Evaluation of Non-nuclear Density Gauges for Measuring …docs.trb.org/prp/13-3779.pdf · Evaluation of Non-nuclear Density Gauges for Measuring In-place Density of Hot Mix Asphalt

Evaluation of Non-nuclear Density Gauges for Measuring In-place Density of Hot Mix Asphalt

Anthony Timm

Graduate Research Assistant Washington State University

Department of Civil and Environmental Engineering Pullman, WA 99164

Email: [email protected]

Haifang Wen, Ph.D., PE Assistant Professor, Director of

Washington Center for Asphalt Technology (WCAT) Department of Civil and Environmental Engineering

Washington State University Spokane Street, Sloan Hall Room 41

Pullman WA, 99164 (509) 335-7632

Email: [email protected] (Corresponding Author)

Sunil Sharma, Ph.D., P.E., Professor

Department of Civil and Environmental Engineering University of Idaho

Box 441022 Moscow, ID 83844-1022

Tel: (208) 885-6403 Fax: (208) 885-6608


Mark Rose Graduate Research Assistant Washington State University

Department of Civil and Environmental Engineering Pullman, WA 99164


Submitted for Publication in the Transportation Research Record: Journal of the Transportation Research Board

Word Count: (4,244 text + 10 figures*250 + 3 tables*250 = 7,494 words)

TRB 2013 Annual Meeting Paper revised from original submittal.

ABSTRACT Ensuring that the hot mix asphalt (HMA) mat is compacted uniformly to an adequate density is critical to the performance of a pavement structure. Nuclear density gauges (NDGs) have been used for many years in pavement construction as a method of nondestructive density testing. Non-nuclear density gauges (NNDGs) perform the same function as NDGs. However, because NNDGs does not contain a nuclear source material, they are not subject to the same extensive fees and regulations as NDGs. Field and laboratory test results for two NNDGs, the Troxler PaveTracker and Transtech PQI 301 (referred to simply as the PQI in this paper), are analyzed in this paper to determine if NNDGs can serve as viable replacements for NDGs. Several potential factors that could affect the accuracy of NNDGs are examined. Global factors, such as aggregate mineralogy, the nominal maximum size of aggregate, HMA class, and aggregate absorption, are shown not to affect NNDGs stastistically significantly. Local factors, such as surface markings, likewise are shown not to have a statistically significant effect on NNDG readings. However, temperature and moisture are shown to affect NNDG readings. Overall, both the PaveTracker and PQI devices appear to be valid replacements for NDGs for quality assurance purposes, provided that the temperature and moisture conditions are met.


INTRODUCTION In-place density is the most important factor in the performance of dense-graded pavements (1). Determining the in-place density of hot mix asphalt (HMA) using extracted cores is time consuming, destructive, and costly. Therefore, many agencies utilize nuclear density gauges (NDGs), which measure scatter from gamma radiation, for rapid, nondestructive density readings in the field. However, nuclear material is heavily regulated, and extensive training and permits are required in order to operate NDGs. Also, both the monitoring of radiation level during operation, storage and transportation of NDGs are inconvenient and expensive. Furthermore, NDGs sometimes are unable to be brought onto federal and/or military property.

Non-nuclear density gauges (NNDGs), however, are not subject to such stringent regulations and are lightweight and easier to handle than NDGs. NNDGs determine HMA density by measuring the electrical impedance at a chosen frequency of alternating current (2). The ability of the HMA to store electrostatic energy per unit volume is called the dielectric constant, and is determined from the electrical impedance. The dielectric constant of air is 1, whereas that of HMA (aggregate and binder) is 5 to 6. Overall, the constant of an entire HMA mat is a weighted (by volume) average of the air and HMA constants (2, 3). NNDG readings have been shown in previous research to be affected by a variety of factors, including the gradation of the HMA aggregate, aggregate source, lift thickness, temperature, gauge orientation, presence of moisture, surface debris, and surface markings (3, 4, 5, 6). The PQI device has been found to be acceptable for quality assurance (QA) purposes by several studies (5, 7, 8, 9, 10). The PaveTracker, however, is found not to be suitable for QA purposes by any of the studies reviewed; it is deemed suitable for quality control (QC) work in several studies (4, 5, 6, 11).

Considering the associated complications as well as recent advances in developing NNDGs, there is a need to re-evaluate these NNDGS for possible use during construction by highway agencies or contractors. The objective of this study is to determine if available NNDGs are adequate in determining the in-place density of HMA for acceptance. Commercially available NNDGs are evaluated in this study based on laboratory and field testing and statistical analysis. The performance of NNDGs is compared to that of NDGs currently used for asphalt paving projects, specifically in terms of replacing the current inventory of NDGs. Two NNDGs, the TransTech PQI 301 (referred to simply as PQI in this study) and the Troxler PaveTracker (referred to simply as PaveTracker in this study), are chosen for testing in this research. Both devices operate on the same principles (3). RESEARCH APPROACH AND TEST METHODS The factors to be evaluated include two types of factors, global and local. Global factors vary only between different paving operations and include aggregate mineralogy and absorption, the HMA class for different traffic levels, the nominal maximum aggregate size (NMAS), and lift thickness. Local factors are those that vary within one paving operation, such as temperature of HMA, presence of moisture, use of markings, and presence of sand/debris. The test protocols for NDG and NNDGs followed the Idaho Transportation Department WAQTC TM-8 and AASHTO TP 68 Method C, respectively. Field Experiments Field testing was performed in order to examine the effects of both global factors (e.g., aggregate source) and local factors (e.g., surface fines). HMA field testing was conducted using the PQI


and PaveTracker NNDGs, as well as an NDG, as shown in Figure 1. PQI readings can be taken in either continuous mode or average mode. In continuous mode, the PQI device stays at one location where readings are taken. In average mode, five PQI readings are taken: an initial reading is taken at a desired location, and a circle is drawn along the perimeter of the circular base of the PQI device. Then, four more readings are taken on the drawn circle with measurements 90º from each other. The average of the five readings is used as the density of the materials within the circle. Two PaveTracker readings, with the gauge rotated 180° between readings, are averaged. Both the PQI and PaveTracker require mix-specific calibration. Typically, the NDG and NNDGs read the density at five locations. Cores are taken and the density of each core is determined by AASHTO T 166 which is considered the true density of the pavement. The average density of the cores is used to calibrate the NDG and NNDGs by inputting the difference between core densities and gauge readings.

The field test procedures for this study are as follows:

1. Input the mix design and pavement mix data to the device. 2. Test the five calibration locations in average mode using the NNDGs without fines

(passing the #200 sieve). 3. Test the calibration locations in average mode using the NNDGs with fines (Figure 2). 4. Test the calibration locations with fines from the NDG tests. 5. Brush off the fines, apply surface markings (Figure 3), and take readings from the

NNDGs. 6. Extract cores for the laboratory determination of the cores that are needed for calibration. 7. Test additional locations (up to seven) using the NDG and NNDGs and take cores for

verification.

Figure 1. PQI, PaveTracker and NDG

PQI

PaveTracker

Nuclear Density Gauge


Figure 2. Surface Fines

Figure 3. Application of Markings

Field testing was conducted at paving projects throughout Idaho. The PQI and PaveTracker readings were taken at all field project locations, except during part of the paving season when the PaveTracker malfunctioned. Field test data obtained from previous Idaho Transportation Department (ITD) tests using the PQI also are included in the analysis of the PQI data. The ITD project information, which is included in this study, is shown in Table 1. For the category of HMA Class, the high class numbers indicate a high traffic level.

Table 1. Field Project Information

Project HMA Class Lift

Thickness (in.)

NMSA (in.) Aggregate

Mineralogy

Aggregate Absorption

(%) SH-51 3 1.8 0.5 Gravel 1.4 I-84 6 2.7 0.75 Granite 1.3 SH-8 4 1.8 0.5 Basalt 2.6 I-90 5 2 0.5 Gravel 0.1

US-12 2 2.4 0.75 Basalt 2.2 US-95 Frontage 3 1.2 0.5 Gravel 0.9 US-95 Lewiston 5 1.92 0.75 Basalt 1.5 Beaver Creek Rd 3 3 0.75 Gravel 1.3 US-95 Wilder 4 1.9 0.5 Gravel 0.34

SH-37 2 1.8 0.5 Gravel 1.17


Laboratory Experiments Due to the busy paving schedule on the job site, it was difficult to conduct a detailed evaluation of the NNDGs in terms of the local factors, such as temperature and moisture in the field. Therefore, laboratory testing was conducted to examine the effects of local factors on the NNDG readings. HMA slabs were compacted from loose mix collected in the field using a vibratory plate compactor in a 21.63 in. × 23.88 in. × 2 in. steel mold, as shown in Figure 4. Once the HMA was compacted into the mold as evenly as possible, the plate compactor was removed, and the steel sides were unbolted from around the sample to prevent interference with the gauge readings. Fines were used to fill any surface voids that resulted from uneven compaction by the plate compactor, and the slabs were allowed to cool at room temperature. Readings were taken as the slab cooled.

After the temperature study was completed, the slab was then flipped over to perform moisture testing. The bottom of the sample was compacted against a smooth metal plate at the bottom of the mold, resulting in a smooth even surface without the need for added fines. Water was applied to the specimen from a small spray bottle, as shown in Figure 5, and a reading was taken. Water was applied to simulate the water deposited on the pavement by rollers. Additional water was applied before another reading was taken and so on. Due to the need to add moisture between readings, the gauge location was marked in order to place the gauge as close as possible to its original location and orientation to minimize procedural error. Once the surface was completely flooded, readings were taken as the water drained into the HMA in order to test the effects of internal water on the gauge readings.

Figure 4. Laboratory Compaction of HMA Slab


Figure 5. Application of Moisture

RESULTS AND ANALYSIS The field and laboratory experimental results were analyzed to evaluate the effects of global and local factors on the NNDG readings. Global Factors Correlation Results The calibration of NNDGs was conducted by determining the difference between uncalibrated NNDG readings and core density. This difference was then added to future NDG and NNDGs readings to obtain the calibrated readings. These calibrated readings were then compared to the measured density values of the additional cores for their respective locations. The NDG and NNDG data were compared to the core density values to compare the accuracy of the NDG and NNDGs. The correlation between the gauge readings and the core density values were then determined using a linear correlation with an intercept of zero. Figures 6 and 7 show the correlation results for the PQI and PaveTracker, respectively.

Correlations for both the NDG and NNDG readings have a slope very close to 1.00. Without surface fines, the PQI readings correlated with the core density (R2 = 0.85) as well as the NDG (R2=0.84) for all calibrations. However, when fines are used, the PQI readings exhibit a low correlation (R2 = 0.60). The PaveTracker readings without fines correlated with the core density (R2 = 0.83) as well as the NDG readings correlated with the core density (R2 = 0.84). With the use of fines, the PaveTracker shows a slightly lower correlation with the core density (R2=0.81) than the PaveTracker without fines and the NDG.


Figure 6. PQI Results vs. Core Density

Figure 7. PaveTracker Readings vs. Core Density

PQINo Finesy = 0.995xR² = 0.85

PQI With Finesy = 0.997xR² = 0.60

NDGy = 0.988xR² = 0.84

130

135

140

145

150

155

160

130 135 140 145 150 155 160

Gau

ge Read

ing (pcf)

Core Density (pcf)

PQI vs NDG (after Calibration)

PQI no fines

PQI with fines

NDG

Linear (PQI no fines)

Linear (PQI with fines)

Linear (NDG)

PQINo Finesy = 1.00xR² = 0.83

PQIWith Finesy = 1.01xR² = 0.81

NDGy = 0.99xR² = 0.84

138

143

148

153

158

163

138 143 148 153 158

Gau

ge Read

ing (pcf)

Core Density (pcf)

PaveTracker vs NDG (after Calibration)

PT no fines

PT with fines

NDG

Linear (PT no fines)

Linear (PT with fines)

Linear (NDG)


General Linear Model (GLM) Statistical Analysis Statistical analysis was performed for each NNDG to determine whether any global factors had a statistically significant effect on the results of the NNDG testing. A multiple regression model was conducted, using the Minitab 16 program, to determine the effects of the global factors, including pavement thickness, aggregate mineralogy, aggregate absorption, HMA class, and NMSA. Three aggregate types, granite (W1), gravel (W2), and basalt (W3) were present within the data set and were used as categorical indicator variables within the model. Aggregate size X1 (in.), lift thickness X2 (in.), HMA class X3 (1-6), and aggregate absorption X4 (%), are considered quantitative rather than categorical variables. Due to the limited number of field projects, the only possible two-way interactions, Wi×X3 and Wi×X4, are included in the statistical model. The model used is: Y=β0 + β1W1+β2W2+β3X1+β4X2+β5X3+β6X4+β7W1X3+β8W2X3+β9W1X4+β10W2X4+ϵ (1) where βi = regression parameters,

ϵ = error. This model is based on the two assumptions that the data are normally distributed and

have constant variance. The basalt response is used as a baseline for the aggregate responses. In order to assess the appropriateness of the model assumptions, graphical analysis of the normal probability plots of the residuals as well as residuals versus fitted values was performed. The resultant plots do not dispute the model assumptions. The data were analyzed at 95% confidence, with two-sided confidence intervals. The data were fit without the intercept, and hypothesis tests of H0:{βi = 0} vs Ha: {βi ≠ 0} were conducted.

PQI GLM Analysis. The resulting regression equation for the PQI is: E1 = 1.49W1+2.39W2-0.32X1+0.54X2+0.32X3-0.55X4-0.51W1*X3-0.85W2*X3+0.21W2*X4 (2)

The fit of the model to the data was analyzed numerically via the standard and adjusted R2 values of 65.19% and 60.89%, respectively. Based on the R2 values and the diagnostics run on the data, including normal probability plots and plots of residuals, the model appears to be able to estimate appropriately the effects of the global factors on the PQI readings. The null hypotheses being retained indicate that none of the global factors affects the response with statistical significance at 95% confidence level. This result is expected because the readings have been corrected using field core density values, which should eliminate the effects of global factors on gauge readings.

PaveTracker GLM Analysis. Due to the lack of data caused by the malfunction of the PaveTracker in several of the projects, only the main factors are included in the model. The basalt response is used as the baseline, and the other aggregate types are considered “non-basalt.” The regression equation for the PaveTracker is: E1 = -0.78 W2 - 3.09 X1 + 3.44X2 - 0.66 X3 - 0.83 X4 (3)


The fit of the model to the data was analyzed numerically via the standard and adjusted R2 values of 62.94% and 58.19%, respectively. The model appears to be a reasonable fit. No global factors were found to affect the PaveTracker significantly. This result was expected, as the results for each project were calibrated using field cores. The results again indicate that the calibration process is critical. Local Factors The effects of markings (paint), temperature and moisture (i.e., local factors) on the HMA surface also were examined. The percentages of error of the gauge readings based on the core density values also were analyzed for local factors in order to compare the accuracy of the gauges when local factors are present. Surface Markings (Paint) Presence of pavement markings, such as paint, could potentially affect NNDG readings in the field. Tables 2 and 3 show the NNDG readings with and without surface markings for the PQI and PaveTracker, respectively. Student t-tests were performed comparing the calibrated gauge readings with the core density values from that location to determine if the readings differed from the core density values with statistical significance. A two-tailed, paired value t-test with 95% confidence was used. A p-value from the t-test of less than 0.05 was used as the significance level. The p-values of the paired student t-tests of the percentages of error of the PQI and PaveTracker are 0.17 and 0.20, respectively. Both values are greater than 0.05, indicating that surface paint has no significant effect on gauge accuracy with 95% confidence. Temperature Readings were taken continuously by the NNDGs as the slabs cooled in the laboratory. It is unknown at which temperature the NNDGs would read the density values most accurately because NNDG readings need to be corrected using field core density values. The correction factor between the NNDG readings and field core density values depends on the temperature at which the NNDG readings are taken. Therefore, only the relative effect of temperature on the readings was studied. All of the readings at different temperatures were subtracted by the reading at 100ºF as a reference.

Figures 8 and 9 show the effect of temperature on the NNDG readings. Overall, as the temperature drops, the readings increase. If a criterion of ±1 pcf is used as the tolerance level, which is slightly less than the 1.5 pcf repeatability level for the NDG and NNDGs (12), it seems that the readings from the NNDGs meet the criterion when the temperature is 90ºF or higher for both the PQI and PaveTracker. Therefore, it is recommended that NNDG readings should be taken when the temperature is 90ºF or higher.


Table 2. PQI With and Without Surface Paint

Project Without

Paint (pcf)

With Paint (pcf)

Core Density

(pcf)

Percentage of Error Without

Paint

Percentage of Error

With Paint

I-84 143.60 143.70 141.30 1.63 1.70

143.30 143.50 145.10 1.24 1.10

SH-51

139.60 140.30 137.60 1.45 1.96

140.50 140.60 140.20 0.21 0.29

140.30 140.30 138.90 1.01 1.01

140.20 140.50 138.90 0.94 1.15

I-90

144.47 144.77 142.95 1.06 1.27

144.07 144.77 144.62 0.38 0.10

144.67 144.87 144.78 0.08 0.06

145.07 145.37 146.18 0.76 0.56

145.07 145.37 144.28 0.55 0.75

SH-8

153.10 152.70 152.18 0.60 0.34

150.00 152.50 150.26 0.17 1.49

147.90 147.80 147.73 0.12 0.05

141.80 141.80 145.64 2.64 2.64

US-95 Wilder Phase 1

135.87 135.57 138.31 1.77 1.99

136.67 136.47 140.92 3.02 3.16

136.37 136.47 136.97 0.44 0.37

136.17 134.97 140.22 2.89 3.75

135.77 136.17 139.66 2.79 2.50

p-value from t-

test 0.17

Moisture The effects of moisture on the surface of the test specimens as well as the effects of water inside the specimens were studied in the laboratory. The readings taken under dry conditions were used as the reference and were subtracted from the readings when the surface was wet. The PQI device reports the H2O index as an indicator of moisture level, whereas the PaveTracker does not. Therefore, the H2O index reported by the PQI is used for both the PQI and PaveTracker. Figures 10 and 11 show the relationship between the change in H2O index (ΔH2O) and the change in readings (ΔPQI or ΔPaveTracker readings). It is seen that an increase in moisture level significantly reduces the PQI readings. However, the relationship between the ΔH2O index and the ΔPaveTracker readings is scattered. In either case, the deviation from the readings taken under dry conditions could be up to 18 pcf for the PQI and 21.5 pcf for the PaveTracker when the surface is flooded. Therefore, it is recommended that NNDG readings are taken under dry conditions.


Table 3. PaveTracker With and Without Surface Paint

Project Without

Paint (pcf)

With Paint (pcf)

Core Density

(pcf)

Percentage of Error Without

Paint

Percentage of Error

With Paint

I-84 144.73 144.18 141.30 2.43 2.04

143.68 144.28 145.10 0.98 0.57

SH-51

136.33 137.13 137.60 0.92 0.34

139.23 138.53 140.20 0.69 1.19

137.73 135.93 138.90 0.84 2.14

138.38 138.23 138.90 0.37 0.48

I-90

141.20 143.25 142.95 1.22 0.21

143.35 142.75 144.62 0.88 1.29

145.00 143.40 144.78 0.15 0.95

146.30 147.55 146.18 0.08 0.94

146.30 147.55 144.28 1.40 2.27

SH-8

156.62 157.72 152.18 2.92 3.64

154.67 153.97 150.26 2.93 2.47

149.17 151.72 147.73 0.97 2.70

145.62 138.32 145.64 0.02 5.03

US-95 Wilder Phase 1

137.10 137.60 138.31 0.88 0.52

137.40 138.60 140.92 2.50 1.65

136.50 138.90 136.97 0.34 1.41

136.50 138.30 140.22 2.65 1.37

136.70 136.50 139.66 2.12 2.26

p-value from t-

test 0.20

Figure 8. Effect of Temperature on PQI Readings


Figure 9. Effect of Temperature on PaveTracker Readings

Figure 10. Effect of Moisture on PQI Readings

y = ‐0.15x ‐ 1.62R² = 0.85

‐20.00

‐18.00

‐16.00

‐14.00

‐12.00

‐10.00

‐8.00

‐6.00

‐4.00

‐2.00

0.00

0.00 50.00 100.00

ΔPQI Read

ing (pcf)

ΔH20 Index

ΔH2O Index vs ΔPQI Reading

Calibrated PQI Readings

Linear (Calibrated PQI Readings)


Figure 11. Effect of Moisture on PaveTracker Readings

CONCLUSIONS AND RECOMMENDATIONS The service life of asphalt pavement is affected greatly by the density of the HMA layer, which is often inspected using an NDG. However, the use of NDGs involves strict regulations, licensing, storage, and transportation requirements. This study evaluates the effectiveness of NNDGs in possibly replacing NDGs. Two NNDGs were evaluated in the field and laboratory. This study finds that both the PQI and PaveTracker NNDGs can measure HMA density as well as NDGs measure HMA density. However, unlike NDGs, no surface fines are recommended for use on the HMA surface. None of the global factors, including aggregate mineralogy, aggregate absorption, NMSA, HMA class, and lift thickness, is found to affect the readings of the PQI and PaveTracker; nor does the existence of surface markings (a local factor) statistically affect the readings. However, temperature (another local factor) is found to affect the readings slightly. Based on a tolerance level of ±1.0 pcf, it is recommended to use PQI or PaveTracker when the HMA temperature as read by the device is 90°F or higher. In addition, moisture (also a local factor) is found to affect the readings and density values significantly and should be measured when the HMA is dry. Based on the laboratory and field tests performed using the two gauges, it is concluded that both the PaveTracker and PQI appear to be valid replacements for NDGs for QA purposes, provided that the temperature and moisture conditions are met. Further research is needed to account for the effects of temperature and moisture on the readings of the NNDGs to make the device more universally applicable to different field conditions. ACKNOWLEDGEMENTS The contents of this paper, funded by the Idaho Transportation Department (ITD) and the Federal Highway Administration(FHWA), reflect the views of the authors, who are responsible for the facts and accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the ITD or FHWA. This paper does not constitute a standard, specification, or regulation, and is being submitted prior to ITD’s acceptance of the final report.

y = 0.042x + 2.96R² = 0.032

‐10.00

‐5.00

0.00

5.00

10.00

15.00

20.00

25.00

0.00 50.00 100.00

ΔPaveTracker Read

ing (pcf)

ΔH20 Index

ΔH20 Index vs ΔPaveTracker Reading

Calibrated PaveTracker Readings

Linear (Calibrated PaveTracker Readings)


The authors wish to thank Mr. Clint Hoops, Mr. Mike Santi, Mr. Ned Parrish, and other field staff for their support. REFERENCES 1. Lenz, R. Texas Department of Transportation Pavement Design Guide.

http://onlinemanuals.txdot.gov/txdotmanuals/pdm/manual_notice.htm. Updated January 1, 2011. Accessed July 11, 2012.

2. Allen, D., D. Schultz, and D. Willett. Evaluation of Non-Nuclear Density Gauges. Kentucky Transportation Center, August, 2003.

3. William, S. Non-Nuclear Methods for HMA Density Measurements. Publication MBTC 2075, Final Report, University of Arkansas, 2008.

4. Romero, P. Evaluation of Non-Nuclear Gauges to Measure Density of Hot-Mix Asphalt Pavements. Univ. of Utah, Dept. of Civil and Environmental Engineering, July, 2002.

5. Sargand, S., K. Sang-Soo, and S. Farrington. A Working Review of Available Non-Nuclear Equipment for Determining In-Place Density of Asphalt. Ohio Research Institute for Transportation and the Environment, December, 2005.

6. Schmitt, R. L., C. Rao, and H. L. Von Quintas. Non-Nuclear Density Testing Devices and Systems to Evaluate In-Place Asphalt Pavement Density. Report 06-12; SPR 0092-05-10, Wisconsin Department of Transportation, 2006.

7. Sebesta, S., M. Zeig, and T. Scullion. Evaluation of Non-Nuclear Density Gauges for HMAC: Year 1 Report. Publication FHWA-TX-04-0-4577-1, Texas Transportation Institute, 2003.

8. Sully-Miller. A Summary of Operational Differences between Nuclear and Non-nuclear Density Instruments. Sully-Miller Contracting Company, October, 2000.

9. Cho, Y. et al. Non-Nuclear Method for Density Measurements. Publication SPR1 (10) P335, The Charles W. Durham School of Architectural Engineering & Construction, 2011.

10. Ziari, H., H. Behbahani, A. Izadi, and H. Divandary. Field Evaluation of Nondestructive Tests in Measuring the Pavement Layers Density. Journal of Applied Science, Volume 10, 2010, pp. 3091-3097.

11. Mason, M. and R. Williams. Investigation of Electromagnetic Gauges for Determination of In-place Density of HMA Pavements. IHRB Project TR-576, Iowa State University Institute for Transportation, May, 2009.

12. Von Quintus, H. L. et al. NDT Technology for Quality Assurance of HMA Pavement Construction. NCHRP Report 626, Transportation Research Board, Washington, D.C., 2009.


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Evaluation of Non-nuclear Density Gauges for Measuring …docs.trb.org/prp/13-3779.pdf · Evaluation of Non-nuclear Density Gauges for Measuring In-place Density of Hot Mix Asphalt