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51 laboratory but was estimated to be similar to the US-2 HMA 2.3.1 NDT Devices for Unbound Layers base with the PSPA (see Table 25). The reason for the large dif- ference between the laboratory and field deviation from unity 2.3.1.1 Variability of Response Measurements for this one mixture is unknown. Conversely, the FWD adjust- Figures 23 through 26 compare the COV to the average ment factors are significantly different from unity. The FWD modulus measured by each device. All COV point com- overestimated the SMA modulus for the overlay project and parisons were for the same test area. Thus, the material underestimated the HMA base modulus for the reconstruction variance should be the same between the different NDT projects suggesting that the calculated values from the deflec- devices. tion basins are being influenced by the supporting materials. The GeoGauge consistently had the lower COV, and that On the average, the PSPA can be used to estimate the value decreases with increasing material stiffness (Figure 26). dynamic modulus measured in the laboratory HMA mixtures, The variations of the GeoGauge measurements were found to while the FWD was found to be extremely variable. The PSPA be less dependent on type and size of aggregate, as well as less ratios are variable, but that variability is less than the ratios dependent on the underlying materials for the thicker layers for the unbound materials. These ratios were compared to tested. The reason for the higher COV values for the other the binder type, gradation, and other volumetric properties devices is that the DCP penetration rate is dependent on the but no relationship was found. It is suggested that dynamic amount and size of coarse aggregate particles, while the modulus tests be performed to determine the target or design LWD modulus values are more dependent on the under- value and that those results be used to calibrate the PSPA for lying materials. The DSPA is dependent on the water content a specific mixture. The dynamic modulus test can be performed variations nearer the surface (water content-density gradi- on bulk mixture compacted to the expected in-place density ents) and the amount of fines in coarse-gained materials. during the mixture verification process or during construction The DSPA had higher variability when testing stiff mate- of a control strip. rials that had water contents significantly below the opti- mum value or where the surface had been primed. Some 2.3 Accuracy and Precision layers tested had a significant modulus gradient near the sur- Important parameters in QA are the accuracy and precision face, which had a much larger effect on the DSPA responses. of a test method. The higher the precision of a test method, Some sites had a positive gradient (modulus increases with the fewer tests need to be completed at some confidence level depth), while other sites had a negative gradient. Those sites for estimating properties of the population or lot and making with positive modulus gradients generally had higher adjust- the "right" decision regarding the quality of the lot. This section ment ratios, while those with negative gradients had lower evaluates and compares the variability measured within the ratios. These modulus gradients were confirmed with the field evaluation projects with different NDT devices. The more DCP--the only device that could readily measure these gra- precise result, however, does not automatically imply that dients in real time. Figure 27 shows some examples of the the test method can identify physical differences or informa- change in modulus with depth, as calculated from the pene- tion about the population related to performance. tration rate (see Equation 16). Fine-Grained Coarse-Grained 70 Coefficient of Variation, % 60 50 40 30 20 10 0 0 10 20 30 40 50 60 Mean Elastic Modulus, DCP, ksi Figure 23. Coefficient of variation versus the mean modulus calculated from the DCP penetration rates.

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52 Fine-Grained Coarse-Grained Log. (Coarse-Grained) 90 Coefficient of Variation, % 80 70 60 50 40 30 20 10 0 0 10 20 30 40 50 60 Mean Elastic Modulus, LWD, ksi Figure 24. Coefficient of variation versus the mean modulus calculated from the LWD deflections. Fine-Grained Coarse-Grained 90 Coefficient of Variation, % 80 70 60 50 40 30 20 10 0 0 50 100 150 200 250 Mean Elastic Modulus, DSPA, ksi Figure 25. Coefficient of variation versus the mean modulus determined from the DSPA responses. Fine-Grained Coarse-Grained Log. (Coarse-Grained) 30 Coefficient of Variation, % 25 20 15 10 5 0 0 10 20 30 40 50 60 Mean Elastic Modulus, GeoGauge, ksi Figure 26. Coefficient of variation versus the mean modulus determined from the GeoGauge responses.

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53 Ohio Crushed Stone North Dakota Crushed Stone Florida Limerock Base 60 Calculated from DCP Penetration Rate, ksi Resilient Modulus 50 40 30 20 10 0 2 4 6 8 10 12 14 Depth Below Surface, inches (a) Aggregate Base Materials/Layers. Oklahoma High PI Clay North Dakota Embankment 25 Calculated from DCP Penetration Rate, ksi Resilient Modulus 20 15 10 5 0 0 2 4 6 8 10 12 14 Depth Below Surface, inches (b) Subgrade and Embankment Materials/Layers. Figure 27. Modulus gradients in unbound layers, as determined with the DCP. 0.64 The LWD had higher variability in test results and 292 E R = 17.6 (DPI )1.12 (16) lower success rates. The higher COV value is related to the variability in the underlying layers and their influence on Where: the measured response with the deflection measuring ER = Resilient modulus, MPa. devices, as well as thickness variations of the layer being DPI = Penetration rate or index, mm/blow. evaluated. A constant layer thickness and subsurface con- dition were used. The DSPA was also placed in different directions relative to The variability of the GPR and EDG for measuring the vol- the roller direction for measuring modulus; the other NDT umetric properties (density and fluids content) was found to devices do not have this capability--only an equivalent or be significantly different from each other, as well as from the average modulus value is reported for all directions. Figure 28 agencies' QA data, when available. Both of these devices had compares the difference between the modulus values parallel very poor success rates in identifying physical differences and perpendicular to the roller's direction to the modulus between different sections. The EDG resulted in very low measured parallel to roller direction. For less stiff materials variability in its estimates of dry density and water content (especially fine-grained materials), there is no difference within a specific area or test section. Most of the COV values between the two readings. For stiffer, coarse-grained materials, for both properties were less than 2 percent (see Tables 27 and however, there is a slight bias. The moduli measured parallel 28). Thus, the average values determined at a test point and to roller direction were slightly higher, on the average. This within a test section did not deviate significantly from the difference and bias resulted in a higher COV for the clustered project average that was determined from nuclear density measurements. gauges and/or sand-cone tests.

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54 Coarse-Grained Fine-Grained Zero Residual Line 40 Residual (E, Parallel - E, 30 Perpendicular), ksi 20 10 0 -10 -20 -30 -40 0 10 20 30 40 50 60 70 80 Adjusted Seismic Modulus Parallel to Roller Direction, DSPA, ksi Figure 28. DSPA modulus values measured parallel to roller direction versus the difference between modulus values parallel and perpendicular to roller direction. Conversely, the GPR resulted in high variability of the tion used to convert the dielectric values to dry densities--a dielectric values (see Table 29), as well as for the dry densities. constant water content for all areas within a lot was assumed. The dry densities determined in some areas exceeded 160 pcf As a result, the GPR data interpretation technique needs to be (see Figure 29)--an unlikely value. The reason for the improb- improved to determine the dry density and water content ably high as well as low values within a project was the assump- along the project prior to day-to-day use in QA programs. Table 27. Dry densities measured with the EDG, pcf. Project Identification Area A B C D I-85 Embankment, Silty Clay; Mean, pcf 107.92 108.9 108.6 107.7 Section 1, Before IC Rolling COV, % 1.3 0.5 1.1 1.7 I-85 Embankment, Silty Clay; Mean, pcf 107.2 107.5 108.9 107.2 Section 2, Before IC Rolling COV, % 0.8 0.8 1.1 1.9 I-85 Embankment, Silty Clay; Mean, pcf 108.1 108.2 108.5 108.4 Section 1, After IC Rolling COV, % 1.0 0.5 0.7 0.3 I-85 Embankment, Silty Clay; Mean, pcf 107.4 107.7 108.0 107.6 Section 2, After IC Rolling COV, % 0.5 0.5 0.8 1.3 TH-23 Embankment, Silt-Sand- Mean, pcf 123.9 123.7 124.4 --- Gravel Mix; North Section COV, % 0.4 0.1 1.0 --- TH-23 Embankment, Silt-Sand- Mean, pcf 122.5 122.9 122.9 --- Gravel Mix; South Section COV, % 1.8 1.8 0.8 --- SH-130 Improved Mean, pcf 123.7 123.7 124.9 --- Embankment; Section 1 COV, % 0.3 0.1 0.6 --- SH-130 Improved Mean, pcf 122.6 123.1 122.7 --- Embankment; Section 2 COV, % 2.0 2.0 0.8 --- SH-130 Improved Mean, pcf 123.3 122.3 123.7 Embankment; Section 3 COV, % 1.4 0.1 0.2 TH-23 Crushed Aggregate; Mean, pcf 129.9 129.8 129.8 --- North Section COV, % 0 0 0 --- TH-23 Crushed Aggregate; Mean, pcf 129.8 129.8 129.8 --- Middle Section COV, % 0 0 0 --- TH-23 Crushed Aggregate; Mean, pcf 129.8 129.9 129.8 --- South Section COV, % 0.1 0.1 0 --- US-280 Crushed Stone; Section Mean, pcf 147.4 1 COV, % 0.7 US-280 Crushed Stone; Section Mean, pcf 148.8 2 COV, % 0.3 US-280 Crushed Stone; Section Mean, pcf 145.9 3 COV, % 0.5 US-280 Crushed Stone; Section Mean, pcf 148.2 4 COV, % 0.3 Note: The shaded cells designate those areas with anomalies (refer to Table 14); the black cells denote the weaker areas, while the gray cells denote the stronger areas tested within a specific project.

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55 Table 28. Water content measured with the EDG, percent. Project Identification Area A B C D I-85 Embankment, Silty Clay; Mean, % 16.9 16.8 16.9 16.9 Section 1, Before IC Rolling COV, % 0.8 0.3 0.3 1.0 I-85 Embankment, Silty Clay; Mean, % 16.9 16.9 16.8 17.0 Section 2; Before IC Rolling COV, % 0.7 0.3 0.3 1.5 I-85 Embankment, Silty Clay; Mean, % 16.9 16.9 16.9 16.9 Section 1, After IC Rolling COV, % 0.5 0.3 0.4 0 I-85 Embankment, Silty Clay; Mean, % 17.0 16.9 16.9 16.9 Section 2, After IC Rolling COV, % 0.5 0.3 0 0.7 TH-23 Embankment, Silt-Sand- Mean, % 8.0 8.0 7.6 Gravel Mix; North Section COV, % 5.1 1.1 11.9 TH-23 Embankment, Silt-Sand- Mean, % 9.8 8.7 7.6 Gravel Mix; South Section COV, % 7.5 7.3 15.8 SH-130 Improved Mean, % 8.1 8.05 7.23 Embankment; Section 1 COV, % 4.4 1.2 6.8 SH-130 Improved Mean, % 8.85 8.43 8.7 Embankment; Section 2 COV, % 19.8 21.6 8.4 SH-130 Improved Mean, % 8.35 9.1 8.05 Embankment; Section 3 COV, % 14.4 1.6 0.9 TH-23 Crushed Aggregate; Mean, % 4.26 4.28 4.34 North Section COV, % 1.3 1.0 2.1 TH-23 Crushed Aggregate; Mean, % 4.24 4.28 4.30 Middle Section COV, % 1.3 2.0 1.6 TH-23 Crushed Aggregate; Mean, % 4.18 4.18 4.38 South Section COV, % 3.9 3.9 1.0 US-280 Crushed Stone; Mean, % 3.92 Section 1 COV, % 3.1 US-280 Crushed Stone; Mean, % 4.18 Section 2 COV, % 2.9 US-280 Crushed Stone; Mean, % 3.77 Section 3 COV, % 2.9 US-280 Crushed Stone; Mean, % 4.06 Section 4 COV, % 2.6 Note: The shaded cells designate those areas with anomalies (refer to Table 14); the black cells denote weaker areas, while the gray cells denote the stronger areas tested within a specific project. 2.3.1.2 Standard Error 2.3.2 NDT Devices for HMA Mixtures Another reason for using the adjustment ratios in evaluat- 2.3.2.1 Variability of Response Measurements ing each NDT device is to eliminate or reduce bias by assum- ing that the target value is the laboratory resilient modulus Figure 32 compares the COV between different tech- measured at a specific stress state. Figure 30 compares the lab- nologies and devices (PSPA, FWD, PQI, and GPR). The oratory measured resilient modulus values to those estimated PQI consistently had a low COV relative to the other with different NDT devices but adjusted to laboratory con- devices, while the FWD had the largest value. It should be ditions, while Figure 31 presents the residuals (laboratory noted that a low COV does not necessarily mean that the resilient modulus minus the NDT modulus), assuming that device is providing an accurate measure of the HMA mix- the laboratory value is the target value. On the average, the ture property and variability. One reason for the lower adjusted elastic modulus from all devices compare reasonably COV values for the PQI relative to the other devices is that well with the laboratory measured resilient modulus. Table 30 five tests were performed at each test point. In other words, contains the tabulation of the mean of the residuals and stan- the testing and sampling error or differences get averaged dard error for the NDT devices that provide a direct measure out through the testing sequence. of material stiffness. Two versions of the GPR air-coupled antennas were used. In summary, the GeoGauge, DSPA, and DCP all provide The first version was a single-antenna method, which was good estimates with negligible bias (effect of adjustment only used in Part A of the field evaluation. The second version ratios) of the laboratory measured resilient modulus val- included the use of multiple antennas and the EPIC Hyper ues. The GeoGauge has the lower standard error. The LWD OpticsTM proprietary data interpretation system. The EPIC has a higher bias and over two times the standard error, in GPR system was supposed to be used along the NCAT, Mis- comparison to the GeoGauge. souri (US-47), and Texas (I-20) sections; however, weather

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56 Table 29. Dielectric values measured with the GPR on the unbound layers. Project Identification Area A B C D I-85 Embankment, Silty Clay; Section Mean 15.38 15.79 14.29 15.19 1, Before Rolling COV, % 17.8 23.3 53.6 25.7 I-85 Embankment, Silty Clay; Section Mean 13.91 17.47 16.82 16.38 2, Before IC Rolling COV, % 29.0 20.5 30.7 24.1 I-85 Embankment, Silty Clay; Section Mean 20.37 21.23 21.61 23.23 1, After IC Rolling COV, % 15.8 10.6 15.0 12.6 I-85 Embankment, Silty Clay; Section Mean 19.13 23.75 23.77 25.36 2; After IC Rolling COV 10.2 10.7 17.6 8.4 TH-23 Embankment, Silt-Sand- Mean 23.004 13.468 19.334 --- Gravel Mix; South Section COV, % 11.3 7.0 14.4 --- TH-23 Embankment, Silt-Sand- Mean 20.324 34.438 23.882 --- Gravel Mix; North Section COV, % 22.2 32.7 22.7 --- SH-130 Improved Embankment; Mean 9.225 10.00 7.65 --- Section 1 COV 33.1 42.3 42.9 --- SH-130 Improved Embankment; Mean 12.875 8.875 9.825 --- Section 2 COV 90.3 47.4 20.1 SH-130 Improved Embankment; Mean 8.775 9.025 11.85 Section 3 COV, % 51.5 50.8 48.7 --- TH-23 Crushed Aggregate; North Mean --- 8.796 10.042 --- Section COV, % --- 1.6 5.4 --- TH-23 Crushed Aggregate; Middle Mean --- 8.950 10.87 --- Section COV, % --- 6.1 10.9 --- TH-23 Crushed Aggregate; South Mean --- 9.792 10.378 --- Section COV, % --- 8.2 4.3 --- US-280 Crushed Stone; Section 1 Mean 11.723 COV, % 8.3 Mean 12.222 US-280 Crushed Stone; Section 2 COV, % 11.4 Mean 11.919 US-280 Crushed Stone; Section 3 COV, % 7.3 Mean 11.569 US-280 Crushed Stone; Section 4 COV, % 7.0 Notes: The shaded cells designate those areas with anomalies (refer to Table 14); the black cells denote the weaker areas, while the gray cells denote the stronger areas tested within a specific project. Due to construction sequencing, lane A of the TH-23 crushed aggregate base sections could not be tested with the GPR after it arrived on site. delays and equipment/plant problems resulted in changes to by assuming that the target value is the laboratory dynamic the testing schedule. These schedule changes resulted in con- modulus measured at a specific load frequency and an aver- flicts with other projects, so ultimately, this system was used age in-place mix temperature. Figure 33 compares the only on the NCAT test sections. PSPA and FWD modulus values that have been adjusted to Data were made available for use from other projects in laboratory conditions using the factors or ratios listed in Florida, which were not included in the original field evalu- Table 26. On the average, the adjusted modulus values ation (Greene 2007; Greene and Hammons 2006). The EPIC compare reasonably well to one another. Table 31 contains system is reported to have much more accurate and repeat- the mean of the residuals (laboratory dynamic modulus able estimates of HMA volumetric properties. One reason minus the NDT modulus) and standard error from the for this increased accuracy and precision is that it does not expected laboratory value--excluding all measurements rely on the assumptions that were included in the single taken in areas with anomalies, segregation, and along lon- antenna method used along the Part A projects. The preci- gitudinal joints. sion and bias for both devices and systems are provided in While the difference between the two NDT devices is small, the next section. the PSPA had the lower residual and standard error. 2.3.2.2 Standard Error 2.3.3 Summary As for the unbound materials, the adjustment ratios Tables 32, 33, and 34 contain the statistical analyses of the were used in evaluating the PSPA and FWD to reduce bias NDT devices included in the field evaluation projects. This