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 45 Because it is difficult to list each of the research papers McNamara (2000) to FDOT for their moduli assessments of that have covered the nondestructive and backcalculation the subgrades. studies on pavements, an attempt is made to cover only state DOT–funded research studies that evaluated nondestructive Ping et al. (2002) studied and compared in situ FWD- studies since the mid 1990s. determined moduli with laboratory resilient modulus for similar stress conditions close to FWD tests in the field. Results suggest that the backcalculated EFWD is about 1.65 Synthesized Information—Nondestructive times higher than laboratory resilient modulus. This varia- Tests tion is close to the 1991 AASHTO pavement design guide that recommends a factor of 0.5 to 0.33 be applied to the Florida—Subgrades EFWD to determine the laboratory MR value, regarded as a design input parameter for flexible pavements. Choubane and McNamara (2000) performed research for Florida DOT (FDOT) to assess the feasibility of using FWD Idaho—Subgrades data to predict the moduli of subsoils. This research described a methodology for using the measured deformation data to Bayomy and Salem (2004) presented FWD studies con- predict the modulus and also the compatibility of the FWD ducted on test sections once a year from 1999 to 2002. For data with those measured by Dynaflect. FDOT used Dynaf- each site, the test was conducted at five different stations lect data for years, and hence they were supporting research using two different loads, 8,000 lb and 12,000 lb (Bayomy to address the potential use of Dynaflect for field operations. and Salem 2004). The radial distances between the cen- Florida’s previous experience with nondestructive deflection terline of the applied load and each of the seven sensors testing (NDT) studies has shown that the pavement deflec- were 0, 8, 12, 18, 24, 36, and 60 in. (0, 20, 30, 45, 60, 90, tions measured at 36 in. away from the load are appropriate and 150 cm), respectively. The plate radius on which the for the determination of the subgrade moduli (Choubane and load was applied was 5.91 in. These measured values were McNamara 2000). then analyzed using backcalculation software, Modulus, Version 5.1. Based on 300 field FWD studies, the following equa- tion was developed and recommended for pavement design FWD-interpreted moduli were regarded as measured (Choubane and McNamara 2000): moduli values. Figure 49 compares the FWD backcalcu- lated moduli (referred to in the figure as measured moduli) with predicted moduli (based on regression equations using  (5) different soil properties). There is some variation between these measurements, though their trends appear to be the same. Bayomy and Salem (2004) also mentioned the need where EFWD is subgrade modulus interpreted from FWD; for more data points for a better assessment of models. P is applied load in lbs; and dr is deflection measured at a radial distance, r, of 36 in. These modulus data from FWD Mississippi—Subgrades and Bases are compared with the moduli determined from Dynaflect data in Figure 48, which suggest a strong correlation between The field studies supported by the Mississippi DOT are E value predictions by FWD and Dynaflect methods. This well documented in the literature (George and Uddin 2000; approach of using both was recommended by Choubane and Rahim and George 2003). Research performed by George FIGURE 48  Comparisons of E predictions by both NDT FIGURE 49  Comparisons of moduli predictions by FWD and methods (Choubane and McNamara 2000). regression modeling methods (Bayomy and Salem 2004).

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46 and Uddin (2000) aimed at correlating the DCP data to The E(Back) 2 values are larger than the corresponding predict field moduli. Subgrade moduli in this study were laboratory values. Hence, the ratio of values of E(Back) 2 and determined by analyzing the deflection profiles obtained by laboratory moduli varied from 0.85 to 2.0, with an average the FWD. value of 1.4. This study also reported that the moduli mea- sured from FWD tests on the subgrade were smaller and As part of Mississippi DOT-funded research, FWD stud- close to laboratory measurements. The FWD data on the ies were conducted on 12 test sections, with two types of sub- pavement sections yielded higher moduli, which was attrib- grades representing both clays and sandy soils (Rahim and uted to the higher confinement induced by the pavement sec- George 2003). The main intent of this research was to deter- tions. These results show that the comparison has less scatter mine the ratios of resilient modulus values obtained from at high moduli (more than 100 MPa), which suggest that laboratory and field measurements, for Mississippi subgrade FWD predictions of low moduli magnitudes need further conditions, to verify earlier studies that documented a wide scrutiny. The findings from this study were also compared variation between laboratory and field moduli. Von Quintus with the LTPP data from Mississippi, and the researchers and Killingsworth (1998) reported that the ratios between reported that a good agreement was obtained. moduli from FWD and laboratory ranged from 0.1 to 3.5 based on the LTPP database. Overall, Rahim and George (2003) acknowledged the need to revise the current factor of 0.33 applied over the This study used resilient moduli results from the FWD moduli, to determine the laboratory moduli, because AASHTO TP-46 tests conducted on the cores collected from the findings from this research showed lesser variations the same subgrades. Though three backcalculation programs between moduli measurements and predictions. The (namely, Modulus 5, FWDSOIL, and UMPED) were initially research findings and conclusions presented here are valid mentioned, results from only the first two programs were for the backcalculation software used in this research. documented in the report. The Modulus 5 backcalculated subgrade modulus values showed a good agreement with the New England—Subgrades laboratory MR, and the FWDSOIL backcalculation of sub- grade moduli was slightly lower than the laboratory MR. Malla and Joshi (2006) reported FWD studies and com- parison analyses in a research project conducted for New Backcalculation analysis of FWD data on subgrades was England states. To correlate the laboratory resilient modu- attempted using a Modulus 5 program developed by Texas lus (MR) values and FWD backcalculated modulus, the Transportation Institute researchers. Each subgrade was LTPP database was accessed. FWD backcalculated modu- subdivided into three layers, and the modulus of each layer lus data for Rhode Island were not available and hence was compared in the analysis. FWD measurements were not included in the comparison analysis. Mean elastic done twice, once on the finished subgrade and the other on modulus was calculated using backcalculation software the finished pavement surface. E(Back)1 was based on back- MODCOMP, version 4.2. For the purpose of compari- calculations using FWD data on the subgrade and E(Back) 2 son, the average of FWD backcalculated elastic modulus was based on FWD data collected on the pavement surface. values, corresponding to different levels of drop heights, Comparisons of both laboratory moduli and field moduli was compared with the average of laboratory MR values at [E(Back) 2], backcalculated from both FWD measurements, confining pressures of 13.8 kPa, 27.6 kPa, and 41.4 kPa. are presented in Figure 50. The backcalculated modulus values used are the same for all comparisons. Researchers noted that the backcalculated modulus values were higher than the laboratory resilient modulus values conducted at the same test site. However, no defi- nite relationship exists between the two values, which were attributed to the difference in years of FWD testing and laboratory specimen sampling and testing. Also, Malla and Joshi (2006) noted that the laboratory MR depends on soil and stress conditions, whereas E from FWD is related to a single field stress condition. They recommended another approach in which MR is calculated using bulk and octahe- dral stresses representative of the subgrade depth, where the stress ratio (ratio of normal stress at the pavement sur- face to the normal stress at the depth, D) is less than or FIGURE 50  Comparisons of moduli predictions by FWD and equal to 0.1 and then compare the calculated MR with the measurements by laboratory methods (Rahim and George 2003). backcalculated MR value from FWD studies.

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 47 Another study conducted by Steinert et al. (2005) for levels of compaction efforts along with corrections based on the New England Transportation Consortium focused on the moisture content variations. Researchers cautioned that the application of LWD to evaluate the support capacity of the results are based on the limited set of materials tested in pavements during the spring-thaw conditions as well as the their research and recommended additional testing for veri- adequacy of the base and subgrades during construction. fication if these materials are used for other DOTs. A PRIMA 100 LWD was used as the primary LWD Minnesota—Subgrades and Bases instrument for this research because it can be used with three different drop weights, three plate diameters, adjustable fall One of the earlier nondestructive device studies for MnDOT heights, and three deflection sensors. The performance of was performed by Siekmeier et al. (1999), in which the Load- seven paved and three gravel surfaced roads were moni- man PFWD and Humboldt SSG were used to characterize tored during the spring of 2004. All test sites were located both subgrade and granular bases for several construction in Maine, New Hampshire, and Vermont. One of the gravel projects in Minnesota. Standard FWD tests using Dynatest surfaced sites located in New Hampshire was monitored were performed at some locations and the moduli were back- during the spring of 2003. Two additional sites in northern calculated by analyzing the FWD data with EVERCALC, a Maine were also used for this testing. PRIMA 100 LWD and backcalculation software program. The moduli from various traditional FWD measurements were taken at a minimum of devices were compared with those from FWD to determine eight locations at each test site. In addition, Loadman PFWD the ability of LWD and SSG to measure in situ stiffness. measurements were taken at spring-thaw test sites in Rum- Figure 51 presents the moduli of granular bases from various ney, New Hampshire. field methods, including LWDs (termed as PFWDs in the figure) and DCP methods. Clegg Impact Hammer and Humboldt Soil Stiffness Gauge (SSG) measurements were taken at the U.S. Forest Also, laboratory resilient modulus tests were performed Service parking lot during the spring of 2003 and 2004. With on field cores, and their results were compared with field- the PRIMA 100 LWD, six measurements were taken, each derived moduli for developing correlations between field and at three different drop heights, at each test location. The first laboratory moduli. The FWD backcalculated moduli varied reading was omitted, and the average of the remaining five between 190 and 230 MPa, which were different from those was used for analysis and comparison. In addition, Loadman determined by other methods. Siekmeier et al. (1999) attri- PFWD, Clegg Impact Hammer, and SSG measurements were bute this variation to the confinement provided by the pave- performed at all test locations. Moduli were backcalculated ment backcalculation program’s simplifying assumption by from FWD data using either the DARWIN or EVERCALC not accounting for pavement edge effects and variations of programs. pressures exerted by the devices on the overlying surface The degree of correlation between moduli backcalculated using FWD and PRIMA 100 LWD was studied (Steinart et al. 2005). Test data from five sites in Maine for which the composite moduli from the FWD were available was used in this analysis. Regression analyses yielded correlation coef- ficients ranging from 0.34 to 0.95. Higher correlation coef- ficients were obtained when thin pavement sections were tested. Loadman PRWD and PRIMA 100 LWD moduli were compared with FWD-derived subbase moduli for two asphalt surfaced test sites in Rumney, New Hampshire (Stei- nart et al. 2005). The Loadman LWD provided a modulus that is less than the value interpreted by the PRIMA 100 data. Steinart et al. (2005) noted that the PRIMA 100 LWD- interpreted moduli correlates better to FWD-derived sub- base moduli (R2 = 0.55) than the moduli obtained from the Loadman LWD (R2 = 0.24). Overall, this research summa- rized that both LWDs are good tools to determine moduli of base and subbase layers. FIGURE 51  Comparisons of moduli of bases from various For compaction control studies, Steinart et al. (2005) pro- field studies (sc = sand cone test and ng = nuclear gage) vided equivalent PRIMA 100 base moduli values at various (Siekmeier et al. 1999).

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48 during the testing. The resilient moduli measured from the (LWD) and GeoGauge devices in determining the moduli laboratory tests were found to range between 180 to 320 MPa of the compacted layers. A total of 40 and 25 tests were per- for bulk stresses of 0.1 to 0.3 MPa (Siekmeier et al. 1999). formed for GeoGauge and PRIMA 100 devices, respectively, Siekmeier et al. (1999) and Siekmeier (2002) cited that the at a site located along a portion of a MnDOT TH 53 Trinity FWD backcalculated moduli are comparable with the resil- Road project. The main intent of this road project was to ient moduli from laboratory measurements at lower bulk demonstrate the intelligent compaction technology using a stresses than at higher bulk stresses. vibratory compaction roller, Caterpillar. Figure 52 presents the moduli of subgrades as deter- Both GeoGauge and LWD data correlated well and also mined by the in situ devices used in this research. Similar showed good agreement with the compaction meter values trends, as seen in the previous figure, can be seen here. provided by the Caterpillar compaction software. Figure 53 Compaction trends did not match well with the subgrade presents the LWD data, which showed that it follows normal soils. distribution trends at all the different heights of the fall of the hammer. These results are also in good agreement with Subsequent to this research work, the nondestructive those measured by GeoGauge. This whole research effort investigations supported by MnDOT focused on portable was to evaluate the QA studies on the compacted subgrade, and light weight FWDs for moduli measurements (Hoffman and not on the moduli assessments. Nevertheless, the stiff- 2004). Most of these studies focused on quality assessments ness measurements of compacted subgrades and unbound related to compacted bases and subgrades. Hoffmann et al. bases could be used for the field determination of moduli (2004) presented an LWD-type device known commer- properties needed for pavement design. cially as PRIMA 1000 for quality assessments of compacted granular bases using the stiffness measurements. A spec- Swenson et al. (2006) studied moisture effects on the tral-based data interpretation method, based on the concept measurements of several laboratory and field devices and and measurement of the frequency response function and a their interpreted moduli values. Four types of subgrade soils single-degree-of-freedom mechanical model, was employed were studied in various sizes and shapes. In the field studies, to interpret the true static stiffness of compacted base lay- this study reported a significant scatter of moduli from vari- ers from PRIMA 100 measurements. Results showed a good ous field measurement devices, including DCP, PRIMA 100, agreement with known and calibrated stiffness properties of GeoGauge, and others. Overall, the results showed that both the materials. moisture and density have a measurable effect on the moduli of all four tested soils. Another study conducted by Petersen and Peterson (2006) presented field data in Minnesota using both PRIMA 100 White et al. (2007) recently completed another research study for MnDOT that focused on compaction quality assess- ments of the subgrades based on the moduli measurements made using LWDs. Two types of LWDs were utilized in this research: the ZFG 2000 LWD and KEROS LWD. The ZFG 2000 LWD device was manufactured by Zorn Stendal, Germany (www.zornonline.de), and complies with German specifications for road construction. Deflections are measured using accelerometers for various load pulses, and the data are then analyzed to determine the dynamic deflec- tion modulus. The KEROS LWD device was manufactured by Dynatest, Denmark (www.dynatest.com). The device is equipped with a load cell to measure the impact force from the falling weight and a geophone to measure the induced deflections at the ground surface. Dynamic modulus was then determined using a modified Boussinesq’s equation. More details of both LWD procedures are presented in White et al. (2007). White et al. (2007) attempted to correlate LWD predicted moduli with the resilient moduli determined from labora- tory testing on the Shelby tubes from the field site known FIGURE 52  Comparisons of moduli of subgrades from as the MnROAD project site. The subgrade soils contain various field studies (sc = sandcone and ng = nuclear gage) (Siekmeier et al. 1999). a mixed glacial till and a sandy soil with silt and gravel.

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 49 FIGURE 53 LWD moduli predictions and their distribution (Petersen and Peterson 2006). AASHTO’s T-307 procedure was followed for laboratory resilient modulus testing. The LWD studies were also con- ducted on the compacted subgrade using both devices, ZFG 2000 and KEROS. Figure 54 shows these devices on the compacted subgrade. ELWD is a function of maximum deformation (or strain) under an applied plate contact stress, and these strains are total strains and not resilient strains. Because of the dif- ferences in strains, both moduli (MR from Laboratory and ELWD) are not considered the same, and hence White et al. (2007) used the secant modulus (Ms) from the permanent strain and resilient strain data obtained from the resilient modulus test. The secant moduli were then compared with ELWD data. Figure 55 presents the three different moduli, ELWD, MR, and Ms determined from these studies. Because of high contact stresses imposed by the LWD tests, the MR data at high confining and deviatoric stresses (42 kPa and 68.9 kPa, respectively) were used in another set of compari- sons, which are presented in Figure 56. Some of the major findings from this research as seen from these two figures are that both LWDs provided differ- ent dynamic moduli for the same subgrade owing to the dif- ferences in the methods adopted by these devices to measure the deformations in the field. One uses a geophone and the other uses an accelerometer. The “KEROS” ELWD (moduli) FIGURE 54 LWD devices in field operation (White et al. 2007).

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50 FIGURE 55  Comparisons of moduli determined from LWD and maximum and minimum resilient modulus from repeated load triaxial tests (White et al. 2007). is on average 1.75 to 2.2 times greater than “Zorn: ELWD Louisiana—Subgrades and Bases (moduli). This study showed the effects of plates used in the equipment on the moduli values determined. A good cor- Abu-Farsakh et al. (2004) performed two types of nonde- relation was obtained when moduli of LWD are correlated structive field tests to assess the stiffness properties of the with the resilient moduli at select confining and deviatoric compacted subgrades and bases, including stabilized layers. stresses (see Figure 56). A similar relationship was recorded The main intent of these investigations was to address the when the LWD moduli were correlated with that of secant applicability of these tests to provide realistic stiffness prop- moduli values. erties that are needed in both mechanistic pavement designs and for quality assessments of compaction. Two devices F or compaction assessments, White et al. (2007) devel- were evaluated, which included GeoGauge and an LWD. oped several tables that list LWD moduli values along A PRIMA 100 was used for the LWD studies. To assess with other in situ penetration test numbers for quality the moduli predictions, several FWD tests, using a trailer- assessments of compaction of the subgrades. Both gran- mounted Dynatest, were conducted and the deflection data ular and cohesive subgrade soil types are listed in these were analyzed using the ELMOD 4.0 backcalculation soft- tables. These tables describe the soils tested in the original ware program. All tests were conducted on both laboratory- investigation. prepared compacted soils and field subgrades.

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 51 FIGURE 56  Comparisons of moduli determined from LWD and resilient modulus triaxial tests at deviatoric stress of 68.9 kPa (White et al. 2007). In the LWD studies, tests were conducted on the same LWD device provides repeatable results at a higher stiffness material compacted with three different compactive efforts. of subgrades and bases. In each case, the coefficient of variation (Cv), an indicator for repeatability, was determined and these results showed Figures 58 and 59 present comparisons of LWD moduli that they vary from 2% to 28%. These results are plotted with FWD moduli (MFWD) and CBR properties, along with against the stiffness property, ELFWD, determined from the regression expressions. The FWD modulus was regarded the PRIMA 100 tests as shown in Figure 57. The Cv value as the resilient modulus in their research. The correlation reduces with an increase in stiffness, indicating that this developed for CBR was poor as a result of a large scatter in

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52 FIGURE 57 LWD moduli versus coefficient of variation, Cv (Abu-Farsakh et al. 2004). FIGURE 58  Correlations between LFWD modulus and CBR properties of different soil types (Abu-Farsakh et al. 2004). the test results. The correlation developed between moduli of FWD and LWD was strong, suggesting that the LWD provides similar moduli values as the FWD. For GeoGauge results, the Cv values varied between 0.4% and 11.4%, indicating excellent repeatability with this device (Abu-Farsakh et al. 2004). This study also reported that there is a strong correlation between EG values from GeoGauge with the field resilient moduli, MFWD from FWD. A good correlation was obtained between EG value from GeoGauge and the CBR value of compacted subgrade and base materials. Figures 60 and 61 present these trends, along with regression expressions to determine both FWD moduli and CBR values from GeoGauge moduli. No laboratory tests were reported in this research for the determination of resilient moduli properties of the same compacted materials. Hence, a comprehensive comparison analysis between nondestructive techniques and laboratory- determined moduli could not be performed. Nevertheless, FIGURE 59  Correlations between LFWD modulus and FWD the comparisons between the nondestructive methods offer modulus properties of different soil type (Abu-Farsakh et al. considerable insights into the applicability of LWD and 2004). GeoGauge in providing the stiffness properties of the sub- grades and bases. address the effects of time of testing (different time periods The Abu-Farsakh et al. (2004) study reported that cor- in a day) on the moduli results of stabilized bases before relations were obtained from the field test-based methods implementing these in the field. for interpreting moduli properties. The authors presented several advantages of GeoGauge, including convenience in New Mexico—Subgrades operating the device, quick test, and durability of the device. This study showed a good match of moduli data from Lenke et al. (2003) performed field investigations to GeoGauge with FWD for both subgrades and bases tested address compaction quality of aggregate bases using a in this research. Similar results were observed for LWD; GeoGauge. It was observed that GeoGauge is capable of however, this device was reported as not handy. Research- determining stiffness properties in the field, and these ers requested further tests on both LWD and GeoGauge to stiffness properties of compacted layers show a strong

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 53 FIGURE 60  Correlations between GeoGauge modulus and FWD backcalculated modulus properties (Abu-Farsakh et al. FIGURE 61  Correlations between GeoGauge modulus and 2004). CBR property (Abu-Farsakh et al. 2004). dependency on the number of passes of rollers used in The device was also used in the laboratory on different the field. Figure 62 presents the field moduli predicted by compacted subgrade soils prepared in the laboratory molds, GeoGauge versus the number of passes applied on the base and these results were reported to be influenced by the material in the field. boundary conditions induced by the rigid molds (Lenke et FIGURE 62  Effects of number of passes on GeoGauge moduli (Lenke et al. 2003).

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54 al. 2003). It was observed that this device was valuable for grades using both the GeoGauge and DCP. Results from assessing field compaction control utilizing the moduli data. these test methods used in 13 project sites across the state However, the establishment of such moduli data from labo- of Wisconsin were analyzed and correlated with vari- ratory tests was still not successful because of the boundary ous soil properties. Figure 63 presents GeoGauge SSG effects of the compaction molds used in the laboratory. results on both coarse- and fine-grained soils from the project site locations. These SSG values ranged from 0 Wisconsin—Subgrades and Bases to 12.1 MN/m, and this range is dependent on the mate- rial type and the compaction state. The mean SSG values Edil and Benson (2005) performed research investiga- for coarse- and fine-grained soils are 6.3 and 5.6 MN/m, tions to address the stiffness and stability aspects of sub- respectively. FIGURE 63 GeoGauge stiffness (SSG) measurements in Wisconsin test sections (Edil and Benson 2005).

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 55 A s part of this research, a separate study performed by Sawangsuriya et al. (2002) documented several laboratory tests and their moduli values. Figure 64 presents some of these results including SSG, resilient modulus–based RLT tests, conventional triaxial tests, seismic bender element– based tests, and RC tests. These tests are known to provide moduli from different types of tests such as static, dynamic, and seismic or nondestructive tests at different shear strain amplitudes, and a comparison of results shows a complete moduli degradation curve with respect to the shear strain amplitudes. The GeoGauge stiffness measurements are representa- tive of 10 -3% to 10 -2% strains, whereas the stiffness mea- surements of bender element and RC tests are close to small-strain conditions (10 -3%). Resilient modulus triaxial test results are representative of medium shear strain levels, similar to the GeoGauge range. The moduli from conven- FIGURE 64 Shear modulus of a granular soil from different tional static-type triaxial tests are close to secant moduli types of laboratory tests (Sawangsuriya et al. 2002). values at large shear strains of 100 % to 10 -1%. Other analy- ses of these results showed that the SSG value depends on compaction moisture content and dry unit weight; however, were different from one another, with the SPA moduli being their trends are obscured by the large dispersion in the test 70% higher than the corresponding FWD moduli. Compar- data of the several types of materials tested in this research ing the laboratory resilient moduli and the nondestructive (Edil and Benson 2005). This study also indicates that the moduli of bases and subgrades, the variation was consider- GeoGauge showed good potential for future application in able and significant (Nazarian et al. 1995). The laboratory the pavement and subgrade property evaluation during the moduli are less than the field moduli from nondestructive construction phase (Edil and Benson 2005). tests by 10% to more than 100%, and this variation was attributed to specimen differences, sample disturbance, and Texas—Subgrades and Bases time effects. Several nondestructive test studies were performed for the In a different research study, SPA, FWD, and Dynaflect TxDOT since the early 1990s. The following summarizes devices were used to measure moduli of seven test sites a few of these studies and their findings related to resilient in Texas (Nazarian et al. 2003 and Meshkani et al. 2004). properties of subgrades and bases. Nazarian et al. (2003, Table 10 presents the SPA results of both bases and sub- 2005) and Nazarian et al. (2006) performed both labora- grades. The average moduli results of subgrades varied from tory and field studies in different parts of Texas, and the aim 207 to 570 MPa, indicating that the subsoils tested were soft of these studies was to correlate both laboratory and field to stiff cohesive materials. The large coefficients of variation moduli and develop a methodology to determine moduli indicate a wide variation in the material type from point to for pavement design. For the backcalculation analysis, the point at each site. MODULUS program was used for analyzing the FWD test data. Meshkani et al. (2004) reported another TxDOT-funded research project in which researchers developed an algorithm An SPA was also introduced to measure the moduli of to predict design moduli based on the seismic modulus and pavement layers. The SPA lowers transducers and sources nonlinear parameters of each layer. Seismic modulus is simi- and digitally records deformations induced by a pneumatic lar to other backcalculated moduli from nondestructive seis- hammer. A complete testing cycle takes 1 minute, and the mic tests. Nonlinear parameters were developed from FWD moduli results can be determined in the field itself. results. SPA was also used in this research for the same pur- pose. This research showed the use of FWDs to predict non- For resilient moduli determination, MR values are either linear resilient modulus expression–related constants, which interpreted using three-parameter expressions whose con- in turn can be used to determine the design moduli values. stants are established in earlier experimental studies or Also, SPA was used to address the compaction QC of pave- by conducting tests on soil samples from the field at high ment layers. Overall, the research performed with TxDOT confining and deviatoric stresses. Stresses for the three-pa- led to the development and application of new devices such rameter expression were estimated using the KENLAYER as SPA and PSPA as well as new methodologies to estimate program simulating field conditions. FWD and SPA moduli design moduli using nonlinear material–related parameters.