The document discusses using an automated dynamic cone penetrometer (ADCP) to characterize subgrade soil resilient modulus (MR) through correlation with ADCP index (DCPI). Twelve subgrade sections with varying soil types were tested with ADCP and falling weight deflectometer before and after pavement construction. Undisturbed soil samples were also extracted and tested in the laboratory for MR. Regression models were developed to correlate MR with DCPI for fine-grained and coarse-grained soils, accounting for other physical properties. Results suggest ADCP can reliably estimate MR and detect changes in subgrade stiffness from pavement loading.

1. 70 s Transportation Research Record 1806
Paper No. 02- 2039
Subgrade soil characterization, expressed as resilient modulus (MR),
is crucial for pavement design. For a new design, MR is generally ob-
tained by conducting repeated triaxial tests on reconstituted and un-
disturbed cylindrical specimens. Because of the test’s complexities, in
situ tests are desirable, if reliable correlation is established. This study
investigated the viability of using the automated dynamic cone pen-
etrometer (ADCP, abbreviated as DCP) for subgrade characterization
through correlation between DCP index (penetration per blow) and
MR. The sensitivity of DCP results to changes in subgrade stiffness,
expressed in modulus values, was also investigated. Twelve as-built
subgrade sections, reflecting a range of typical Mississippi subgrade
materials, were selected and tested with DCP. Undisturbed samples
were extracted with a Shelby tube and tested in a repeated triaxial
machine for MR. After other soil physical properties were determined,
the soil was classified according to AASHTO procedure. DCP tests
were repeated atop the subgrade through drilled holes after construc-
tion of pavement layers. Falling weight deflectometer (FWD) testing
was conducted near the DCP test locations. Results suggest two rela-
tionships, for fine-grain and for coarse-grain soils, in correlating the
DCP index to laboratory MR. Other physical properties helped improve
the robustness of the regression models. For model verification, the DCP
and MR tests were repeated and produced good agreement between pre-
dicted and actual MR values. The DCP index before and after pavement
layer’s emplacement suggests subgrade stiffness enhancement, owing
to pavement overburden, which agrees with FWD–backcalculated
moduli.
In the original AASHTO Guide for Design of Pavement Structures,
published in 1961 and revised in 1972, the subgrade stiffness was
accounted for by assigning a soil support value (SSV) on a scale
ranging from 1 to 10. In 1986, the AASHTO guide was substantially
revised to include replacement of SSV with subgrade resilient mod-
ulus (MR ) (1). MR values may be estimated directly from labora-
tory testing, indirectly through correlation with other laboratory and
field tests, or backcalculated from deflection measurements.
For a new design, MR values are generally obtained by con-
ducting repeated triaxial tests on reconstituted and undisturbed
cylindrical specimens. The laboratory test is a tedious, costly, and
time-consuming procedure. Large numbers of samples need to be
collected and tested for reasonably accurate results. Even then, it is
difficult to reproduce the in situ sample conditions (2). Considering
the complexity of the repeated load triaxial test, field testing proce-
dures have been proposed to estimate subgrade moduli. Of particu-
lar interest in this paper is an automated version of the dynamic cone
penetrometer (DCP) as a potential device for estimating MR through
correlation.
The DCP consists of a steel rod with a cone at one end that is
driven into the subgrade by means of a sliding hammer. The material’s
resistance to penetration is measured in terms of the DCP index
(DCPI) in millimeters/blow (3). Figure 1 shows the fully portable
trailer-mounted automated DCP during operation in the field. The
DCP was originally designed and used to determine the strength
profile of the subgrade because of its ability to provide a continuous
record of relative soil strength with depth. Numerous studies have been
conducted investigating the use of the DCP and factors affecting its
results (4–8).
The direct use of the DCP in pavement design is yet to be estab-
lished. However, it has been correlated to commonly used soil
parameters, for example, California bearing ratio (CBR) (5). Burn-
ham and Johnson (9) stated that the DCP could be used to provide
a reasonable estimate of unconfined compressive strength of soil-
lime mixtures. The shear strength of soils was correlated with the
DCPI, for different confining pressures, in a laboratory study con-
ducted by Ayers et al. (7 ). Employing a DCPI–CBR relation fol-
lowed by another one between the CBR and MR, the DCP results were
converted to the MR, which showed good agreement with both
laboratory-measured and falling weight deflectometer (FWD)–
backcalculated values (8).
Only a few studies attempted to directly correlate the MR to DCPI.
Hassan (4) developed a simple regression model correlating the
MR with DCPI for fine-grain soils at optimum moisture content.
Chai and Roslie (10 ) used the results of DCP tests and CBR–DCP
relationships developed in Malaysia during the 1987 National Axle
Load Study to determine in situ subgrade elastic modulus. Jianzhou
et al. (11) analyzed the FWD deflection data and DCP results of
six pavement projects in Kansas to develop a relationship be-
tween the DCPI and backcalculated subgrade moduli. Adopting the
one-dimensional projectile penetration, originally developed by
Yankelevsky and Adin (12), Chua (13) related the DCP test results to
elastic moduli of subgrade soil.
The objective of this paper is to explore the feasibility of using
the DCP to characterize subgrade soil MR, via correlating the DCPI
to laboratory resilient modulus. For two different soil groups,
namely, fine-grain and coarse-grain soils, two different regression
models are attempted correlating the MR (as dependent variable)
with the DCPI and other soil physical state variables as independent
variables. Influence of pavement overburden on subgrade stiffness
(moduli), and in turn on the DCPI, is also investigated.
Automated Dynamic Cone
Penetrometer for Subgrade
Resilient Modulus Characterization
Ashraf M. Rahim and K. P. George
A. M. Rahim, California Department of Transportation, Central Region, Fresno, CA
93726. K. P. George, Department of Civil Engineering, University of Mississippi,
Carrier Hall, Room 203, University, MS 38677.

2. Rahim and George Paper No. 02-2039 71
TESTING PLAN
Test Sections
Selected were 12 test sections of 244 m (800 ft) in completed grad-
ing projects, reflecting a range of soil types in Mississippi. Table 1
lists the locations, soil classifications, and other properties of the soils
in each section. A schematic of the test activities and evaluation
strategies can be seen in Figure 2.
Field Testing and Sampling
Dynamic Cone Penetrometer Tests
Before the experimental program in the field, side-by-side tests were
conducted with a manual DCP and an automated DCP, establishing
that they provide statistically similar results. For the purposes of this
discussion, however, the penetration test results will be referred to
as DCP results, though most of the results were gathered employing
an automated DCP. The grading operation was completed in early
1999, and the subgrade of each section was tested in the summer of
1999. The scheme for the DCP investigation consisted of testing at
30 m (100 ft) intervals to a depth of 1 m (3 ft) in the subgrade. At the
same locations, the DCP tests were repeated in the spring and sum-
mer of 2000, after completion of pavement construction. After graph-
ing the results of penetration versus number of blows, as shown in
Figure 3, possible subgrade layering was sought by investigating
slope changes in the trend line. Through a visual inspection of the
plots, the points of slope change were identified, thus determining
different layers, each with a finite slope. The slope of a segment is
designated by the DCPI in units of millimeter/blow. As shown in
Figure 3, most of the 60 sample locations exhibited three layers. The
calculated DCPI for each layer was used for correlation with the cor-
responding laboratory-measured MR, as will be discussed in detail
in a later section.
Falling Weight Deflectometer Tests
Side-by-side FWD tests were also conducted in all 12 test sections,
before and after emplacement of pavement layers. The stiffness change
observed in the FWD test series will be compared with the corre-
sponding change in the results of DCP test series. How sensitive the
two tests are to the overburden will be discussed in a later section.
Detailed results of FWD–backcalculated moduli before and after
pavement construction can be found in Rahim and George (14).
Proctor Test Shelby Tube Samples
Section
No.
Roadway/
County Station
AASHTO
Classification
Max. Dry Densitya
, kN/m3
(pcf)
/ Optimum Moisture, %
Dry Densityb
, kN/m3
(pcf) / Moistureb
, %
1 SR25/Rankin 1303-1311 A-6 17.4 (110.8) / 14.0 18.0 (114.6) / 16.8
2 SR25/Rankin 1347-1355 A-6 18.2 (115.9) / 12.0 19.0 121.0) / 12.5
3 SR25/Rankin 1590-1598 A-6 17.1 (108.9) / 14.3 17.0 (108.3) / 16.9
4 SR25/Rankin 1696-1704 A-6 18.00 (114.6) / 13.0 18.4 (117.2) / 12.8
5 SR25/Leake 522-530 A-6 18.4 (117.2) / 14.0 19.2 (122.3) / 11.7
6 US45/Monroe 88-96 A-2-4 16.7 (106.4) / 15.0 17.5 (111.5) / 14.2
7 US45/Monroe 108-116 A-2-4 16.3 (103.8) / 16.0 17.4 (110.8) / 16.5
8 US45/Monroe 170-178 A-2-4 17.1 (108.9) / 14.5 18.4 (117.2) / 12.4
9 US45/Monroe 260-266 A-2-4 15.7 (100.0) / 17.5 16.8 (107.0) / 16.3
10 US45/Monroe 461-469 A-6 17.4 (110.8) / 14.5 18.1 (115.3) / 16.6
11 US45/Monroe 490-498 A-6 17.1 (108.9) / 15.5 18.2 (115.9) / 13.7
12 US45/Monroe 668-676 A-3 15.7 (100.0) / 15.5 16.5 (105.1) / 17.2
a
Standard Proctor.
b
Average dry density/moisture determined from Shelby tube samples.
TABLE 1 Soil: Physical Properties of Test Sections
FIGURE 1 Automated DCP in operation.

3. 72 Paper No. 02-2039 Transportation Research Record 1806
Soil Sampling and Testing
Composite bulk samples were collected from every section for rou-
tine laboratory testing, including the Standard Proctor compaction
(T99-90), with those results presented in Table 1. For MR testing,
Shelby tube samples were obtained from five locations at 61-m
(200-ft) intervals to a depth of 1.5 m (5 ft). Retrieved from each foot
was one test cylinder of 71 mm (2.8 in.) in diameter by 142 mm
(5.6 in.) in height, with the top three samples tested for the MR in the
laboratory, accumulating 15 MR values from each test section. At
completion of the MR test, the density and moisture contents of each
sample were determined. The average values per section are listed
in Table 1. As can be verified, the actual densities of the Shelby tube
samples are somewhat higher than the maximum Standard Proctor
densities (by 1.5% to 8.8%). It could be the case that those samples
have undergone recompaction and densification while the Shelby tube
was being pushed in extracting a sample. This problem is aggravated
in the top layer because of desiccation and shrinkage.
Laboratory Tests
Resilient Modulus Test
Laboratory MR tests, following the AASHTO TP46 Protocol, were
conducted employing the Mississippi Department of Transportation
repeated triaxial machine furnished by Industrial Process Control.
The deformation in the samples was monitored employing two lin-
ear variable differential transformers mounted outside of the testing
chamber. The average MR values for the last five loading cycles of a
100-cycle sequence yielded the MR. Figure 4 presents a typical plot
of MR versus deviatoric stress for a fine-grain soil sample. In total,
180 plots were prepared, from which the MR of each sample was
interpolated for the stress state representative of field conditions.
As a general rule, the modulus values of the first-foot samples of
fine-grain soil sections were always higher than the corresponding
coarse-grain soil values. This result could be attributed to desicca-
tion and shrinkage and consequent stiffening of the soil. Increase in
Study Plan
Field Testing
Shelby Tube DCP
Before and after pavement
layers construction
Laboratory Tests
Laboratory
resilient modulus
Atterberg limits, sieve analysis,
moisture content, dry density
Undisturbed
cylindrical samples
FWD
Before and after pavement
layers construction
Comparison of DCP and FWD
results before and after pavement
layers construction
LL, PI, wc, cu, γd, % passing #200 MR DCP index
1- Fine-grain soil correlation; MR = f (DCPI, LL, wc,)
2- Coarse-grain soil correlation; MR = f (DCPI, cu, ….)
FIGURE 2 Schematic chart showing field and laboratory tests and data analysis.

4. Rahim and George Paper No. 02-2039 73
soil suction in fine-grain soil, as a result of desiccation, in turn, gives
rise to larger modulus values.
Laboratory Routine Tests
Routine tests for soil classification (M145-87) (15) were conducted
on composite samples collected from each section (see Table 1).
The same routine tests determining soil state variables were per-
formed on soil from each test cylinder at completion of the MR test.
Those properties comprised some of the explanatory variables in the
regression analysis.
RESILIENT MODULUS RELATED TO DCPI
Resilient Modulus Determination
The plan called for correlating the MR to DCPI (possibly in con-
junction with other soil physical state properties). At each Shelby
tube location, samples from the first, second, and third foot yielded
MR–stress state curves, such as are shown in Figure 4. From the pen-
etration versus blows plot (typical plot in Figure 3), three DCPI val-
ues were extracted corresponding to the same sample location. Since
the MR is a function of stress state, the question arises as to selecting
an MR from an MR–stress state relation. Relying on the results of
Thompson and Robnett (16), Elliot (17) suggested using a zero con-
fining pressure and a 41.6-kPa (6-psi) deviatoric stress when selecting
an MR value from laboratory test data. With the recognition that
in-place subgrade must sustain the overburden of pavement layers,
in addition to the standard 18-kip axle load, in situ stress due to a
typical pavement is combined with stress due to a 20-kN (4,500-lb)
wheel load at a tire pressure of 690 kPa (100 psi). Stresses calcu-
lated by KENLAYER (18) yielded a stress state of 37 kPa (5.4 psi)
deviatoric stress and 14 kPa (2 psi) lateral stress. Making use of
the foregoing stress combination, one MR value for each sample was
interpolated from plots such as those in Figure 4.
MR –DCPI Correlation
By necessity, the 180 test cylinders from 12 test sections were clas-
sified into two groups: fine-grain and coarse-grain soil, in accordance
0
200
400
600
800
1000
1200
1400
0 10 20 30 40 50 60
No. of Blows
CumulativePenetration(mm)
20
40
60
80
100
120
0 10 20 30 40 50 60 70
Deviatoric Stress (kPa)
ResilientModulus(MPa)
Conf. pressure = 41 kPa
Conf. pressure = 28 kPa
Conf. pressure = 14 kPa
FIGURE 3 Typical ADCP test results (Monroe County, Station 89؉00).
FIGURE 4 Typical plot of laboratory MR test results (Monroe County, Station
670+00, Sample 1).

5. 74 Paper No. 02-2039 Transportation Research Record 1806
with AASHTO M145-87 (15). For each group, one regression model
relating the MR to the corresponding DCPI was attempted.
Because the DCP test is destructive in nature, it is not realistic to
expect a one-to-one relation between the MR and DCPI. Therefore,
it appeared prudent to include basic soil state properties as indepen-
dent variables in the regression models. After an exhaustive search
for variables that may have some bearing on the MR, the following
were selected in the regression analysis: dry density (γd), moisture
content (wc ), liquid limit (LL), plasticity index (PI ), percentage pass-
ing the #200 sieve, and uniformity coefficient (cu ). Table 2 lists the
range of dependent and independent variables for the two soil groups.
The Statistical Package for the Social Sciences (SPSS) (19) was
employed to perform the regression analysis in this study. For identi-
fying individual variables that exhibit high correlation with the MR, a
simple correlation analysis was conducted. Explanatory variables with
poor correlation with the MR were identified and in turn excluded from
further consideration. The PI for fine-grain soils and the percentage
passing the #200 sieve for coarse-grain soils are examples. For a reli-
able regression model, there should not be a strong correlation among
the explanatory variables. Explanatory variables, if they are highly cor-
related, would weaken the predictive power of the model, a problem
referred to as multicollinearity. Multicollinearity is always associated
with unstable estimated regression coefficients and can seriously
limit the use of regression analysis for inference and forecasting (20,
21). If such multicollinearity does exist, its effect can be eliminated
by coining or transforming variables. Simple correlations were con-
ducted exploring multicollinearity, among the transformed variables.
For illustrative purposes, the correlation matrix for coarse-grain soil is
presented in Table 3. It is clear from the table that the correlation coef-
ficients between each pair of transformed variables are relatively low,
less than 0.40 for all combinations. In addition, the coefficient of cor-
relation of the MR with each transformed variable has improved com-
pared with that before transformation. More detailed results and
analysis can be found in Rahim (22). The listed transformed variables
were, therefore, employed for developing the regression model.
In developing regression models, the curve estimation option in
SPSS was employed. The best forms of relation between the MR and
each of the explanatory variables were investigated, employing the
coefficient of determination (R2) as the best-fit criterion. The explana-
tory variables were then combined, and different model forms were
examined. The nonlinear regression option in SPSS was employed
to determine the regression coefficients. After an exhaustive search,
examining many different forms and interaction terms, the following
model forms were selected: for fine-grain soil,
and for coarse-grain soil,
where R2
is the coefficient of determination, and RMSE is the root-
mean-square error.
Listed in Table 4 are the values of regression coefficients and
summary statistics of the derived models. The F-test for multiple
regression relation was conducted to validate the significance of
the relationship between the MR and all of the explanatory variables
included in the models (20). With F* values for fine-grain and coarse-
grain soil much higher compared with F(0.95, p − 1, n − p), the evi-
dence is sufficient to show that a relationship does exist between the
MR and other explanatory variables. Note that p is the number of
explanatory variables, and n is the number of observations. The sig-
nificance of individual coefficients was tested employing the t-test.
At a confidence level of 95%, all of the coefficients are significant,
as t* > 1.96 (see Table 4).
To check the robustness of the developed models, residuals versus
the predicted MR values for the two soil groups were plotted. Such plots
are used to examine multicollinearity among the explanatory variables
after developing the model. The residual-MR plots for fine-grain and
coarse-grain soil are presented in Figure 5a and 5b, respectively.
No distinct pattern is observed, ruling out multicollinearity among
explanatory variables. Therefore, the models are well specified.
The laboratory MR values were plotted against the predicted MR
for fine-grain and coarse-grain soil, as shown in Figure 6a and 6b,
respectively. The plotted points clustering along the line of equality
are an indication of the robustness of the presented models.
M b
DCPI
c
w
R RMSE
R o=





 +( )
= =
log
. . ( )
u
b1
dr
b2
cr
b3
γ
2
0 72 12 1 2
M a DCPI
LL
w
R RMSE
R o= ( ) +












= =
a1
dr
a2
c
a3
γ
2
0 71 31 6 1. . ( )
Soil Type Variable
Variable
notation Description Range
Dependent MR Laboratory resilient
modulusa
, MPa (psi)
31(4,436) – 269 (38,986)
DCPI Penetration Index, mm (in.) 3.7 (0.14) – 66.7 (2.63)
γd Dry density, kN/m3
(pcf) 15.1 (96.0) – 20.6 (131)
wc Moisture content, % 10.6 – 31.1
LL Liquid limit 20 - 57
Fine-grain
Independent
PI Plasticity index 2 - 31
Dependent MR Laboratory resilient
modulusa
, MPa (psi)
28 (4,058) – 158 (22,899)
DCPI Penetration index, mm (in.) 5.6 (0.22) – 40.0 (1.6)
γd Dry density, kN/m3
(pcf) 15.7 (99.7) – 19.1 (121.6)
wc Moisture content, % 12.4 – 22.0
cu Uniformity coefficient 2.8 - 925
Coarse-
grain
Independent
% #200 Percent passing # 200 sieve 7 - 33
a
MR values calculated at 37 kPa deviatoric stress and 14 kPa confining pressure.
TABLE 2 Ranges of Dependent and Independent Variables for Fine-Grain and
Coarse-Grain Soils

6. Rahim and George Paper No. 02-2039 75
Model Verification
To verify the predictability of the developed models, the DCP test was
conducted at four different locations in a construction site in Oxford,
Mississippi. Field density and moisture content were measured for the
four locations. Bulk soil samples were collected from each of the test
locations, and cylindrical specimens were reconstituted for labora-
tory MR testing, duplicating the moisture and density in the field.
Atterberg limits and grain size distribution were also determined, with
the soils classified as A-4, putting them in the fine-grain soil group.
The laboratory MR values were determined, for the three samples
from each location, at stress combination of 37 kPa deviatoric stress
and 14 kPa confining pressure. The average of the three MR values
is listed in Column 7 in Table 5. With employment of the fine-grain
soil model (Equation 1), the MR values were predicted and compared
with average laboratory values (Columns 8 and 7 of Table 5).
To compare the predicted and actual MR values, the test of dif-
ference of paired samples was conducted (23). Twelve modulus val-
ues (three per location) form the sample size. On the basis of the
test results (t* = −0.52 compared with Η t0.025, 11Η = 2.201), the null
hypothesis, H0—no significant difference exists between predicted
and actual values—is accepted. Viewed differently, no evidence
of significant difference exists between actual and predicted mod-
uli, which validates the predictability of one of the models devel-
oped in this study.
SENSITIVITY OF DCP TO CHANGES IN
SUBGRADE STIFFNESS
The MR –DCP correlation study was part of a larger study to char-
acterize Mississippi subgrade soils, in terms of MR. Because of the
need for MR values in both pavement design and rehabilitation, other
tests were conducted, including FWD tests, before and after pave-
ment layer construction, and DCP atop subgrade following coring
through pavement layers. Results from these tests made it possible
to investigate the sensitivity of the DCP test to changes in subgrade
stiffness brought about by overburden pressure.
A comparison between backcalculated moduli from FWD deflec-
tion data conducted directly on the subgrade and after pavement layer
construction revealed that modulus values were enhanced primar-
ily due to overburden imposed by pavement layers. On average, the
modulus was enhanced by 40% and 100% for fine-grain and coarse-
grain soils, respectively. More details of the comparison, results, and
analysis can be found in Rahim and George (14). The DCP results
before and after pavement layer construction (DCPI1 and DCPI2 )
were compared to investigate whether the stiffness change, captured
by backcalculated moduli, could be in line with the DCPI change.
Figure 7a and 7b compare DCPI1 and DCPI2 for fine-grain and
coarse-grain soil, respectively.
As expected, the ratios of DCPI2 / DCPI1 are different for the two
soil types. With stiffening of the soil, the DCPI2 decreased in rela-
tion to the DCPI1, providing percentage decreases of 20% and 34%
for fine-grain and coarse-grain soil, respectively. A scrutiny of the
test conditions reveals that the subgrade is only partially confined
when tested through the core hole. A recent study by Chen et al. (8)
showed that the DCPI2, determined through a core hole, was 11%
smaller than the DCPI1 with no overburden. The authors gave no
clue as to the type of soil tested, however. In the second part of their
experiment, the DCP was driven through the asphalt layer deter-
mining DCPIasp for fully confined conditions, observing 30%
reduction from DCPI2 to DCPasp. If this 30% reduction were to be
applied to our results, it could be shown that from a no-overburden
to full-overburden state, the DCPI could decrease by 44% and 60%
for fine-grain and coarse-grain soil, respectively. These changes
(decreases) in the DCPI are somewhat in line with the changes
in backcalculated moduli of 40% for fine-grain and 100% for
coarse-grain soils.
A basic difference in the test procedure may be cited for the poor
or inferior correlation between the DCPI change and backcalculated
MR DCPI/ Log cu γdr wcr
MR 1 0.35 -0.42
DCPI / Log cu -0.45 -0.20 0.03
γdr 0.35 1 -0.40
wcr -0.42
-0.45
1
-0.20
0.03 -0.40 1
γdr = Actual density / standard Proctor maximum density.
wcr = Actual moisture content / optimum moisture content.
Soil Type Coefficient Value t* F* RMSE R2
ao 27.86
a1 -0.114
a2 7.82Fine-grain
a3 1.925
46.5a
31.45 0.71
bo 90.68
b1 -0.305
b2 -0.935Coarse-grain
b3 0.674
4.33
2.05
4.60
10.81
9.99
10.48
1.98
2.17
31.82b
12.12 0.72
a
Critical F = 2.50
b
Critical F = 2.55
TABLE 3 Correlation Matrix of Transformed Explanatory Variables for
Coarse-Grain Soil
TABLE 4 Summary Statistics for Two Soil Group Models

7. 76 Paper No. 02-2039 Transportation Research Record 1806
moduli change. The difference is that the DCP test is destructive in
nature, whereas the FWD test is not. Because the soil is plasticized
while the cone is driven into the soil, the effect of confinement
hardly arises in the DCP test; accordingly, the DCPI change from no
overburden to full overburden is not as significant as is the modulus
change in the FWD test. Nonetheless, that the DCP change closely
follows the modulus change is an indication of the viability of the
DCP for monitoring modulus changes of in-place subgrade.
SUMMARY AND CONCLUSIONS
The focus of this study was to investigate the use of the automated
DCP for subgrade soil characterization. In 12 sections of prepared
subgrade, side-by-side DCP and FWD tests were conducted, and
undisturbed samples for MR tests were collected. MR values were
regressed against the DCPI other soil state properties, obtaining
models for fine-grain and coarse-grain soil. A feature of the models
is that in addition to the DCPI, other soil state variables are found to
be significant in MR prediction. The models were verified by repeat-
ing the tests in another site and comparing measured and predicted
moduli. Following is a summary of the significant conclusions:
1. The automated DCP is a simple and expedient device for field
testing of soils and particulate material.
2. Shelby tube samples extracted for MR testing had suffered sig-
nificant sample disturbance, especially those from the desiccated
top layer.
3. As mandated by data, two models were developed—one for
fine-grain and another for coarse-grain soil. The model predictabil-
ity is substantially increased by including soil state properties as
additional explanatory variables.
4. The predictability of the fine-grain soil model was substantiated
by testing an independent site with excellent comparison between
Location
#
Actual
moisture
contenta
, %
Dry densitya
,
kN/m3
Moisture
ratioa
Density
ratioa
Liquid
limita
Actual
MR
a
,
MPa
Predicted
MR
a
,
MPa
1 17.1 0.76 1.05 39.0 189 216
2 17.8 0.76 1.08 37.0 197 193
3 16.8 0.93 1.03 39.0 141 146
4
12.6
12.6
15.3
13.0 16.7 0.79 1.02 28.0 113 103
a
Average of three samples.
(a)
(b)
-40
-30
-20
-10
0
10
20
30
40
0 50 100 150
Predicted MR, MPa
Residuals,MPa
-100
-80
-60
-40
-20
0
20
40
60
80
100
0 50 100 150 200 250 300
Predicted MR, MPa
Residuals,MPa
FIGURE 5 Residuals versus predicted MR values for
(a) fine-grain soil and (b) coarse-grain soil.
(a)
(b)
0
20
40
60
80
100
120
140
160
180
0 20 40 60 80 100 120 140 160 180
Laboratory MR, MPa
PredictedMR,MPa
0
50
100
150
200
250
300
0 50 100 150 200 250 300
Actual MR, MPa
PredictedMR,MPa
FIGURE 6 Laboratory versus predicted MR values for
(a) fine-grain soil and (b) coarse-grain soil.
TABLE 5 Physical and Mechanical Properties of Samples Tested for Model Verification

8. Rahim and George Paper No. 02-2039 77
the measured and predicted MR values. The coarse-grain soil model,
however, awaits verification.
5. The sensitivity of the DCP test procedure is substantiated by
comparing the change in DCPI (before and after emplacement of pave-
ment) with the corresponding change in FWD backcalculated moduli.
By way of general conclusions and recommendation, it is advanced
that for the range of soils tested, the developed MR –DCPI models
provide useful predictions of MR.
ACKNOWLEDGMENTS
This paper is a part of the study entitled Subgrade Characterization
for Highway Pavement Design, conducted by the Department of
Civil Engineering, University of Mississippi, in cooperation with
the Mississippi Department of Transportation (MDOT) and
FHWA. Technical help received from Alfred Crawley, Joy
Portera, Bill Barstis, Johnny Hart, and Alan Hatch of MDOT, and
Jianrong Yu, of the university, is acknowledged.
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The opinion, findings, and conclusions expressed in this paper are those of the
authors and not necessarily those of the Mississippi Department of Transportation
or FHWA. This paper does not constitute a standard, specification, or regulation.
Publication of this paper sponsored by Committee on Strength and Deformation
Characteristics of Pavement Sections.
FIGURE 7 Comparison of DCPI1 and DCPI2 (before and after
pavement construction) for (a) fine-grain soil and
(b) coarse-grain soil.
0
10
20
30
40
50
60
70
80
0 20 40 60 80
(DCPI)1, mm/blow
(DCPI)2,mm/blow
(a)
(b)
0
10
20
30
40
50
60
70
0 10 20 30 40 50 60 70
(DCPI)1, mm/blow
(DCPI)2,mm/blow