technique may be able to serve as a final quality assurance check on a completed mat. GPR possesses a unique advantage over traditional density-based testing because data collection with fact GPR survey is typically conducted by a vehiclemounted system that travels at highway speed. Nearly 100% of the newly constructed surface area can be tested in a matter of minutes. Low surface dielectric values typically indicate higher air voids (or lower density) in the compacted HMAC, as shown in Fig. 1(B). This paper illustrates three case studies conducted over the last 6 years that related to poor performing pavements with high air void in HMAC layers. In particular, one project failed prematurely in less than one year of trafficking. GPR surveys were conducted to identify locations with various degrees of densities for coring verification. Laboratory tests were performed to determine the densities that provide opportunity to establish the correlation between measured dielectric from GPR and lab determined density. The correlation provides an opportunity to locate low quality HMAC in real time during the GPR survey.

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Segregated Areas

Low Density

Fig. 1. Segregated HMAC with regularly spaced defects and low density with the corresponding low surface dielectric.

Alligator Cracking & Deformation

Fig. 2. A typical pavement cracking condition due to low density in HMAC.

1.1. Principle of GPR in pavement data collection A GPR system uses discrete pulses of radar energy or electromagnetic (EM) wave. The principle of GPR can be explained through the traveling of EM wave in a subsurface structure system, as is shown in Fig. 3 [1,4]. A GPR system's transmitter embedded in the antenna applies a short pulse of EM energy into a subsurface structure system. At the interface (e.g. between air and surface layer, or between the surface and base layers of a pavement) where the pulse meets a material that has different electrical properties, part of transmitted energy reflects back and the remaining energy travels to the next layer. A receiver records the amplitude and arrival time of reflected signals. The magnitude of amplitude varies with the contrast of adjacent layers materials' properties—the more significant the contrast, the higher the amplitude. If an EM wave travels through a homogeneous layer, the amplitude is zero. Typically, it is reasonable to regard that material in a given pavement structural layer with sound construction is homogeneous. When material properties vary across different structural layers, it results in identifiable reflection at the interface between adjacent layers.

To better understand the principle of GPR and its application in the underlying study, one of the electrical properties of road pavement materials, the dielectric constant deserves a special discussion. The dielectric constant is closely related to a material's property in the following aspects: (1) it reflects the material's conductivity; (2) it defines the refraction index of the material; and (3) it controls the speed of the EM waves in the material. The term “constant” implies that the dielectric for a given material can be assumed to be a fixed value. As examples, AASHTO Designa tion R 37-04 provides dielectric constants for a list of materials commonly encountered in pavement construction: 1 for air, 81 for water, 2–5 for dry sand, 20–30 for wet sand, 5–30 for silts, 5–40 for clays, 4–6 for granite, 4–8 for limestone, 6–11 for Portland cement concrete (cured), and 3–6 for bituminous concrete [1]. In a subsurface system, it is the contrast of dielectric constants that governs the magnitude of reflection amplitudes. For example, for an EM wave moving from dry sand to wet sand, a very strong reflection will appear; while one wave moving from dry sand to limestone will produce a very weak reflection. In practice, the materials' dielectric constants are not directly available. Nevertheless, the reflection amplitudes can be easily obtained based on the recorded signal in the receiver, which can be used to derive the dielectric constant. Based on surface reflection method, a physical equation has been established and used to determine surface/HMA layer's dielectric [6,11,12].   1 þ A0 =Am 2 EHMA ¼ ð1Þ 1  A0 =Am where, EHMA : HMA layer dielectric, A0 :Amplitude of pavement surface reflection, and Am :Amplitude of metal plate (on pavement) reflection. It should be noted that the dielectric constant for a mixture (such as HMAC) is a function of its material composition and components' dielectric constants. In particular, existing studies suggested that there was a strong correlation between HMAC dielectric constant and air void content [2,11].

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Metal Plate

Fig. 3. Ground-Penetrating Radar (GPR) with mounted antenna and principle of GPR (with EM wave traveling in different materials).

2. Case studies In this study, three highway projects are utilized to establish relationships between in-place air voids and dielectric values of HMAC surface layer. The GPR with a central frequency of around 1000 MG Hz was first used to scan a pavement of interest to obtain HMAC's dielectric. The data collection interval could vary from 1 ft to any higher value. Second, core samples were retrieved for lab testing to determine the in-place air voids. These two pieces of information are linked to develop a mathematical relationship between air void and dielectric values of HMAC. Each project involved in this study is described in the following.

2.1. Project 1 A forensic investigation was conducted to evaluate the premature failure of a pavement on Farm-to-Market (FM) 917 with less than one year of trafficking. The premature pavement failure occurred in the form of rutting and alligator cracking, as shown in Fig. 2 Though the majority of failures occurred in the west bound (WB) lane, east bound (EB) lane did have a few failures. The typical section consists of 38 mm (1.5 in.) of Type D HMAC, 64 mm (2.5 in.) of Type B HMAC, 152 mm (6 in.) of New Flex Base, Geogrid, and 152 mm (6 in.) of remixed existing base. Type D and Type B represent fine and coarse mixes, respectively. Additional information related to the Type D and Type B mixes can be found in the literature [TxDOT 2004]. The project length is approximately 11.27 km (7 miles). Based on 2007 traffic data, the estimated 20-year design traffic (2007–2027) was 2.4 million 80 kN (18,000 lbf) Equivalent Single Axle Loads (ESALs) with an Annual Average Daily Traffic (AADT) of 5100 vehicles. The GPR was utilized to survey the entire area. Core locations were firstly identified based on the GPR survey. The cores were taken to determine the HMAC densities. The main purpose is to correlate the dielectric measurements from GPR with the lab measured density to generate the density/air void map for the entire pavement section. The results clearly showed that some of the air voids did not meet the specification requirements with air voids exceeding 10%. The current specification indicated that when an Asphalt Concrete (AC) air void is greater than 9.9%, the HMAC mat needs to be removed and replaced. Also, there are penalty when the air void is greater than 8.5%. Based on the lab results, 36% of the cores fall into the category of either penalty or “remove and replace”.

District needs a contour map that shows the areas are (1) greater than 8.5% but less than 9.9% (2) greater than 9.9%. Multiple GPR runs in one travel lane were conducted to measure dielectric and additional 52 cores locations were identified based on the GPR survey and they were used to determine the core densities. Correlations between the measured dielectric and air void were established. To ensure a sufficiently large coverage on the pavement being investigated, three passes were surveyed by the GPR on each direction, left wheel path (around 8 ft from the lane edge), between wheel path (around 5 ft from the edge), and right wheel path (around 2 ft from the lane edge). It is known that the dielectric of an asphalt mix is proportionally contributed by the components in the mix, which includes binder, aggregate, air void, and others such as moisture. As aforementioned, the dielectric of air being equal to 1 is smaller than those of all other components. Thus, it will reduce the dielectric of the mix if there is more air void content in the mix. This implies that for a mix with a given volume of binder and aggregate, the dielectric of the mix increases with the decrease of the air void, or vice versa. This finding was supported in some existing studies such as by Saarenkento et al. [9], [2], and Sebesta and Scullion [10]. In these studies, an exponential function was found effectively predict air voids based on dielectric values. In the same manner, this study adopts a prototype of exponential model to represent the relationship between air voids and dielectric as shown in Eq. 2. AV ¼ φ0 expðφ1 EÞ

ð2Þ

where, AV is the air void, %; Eis asphalt concrete dielectric, and φ0 andφ1 are constants. Regression analysis is conducted to obtain the constants of the exponential functions in Eq. 2. Based on the established functions, the air void in the pavement can be predicted by plugging the dielectric into Eq. 2. The details of the modeling and regression analysis are to be discussed in the subsequent sections. Furthermore, based on the estimated air voids along all of the scan lines, contour maps were obtained from the predicted AV for an entire travel lane. As an example for overall view, the contour for a 1.61 km (one mile) stretch of both eastbound and westbound pavement is illustrated in Fig. 4. According to Texas Department of Transportation [16], for AV between 8.5% and 9.9%, penalty is applied on the project; while it is required that the pavement be removed and reconstructed if the AV is over 9.9%. To capture these

AcrossLane(0.1in)

D.-H. Chen et al. / NDT&E International 68 (2014) 120–127

900

AV7.8%

LW

600

AV8.6%

AV11.8% AV11.4%

BW

AV10.2%

AV9.8%

300 RW

123

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

AcrossLane(0.1in)

E/B Distance (ft) 900 LW

AV10.2%

600

AV13.0%

BW

300 RW

AV9.4%

500

AV6.2% AV6.2% AV7.0%

AV8.2%

1000

1500

2000

1ft=0.3m

2500

3000

3500

4000

4500

AV6.2%

5000

W/B Distance (ft)

Fig. 4. Contours with predicted AV for the first mile of pavement on both directions, i.e. eastbound (E/B) and westbound (W/B).

LW

AV7.8%

AV8.6%

BW AV11.8% AV11.4% AV10.2%

AV9.8%

RW

2500

3000

3500

4000

4500

E/B Distance (ft) 1ft=0.3m

Fig. 5. An example of highlighted contour of predicted AV in the pavement.

different boundaries concerning the hot mix asphalt quality check, different colors are adopted in the Figure: white indicates the AV is less than 8.5%; gray indicates the AV is between 8.5% and 9.9%; and red indicates the AV is over 9.9%. The x-axis represents the distance in feet along the travel direction. The y-axis represents the distance in 2.5 mm (0.1 in.) across lane or transversely from the outside lane edge. The location for left, between, and right wheel paths across the lane is labeled on the y-axis as LW, BW, and RW respectively. The Figure also includes the calibration AV values from the field cores, marked with circles and measured AV values. Overall, the prediction closely reflects what is revealed from the field investigation. To facilitate illustration, a sub-stretch of pavement from the eastbound is highlighted, as shown in Fig. 5. For some locations, such as the right left corner spot with AV ¼ 10.2%, the prediction is slightly different from the observation. This can be mainly attributed to two reasons: (1) the limitation of the regression equation's prediction capacity due to inevitable variability, and (2) measurement errors between the GPR designated and coring spots. The responsible district had used the contour maps to determine the areas for removal and replacement. An effort was made to determine the air void for the entire pavement section in study. Fig. 6(A) shows the cumulative air void distribution, which indicates approximately 40% areas are in the “remove and replace” category. Fig. 6(B) illustrates the histogram with the majority of the air void at 8%. Figs.6(A) and (B) demonstrate the ability of the proposed methodology to characterize the air void condition for 100% of the survey areas. 2.2. Project 2 In this project, a newly constructed HMA overlay is examined. The construction site is located on Interstate Highway (IH) 35, in Laredo District, Texas. Immediately after the overlay construction was completed, many locations at the longitudinal joints appear to be porous. Fig. 7 shows the longitudinal joint conditions. GPR

surveys were carried out on the pavement to characterize density profile. The responsible district would like to know the remedy before planned/scheduled overlay. A total number of 35 cores were taken at the center of the lane, 203 mm (8 in.) offset from joint, and on the joint. As expected, the cores taken on the joint have the highest water absorption and lowest density. The cores taken at the 203 mm (8 in.) offset all met the longitudinal joint density requirement in the 2004 specifications [16]. The laboratory density results indicated that the air voids ranged from less than 4% to 10% (as shown in Fig. 10(A)). For Stone-Matrix Asphalt (SMA), when air void exceed 7%, the current TxDOT specification calls for penalty. Construction joint problems are believed due to poor joint staggering, effects of trench construction, and poor joint compaction on part of the contractor. Trench construction is the process of removing, then rebuilding the existing structure one lane width at a time, allowing little latitude for joint staggering or proper density control along the mat free edge. These problems resulted in open permeable construction joints that detrimentally allow moisture to infiltrate into the pavement structure. GPR measurements along and within the vicinity of the construction joints detected the presence of both low density spots and moisture entrapment. As the result of the investigation, responsible district had placed a strip seal along the SMA longitudinal joints to minimize water intrusion. After four years of the placement, the strip seal works well as no visible distress can be found along the longitudinal joint. GPR dielectric measurements and core density results from this project are to be combined with other projects to develop mathematical equation to predict air void. 2.3. Project 3 In this project, an approximately 14.48 km (9 miles) segment of pavement on IH 30 located in Paris District was examined with the GPR. It was indicated that the underlying highway with two lanes in each direction had been experiencing very severe rutting. The annual average daily traffic was 24,610 in 2010, which included 26% trucks. The estimated accumulated Equivalent Single Axle Load (ESAL) in the future 20 years from 2011 for one direction was around 33 million. Rutting is one of the critical elements in pavement performance evaluation. The main reason is that rutting can detrimentally affect the driving safety due to hydroplaning in wet weather conditions. Rutting usually manifests itself as the permanent deformation on the wheel paths of a pavement although it can occur at other locations of a lane. An HMAC overlay was on the top of pavement used to directly sustain traffic, as shown in Fig. 8(A). During the construction in 1997, a layer of Type B AC with thickness of 90 mm (3.5 in.) was laid first and one surface treatment course (with negligible thickness)

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Cummulative Percentage

100% 90% 80% 70% 60%

Remove & Replace

50% 40% 30% 20% 10% 0% 0%

2%

4%

6%

8%

10%

12%

14%

16%

Air Void

50% EB

40%

Percentage

WB

30%

20%

10%

0% 2%

4%

6%

8%

10%

12%

More

Air Void Fig. 6. Cumulative air void and Histogram FM 917.

Longitudinal Joint Longitudinal Joint

Fig. 7. New HMAC on IH35 with many locations appear to be porous along the longitudinal joint.

was applied on the top. Then a layer of Type D HMAC with thickness of approximately 50 mm (2 in.) was constructed at the surface. It is noted that this material has been in service for approximately 14 years at the time of data collection in 2011. Partially because of the heavy traffic, rutting was the major distress on the pavement, as shown in Fig. 8(B). Before the HMAC overlay, the pavement was constructed with 200 mm (7.87 in.) Continuous Reinforced Concrete (CRC) on top of 150 mm (5.9 in.) cement treated base. Fig. 9 shows a typical rutting profile for IH30. Both directions, eastbound and westbound were surveyed. Because only cores from the eastbound were available, the GPR data from eastbound pavement was used in the study. Due to very

high traffic volume during the data collection, for safety consideration, only five cores were taken for calibration purpose. It is well known that severe rutting can be due to low voids (ie, less than 3.5%) or high (ie, high than 8.5%) air voids. The cause of severe rutting due to air voids has been documented in the literatures [7,8]. The rutting can be due to the densification of pavement materials when the air voids are too high and/or shearing of materials when the air voids are too low. [13] concluded that the rutting of asphalt concrete as a function of air void content. The effects of air void content on permanent strain were found to be very significant, and rutting increases with increasing air void content. [15] reported that when air void

D.-H. Chen et al. / NDT&E International 68 (2014) 120–127

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Fig. 8. HMAC overlay condition for IH30: (A) overview and (B) rutting on outside lane.

Edge

Table 1 Model parameters Eq. (4).

Distance from Edge (feet) 10

8

6

4

2

0 0

Variable

Parameter

Mean

t-statistic

0.2

Intercept Dielectric Section 2 Section 3 Section 4 Section 5 R2

γ0 γ1 γ2 γ3 γ4 γ5 0.62

0.58  0.60  0.35 0.68 0.36  0.36

1.48  8.49  3.55 6.28 3.82  2.43

0.4 0.6 0.8 1

Rut Depth (inch)

12

study there are five sections with different site conditions, five variables are adopted herein. To avoid multicollinearity in the model estimation, the site factor for Section 1 is used as the reference. The model specification is established in the following.

1.2 1.4

Fig. 9. Examples of rut profile from IH30.

lnðAV Þ ¼ γ 0 þ γ 1 E þ γ 2 S2 þ γ 3 S3 þ γ 4 S4 þγ 5 S5 þε0

contents range from 4.5% to 8% they did yield good rutting resistance. Rutting resistance reduces with air void content above or below this range.

3. Mathematical modeling of in-place air Voids This study endeavored to establish correlation between measured dielectric from GPR and lab determined density. Measured dielectrics and densities from three aforementioned projects were grouped together to derive an average trend. As discussed in Project 1, an exponential function could be used to effectively capture the relationship between air voids and dielectric values. To accommodate model estimation, the exponential function is linearized and expressed in the following log transformed equation: lnðAVÞ ¼ ln β0 þ β1 E þ ε ¼ α0 þ α1 E þ ε

ð3Þ

where, AV is the in-place air void content (%), E is the dielectric constant, α0 , α1 ,β0 and β1 are the parameters to be estimated, and ε is the error term. It is noted that an error term is used to capture the remaining factors in modeling air void. This means, in addition to the dielectric, other factors can affect the accuracy of the prediction of the air void, which may include model error, variation in materials, environment such as ambient temperature, subgrade support, and others. In addition to the model error, the others can be included in the model as explanatory variables, which will increase model fitting accuracy. However, those variables are unavailable in the underlying study. In order to reduce the significance of the error, a section factor is introduced to represent the section-to-section variation. Considering the fact that in this

ð4Þ

where, A V is the in-place air void content (%), S2 –S5 are the section factors, it is either 1 or zero. It equals to 1 corresponding to each section; section 1 serving as the reference is embedded in the intercept term, γ 0  γ 5 are the parameters to be estimated, and ε0 is the error term. Based on the available core information from the three projects with five sections, a sample size of 92 is used in the model ing analysis. Through regression analysis based on linear model Eq. (4), the model estimation results are shown in Table 1. First of all, the R2 being equal to 0.62 suggests that the proposed model fit the data fairly accurately. To facilitate understanding, Fig. 10 depict show the curves from the proposed models fit the relationship between air voids and dielectric for two projects: FM917 and IH35. Fig. 11 includes all data points involved in this study and presents how close the observed and fitted points are close to the equality or 451 line. Second, the parameter estimate for variable dielectric deserves a discussion. The absolute value of its t-statistic larger than 1.96 suggests that γ 1 is statistically significant at a 95% confidence level. The negative sign implies that with the increase of dielectric the air void decreases. This is consistent with engineering judgment because the dielectric of air, 1 is the smallest and higher dielectric means less air.

4. Conclusions This study applied a non-destructive testing technology, GPR, to determine air voids in hot mix asphalt pavements. Three highway projects with varying service lives were investigated with a goal of developing a mathematical equation for air void prediction. The results demonstrate that GPR could be used

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14% 12%

Air Void

10% 8% 6% 4% 2% 0% 3

4

5

6

7

8

8

9

Dielectric Obs

Fit Curve

16% 14%

Air Void

12% 10% 8% 6% 4% 2% 0% 4

5

6

7 Dielectric

Obs

Fit Curve

Fig. 10. Examples of observed vs. model fitted air voids: (A) Section 1 from IH35 and (B) Section 1 from FM917.

14%

Line of Equality Fitted AV

12% 10% 8% 6% 4% 2% 0% 0%

2%

4%

6%

8%

10%

12%

14%

Observed AV Fig. 11. Observed vs. fitted air void values by the model.

efficiently to identify areas with high air voids. Based on 92 cores retrieved from three field projects, the relationship between the dielectric and air voids was established. The responsible districts

had used the air void contour maps to make rehab decisions. The advantage of nondestructive testing is that it provides a comprehensive evaluation of subsurface conditions throughout the entire

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