Geomaterials, 2012, 2, 1-9
http://dx.doi.org/10.4236/gm.2012.21001 Published Online January 2012 (http://www.SciRP.org/journal/gm) 1
Soil Physical Properties as Predictors of Soil Strength
Indices: Trinidad Case Study
Ronald Roopnarine1, Gaius Eudoixe1*, Derek Gay2
1Department of Food Production, University of the West Indies, St. Augustine, Trinidad
2Department of Civil Engineerin g, University of the West Indies, St. Augustine, Trinidad
Received October 18, 2011; revised November 29, 2011; accepted December 11, 2011
ABSTRACT
Characterizing soil engineering properties and analyzing their spatial pattern has a key role in managing soils for dif-
ferent land uses. A study was conducted to generate two soil engineering properties; shear strength (SS) and friction
angle (FA) both related to slope stability from the datab ase of soil agricu ltural indices. A total of 30 so ils were analyzed
in two batches of 15 for physicochemical and engineering properties. The first batch was subjected to correlation and
regression analysis among properties, whilst the second was used to validate model predictions. Soil friction angle
showed strong significant correlations with clay and sand percent. Further stepwise regression resulted in these two
properties being the only predictors of peak and residual friction angle. None of the tested properties explained shear
strength distribution among the soils. The validated model predicted friction angles for the larger database, which
showed non-significant temporal differences from the present dataset used in this study. Spatially distribution of both
peak and residual friction angles varied across Trinidad, higher friction angles being associated with higher slopes.
Combination of this data with other spatial land attributes would greatly improve land management and slope stability
prediction.
Keywords: Soil; Friction Angle; Engineering Properties
1. Introduction
Estimation of soil strength indices is required for the de-
sign of foundations, retaining walls, and pavements in ci-
vil engineering applications and for determining the re-
sistance to traction and tillage tools in agricultural appli-
cations (Freudlund & Vanapali, 2002) [1]. These indices
are also essential in assessing the stability of slopes and
soil, and can be used to construe the ability of a soil to with-
stand stresses and strains associated with naturally occur-
ring instances of; increased pore pressure, cracking, swel-
ling, development of slickensides, leaching, weathering,
undercutting, and cyclic loading (Duncan & Wright, 2005)
[2] as well as anthropogenic changes to the landscape.
Shear strength and friction angle are two important
soil strength indices which have not been given due at-
tention, particularly in a country dominated by structur-
ally weak and expanding soils (Brown and Bally, 1967)
[3]. Locally, available soil information and spatial char-
acterization have been centered on agricultural data. Soil
physical and chemical data, along with profile descrip-
tions are provided by Brown and Bally (1967) [3] and Smi-
th (1983) [4] . Changing land use and development has
seen alternative uses for this information with obvious
limitations. Soil engineers rely on the existing soil phy-
siochemical data and their theoretical relationships with
engineering strength parameters to support and address
land use decisions and slop e stability issues. The need to
estimate and spatially characterize these engineering bas-
ed indices for a wide range of soils using a quick and re-
liable method is paramount to proper planning and man-
agement.
The difficulty and in some cases the high cost of at-
taining the soil strength indices has led to many resear-
chers seeking correlations with easily measured soil in-
dex properties (Eid, 2006) [5]. Several empirical proce-
dures have been developed over the years to predict the
shear strength of soils, particularly unsaturated soils.
Drained residual strength was shown to correlate with
clay content as well as type of clay minerals (Stark & Eid,
1997) [6]. The authors also showed strong correlations
between drained residual strength and liquid limit. The
soil water characteristic curve (SWCC) along with satu-
rated shear strength parameters have been used to predict
the shear strength of unsaturated soils (Vanapalli et al.,
1996 [7]; Freudlund et al., 1996 [8]; Oberg & Salfours,
1997 [9]; Khallili & Khabbaz, 1997 [10]; Bao et al.,
*Corresponding author.
C
opyright © 2012 SciRes. GM

R. ROOPNARINE ET AL.
2
1998 [11]). Other investigators suggested mathematical
relationships such as elliptical and hyperbolic functions
to predict the shear strength of unsaturated soils (Abra-
mento & Carvalho, 1990 [12]; De Campos & Carillo,
1995 [13]; Escario & Juca, 1989 [14]; Lu, 1992 [15];
Shen & Yu, 1996 [16]; Xu, 1997 [17]).
Soil friction angle, which is a measure of the ability of
a unit of soil to withstand a shear stress, is a derivative of
the measurement of soil shear strength. It is the angle,
measured between the normal force (confining stress)
and the resultant force within the soil colu mn (Coulomb,
1776) [18] that is attained when failure just occurs in
response to a shearing stress. Peak soil friction angle
refers to the initial angle attained from the initial shearing
phase, while the residual friction angle refers to the angle
obtained following the initial failure of the soil sample.
Skempton (1964) [19], introduced the concept of residual
strength and residual friction angle and proposed that it is
this “softened strength” that governs the behavior of re-
activated landslides and demonstrated that residual streng-
ths as well as residual friction angles are typically much
lower than their peak counterparts for clayey soils and
that they consequ ently have a detrimental effect on long-
term slope stability. The concept has since received con-
siderable attention. Specifically, research efforts have fo-
cused on determining correlations between the residual
friction angle of soils and soil indexes such as Atterberg
limits, and clay fraction (Kaya & Kwong, 2007) [20].
Harris et al. (1984) [21] proposed that specific engineer-
ing properties were related to particle size distribution
and mineralogy. Tsiambaos (1991) [22] studied the in-
fluence of the variation in clay mineral content on the
residual strength of soils and attempted correlations with
clay size faction and plasticity index. Tugrul & Zarif
(1998) [23] showed that there were strong correlations
between engineering properties of soils and particle size
distribution and indicated that particle size distribution
was more influential than mineralogy.
Relationships between engine ering param eters and m ore
specifically shear strength and friction angle with simple
soil index properties vary across regions, which indicate
a need for localized inv estigations. This study f ocused on
identifying and modeling such relationships, across a wi-
de range of soils.
2. Methodology
2.1. Soil Selection and Sampling
According to Suter (1960) [24] Trinidad is divided into
five physiographic zones (northern range, northern basin,
central range, southern range and southern basin), which
provided the rationale for selecting a cross section of
soils. A total of 15 soils were selected with at least two
soil series in each zone to encompass the diversity of soil
properties (Tabl e 1 ). An additional 15 soils were selected
following model development and used to validate the
model. For each of the initial 15 soil series two types of
samples were taken (disturbed and undisturbed) at a dep-
th between (1.6 - 2.0 m). For the 15 soil series used for
validation, only undisturbed samples were taken. Undis-
turbed samples were taken using a core (height 0.15 m,
diameter 0.073 m) that was inserted vertically using a
core sampler. The core sample was then sealed in plastic
wrap and stored for laboratory analysis. Disturbed sam-
ples were collected using an auger and were prepared for
subsequent laboratory analysis by air drying and grinding
to pass a 2 mm sieve. Samples were stored in plastic con-
tainers until analyzed. In total 25% of the soil series were
represented in the stud y.
2.2. Laboratory Analysis
2.2.1. D isturbe d Samples
The disturbed samples were subjected to physical and
chemical tests based on expected relationships with soil
strength indices (Kaya & Kwong, 2007 [20]; Harris et al.,
1979 [21]) and availab le soil survey data. Six parameters
were analyzed including; effective cation exchange capa-
city determined by the barium chloride method (Sch-
werdtfeg er & Hender shot, 200 9) [25 ], pH determined po-
tentiometrically in a soil to water ratio of 1:1 (Thomas,
1996) [26], particle size distribution determined using the
hydrometer method (Gee & Or, 2002) [27], Atterberg
limits determined according to ASTM 2000a (McBride,
2000) [28], non-capillary void space and bulk density
(Db) dete rmined by (Brady & W ei l, 2 002) [29].
Table 1. Physiographic zones, and families of the selected
soils.
Physiographic ZoneSoil Series Family†
San Souci fine, mixed
Anglais clayey, kaolinitic
Diego Martin coarse-loamy, carbonatic
Northern Range
Maracas clayey, oxidic
Piarco clayey, kaolinitic
Bejucal very-fine, mixed, acid Northern Basin River Estate fine-loamy, micaceous
Montserrat fine, oxidic
Biche very-fine, mixed
Brasso very-fine, montmorillonitic,
nonacid
Central Range
Marac very-fine, mixed
Princess Town very-fine, montmorillonitic,
nonacid
Moruga fine-loamy, mixed
Talparo very-fine, mixed, acid
South Range and
Basin
Ecclessville very-fine, mixed, acid

R. ROOPNARINE ET AL.
3
2.2.2. Undistu r be d Samples
A modified version of the drained direct shear test (Va-
napalli, 2002) [30 ] was used to determine shear strength.
Soils were subjected to three vertical-confining stresses
(0.5 normal stress, normal stress and 1.5 normal stress).
The modifications involved using a constant shear rate
on all samples of 0.35 mm·min–1 and a constant series of
confining stresses. This was done to ensure that all the
samples were exposed to similar stresses throughout the
experiment and to ensure that there was consistency in
the process. A plot of the maximum shear stresses versus
the vertical (normal) confining stresses for each of the
tests was produced. From the plot, a straight-line ap-
proximation of the Mohr-Coulomb failure envelope cur-
ve was drawn. The drained direct shear tests allowed the
determination of peak and residual shear strength and
friction angle respectively. This was conducted on all 30
undisturbed soils. The initial fifteen (Table 1 ) were used
in model development, the remaining 15 where used to
validate the mo del.
2.3. Statistical Analysis and Model Development
Person’s product moment correlation s were performed to
determine variable colinearity and to aid in the selection
of predictive variables, of soil strength parameters. Vari-
ables were subjected to a stepwise regression to deter-
mine the best model for predicting FA and SS that con-
tained statistically significant, intuitiv ely meaningful pre-
dictive variables. Only data elements that contributed sig-
nificantly (P < 0.05) to predicting FA and SS and that
contributed greater than 5% to the overall improvement
of the R2 were included in the equations. Where signifi-
cant relationships were observed th e models were used to
generate FA and SS from the entire Brown and Bally
(1966 [31], 1967 [3]) database. To account for temporal
variability of parameters, the generated data was statisti-
cally compared to Brown and Bally, (1966 [31], 1967 [3])
for the respective soil series at the study depth, using t
tests. The model was further validated with an indepen-
dent dataset by comparing measured versus predicted
values using Pearson’s correlations. The generated data
was then used to produce geospatial engineering maps.
Categories for friction angle were determined based on
the range and standard deviation (SD) of the data.
3. Results
3.1. Characterization Data
The range in properties of sampled soils used to develop
the prediction equation s for SS and FA are shown in Ta-
ble 2. A broad variation in taxonomical classification
was seen, with differences in mineralogy and lithology,
necessary criteria for validity and reliability. Normality
tests indicated that the data was normally distributed.
The clay and sand contents ranged from 27.2 - 94.3
and 1.74% - 56.7 % respectively. Similar br oad variation
was seen for most soil properties, especially where cor-
related to sand or clay (Table 3). Two soils sh owed alk a-
line pH values, whilst 53% of the sampled group were
strongly to extremely acid. Plastic and liquid limits
ranged from 16.6 - 33.3 and 17.4% - 79.6 % respectively.
A notable difference was observed between Anglais and
Piarco series which are both described as clayey, kao-
linitic but showed contrasting plastic index values, the
latter being much higher (23.2%). Significant positive
correlations were observ ed between plastic limits, ECEC
and particle distribution (Table 3), with values for the
former two properties increasing with increasing clay
content. Capillary vo id space as well as bulk density was
typical for mineral soils and showed minimal variation.
The two properties were negatively correlated. Unex-
pectedly, ECEC showed no relation to clay or sand con-
tent, but ranged from 5.44 - 39.6 cmol+·kg–1.
Table 2. Predictive soil properties used in developing SS and FA regression equations.
Soil Series pH ECEC Clay Sand PL LL NCVP Db
cmol+·kg–1 % g·cm–3
San Souci 6.36 21.4 34.0 44.7 26.7 39.0 6.65 1.39
Anglais 4.18 5.44 27.2 56.7 16.6 17.4 12.3 1.36
Diego Martin 6.86 12.5 34.6 47.1 25.0 22.5 7.99 1.37
Maracas 3.85 11.6 62.1 25.5 26.3 63.7 10.9 1.46
Piarco 3.45 14.3 39.6 51.6 23.2 56.5 10.8 1.47
Bejucal 3.95 20.2 94.3 1.74 25.0 64.5 1.73 1.17
River Estate 6.78 16.9 40.9 44.9 28.6 38.3 9.16 1.31
Montserrat 6.07 35.0 44.7 37.0 27.7 40.6 7.53 1.42
Biche 7.71 18.7 51.6 26.2 23.1 30.8 3.45 1.06
Brasso 6.98 19.8 58.3 28.2 31.3 50.8 5.13 1.53
Marac 4.01 22.8 58.3 29.2 33.3 43.5 6.95 1.34
Princess Town 7.3 39.6 79.2 13.4 33.3 79.6 12.9 1.20
Moruga 4.54 25.5 52.5 33.4 26.0 39.9 12.4 1.39
Talparo 5.93 33.3 93.2 4.77 32.7 61.0 3.82 1.45
Ecclessville 3.56 16.8 69.4 16.5 33.2 36.8 3.46 1.53

R. ROOPNARINE ET AL.
4
Table 3. Correlations betwee n soil proper ties and FA and SS.
Soil Property† FA-P FA-R SS-P SS-RpH ECEC Sand Silt Clay PL LL Db
(˚) (kN·m–2) (cmol·kg–1)(%) (g·cm–3)
FA-R 0.843***
SS-P 0.281 0.153
SS-R 0.365 0.401 0.732**
pH –0.038 0.061 –0.282 0.025
ECEC –0.392 –0.458 0.187 0.1260.363
Sand 0.720** 0.566* 0.097 0.097–0.043–0.523
Silt 0.475 0.651* 0.059 0.1510.340–0.319 0.618*
Clay –0.711** –0.636* –0.094 –0.120–0.0610.509 –0.975***–0.776**
PL –0.688** –0.634* 0.092 0.0010.3510.647* –0.612* –0.365 0.593*
LL –0.543* –0.679** 0.005 0.132–0.0910.545* –0.653* –0.755**0.735* 0.536*
Db† 0.118 0.034 0.351 0.115–0.398–0.081 0.297 –0.062 –0.221 0.144 –0.044
CVS† –0.433 –0.194 –0.283 –0.258 0.1390.072 –0.603* –0.061 0.501 0.163 0.082–0.636*
3.2. Soil Engineering Properties and Model
Development
Peak SS ranged from 27.8 - 49.7 kN·m–2 with a SD of
6.61 kN·m–2 whilst peak FA ranged from 11.7˚ - 43.5˚
with a SD of 10.2˚ (Table 4). Peak FA showed strong
and significant (P < 0.01) positive and negative correla-
tions with sand (R2 = 0.720) , and clay (R2 = –0.7 11) and
PL (R2 = –0.688) respectively. Similarly significant but
less strong relationships were seen for residual FA. Soil
physiochemical variables showed no relationship with ei-
ther peak o r r esidual S S .
Table 5 shows th e results of t tests used to compare %
clay and sand of the study data set and Brown and Bally
(1966, [31] 1967 [3]). There was no significant difference
between the data set. Further correlation analysis reveal-
ed significant (P < 0.05) positive relationships between
these two data sets with R2 values of 0.747 and 0.731 for
clay and sand respectively. This validated the us e of Bro-
wn and Bally (1970), data set to generate predicted val-
ues for peak and resid ual FA.
Clay and sand content explained 80% and 70% of the
variation in the peak and residual FA of the data set re-
spectively. The following regression equations were de-
veloped:
FA-P24.50.159% clay0.357% sand 
FA-R = 92.80.886% clay0.723% sand
(1)
(2)
Measured versus predicted peak and residual FA val-
ues for Equations (1) and (2) regression models are
shown in Figures 1(a) and (b). The 95% confidence in-
tervals about the slope an d intercept of the regression line
for prediction of both peak and residual FA, indicate no
significant difference from unity (Table 5). Peak and
residual FA prediction equations values compared ag-
ainst the measured independent data set resulted in an R2
of 0.93 and 0.60 respectively.
The generated peak and residual FA data were con-
verted to spatial coordinates and are shown in Figures 2
and 3 respectively. Colour codes identify FA categories
Table 4. Measured soil strength (SS) and friction angle (FA)
of selected soils used in developing predictive equations.
Soil Series SS-P SS-R FA-P FA-R
kN·m–2 ˚
San Souci 43.9 35.8 41.8 37.9
Anglais 42.8 34.1 43.5 35.6
Diego Martin 39.3 28.3 33.8 25.9
Maracas 45.7 38.7 25.9 20.3
Piarco 48.6 30.6 39.9 15.5
Bejucal 27.8 20.2 11.7 10.5
River Estate 31.8 23.7 28.0 21.5
Montserrat 45.1 30.6 27.0 20.3
Biche 41.1 31.8 31.0 28.8
Brasso 32.4 26.0 21.4 19.3
Marac 43.5 38.9 19.0 16.5
Princes Town 45.1 37.0 15.5 9.1
Moruga 49.7 30.1 14.3 12.7
Talparo 47.4 33.5 16.7 11.7
Ecclessville 47.7 29.1 24.8 21.5
Table 5. Statistical indices of t tests for difference between
data sets for regression variables.
Data Set Sand Clay
MeanSE P Mean SEP
Our Study 29.64.4 57.5 5.5
Brown and Bally (1970)27.64.9 0.774 60.9 6.2 0.685
shown in association with soil series. Greater friction an-
gles are associated with soils of the northern range and
basin with the lower values within the central range and
southern basin. Areas depicted in white represent regions
for which no data was available which in most cases re-
presented reclaimed land.
4. Discussion
4.1. Characterization Data
The initial characterization of soil physiochemical prop-

R. ROOPNARINE ET AL. 5
erties focused on indices with known relationships with
engineering properties. Particle size distribution, effecti-
ve cation exchange capacity (ECEC) and Atterberg limits
have all been shown to be related to soil strength proper-
ties (Kenney, 1967 [32]; Voight, 1973 [33] and Stark and
Eid, 1997 [6]) however, the relationships have been spe-
cific to location. Correlation results were consistent with
previous work done on these soils (Eudoxie, 2010 [34];
Brown and Bally, 1966 [31], 1967 [3], 1970 [35 ] & K. V.
Ramana, 1992 [36]). Strong positive correlations and in-
significant t test differences between our data set and that
of Brown and Bally (1966 [31], 1967 [3], and 1970 [35])
indicated that these tested properties were temporally
constant and ideal for use as predictive variables.
Notwithstanding the aforementioned finding, the re-
sults also revealed some anomalies. ECEC and clay con-
tent, showed non-significant correlations, which is con-
trary to the general consensus in the literature. This may
be explained by the stronger influence of clay mineral-
ogy on ECEC than clay content as evident by the find-
ings of Kulkarni, (1972) [37]. The Maracas series which
contained 60% clay, had an ECEC of 11.6 cmol+·kg–1.
The soils of the Northern Range including Maracas are
high in kaolinite, with minor amounts of montmorillonite,
illite, and vermiculite (M. Sweeney, 1981) [38]. Kaolin-
ite is a 1:1 mineral with low CAC (cation adsorption ca-
pacity). The Piarco series showed an unusually high PL,
especially when compared to other soils within the same
family. This is attributed to the depth at which the sample
was taken (1.6 - 2.0 m). In the Piarco series clay accu-
mulates at that depth, due to eluviation and illuviation
processes (Brown & Bally, 1970) [35]. The Atterberg
limits are important indicators of a soil’s ability to with-
stand deformation or stress at various moisture contents.
Odell et al. (1960) [39] indicated that values of plastic
and liquid limits all increased with clay content and pro-
portion of 2:1 expanding minerals. They showed strong
correlations between liquid limit, plastic limit, and plas-
ticity index, respectively, and three soil properties na-
mely, percent of organic carbon, percent of clay, and per-
cent of montmorillonite in the clay separate. Seybold et
al (2008) [40] additionally reported that clay content and
CEC explained 81% of the variation in LL of a very lar-
ge data set (n = 6592). Similar findings are reported
herein, with clay and ECEC showing the greatest R2 val-
ues for LL and PL of respectively.
4.2. Soil Engineering Properties
SS showed limited variation compared to friction angle
among soil series. According to the Mohr coulomb fail-
ure criterion, the former is a derivative of the friction
angle and other mathematically related variables. The
low SS values of 27.8 and 31.8 kN·m–2 for the Bejucal
and River Estate series, can be attributed to low internal
friction angle and negligible cohesion of the former soil.
This is consistent with the work of Kenney, (1967) [32],
Lupini et al. (1981) [41] and Skempton (1985) [42]. Be-
jucal and Talparo both show low internal friction angles
but widely different peak shear strengths, this may be
due the mineralogy and percentage clay being more in-
fluential on shear strength than on FAs as well as the
over consolidated nature of the latter which may have
prevented the soil from being fully drained and hence
matric suction would have contributed to the shear str-
ength value. As expected the residual strength was lower
than the peak sh ear strength for all soil series sampled as
explained by Skempton (1964 ) [1 9].
Friction angles varied from 9.1˚ to as high as 37.92˚.
The lower FAs were associated with soils that from cen-
tral and southern zones, which could explain the high
degree of slumping and sliding, associated with the clays
of that region (Kanithi et al. 2006) [43]. A strong nega-
tive correlation with clay content reaffirms the previous
inference, since these soils had higher clay contents. Fri-
ction angles are equivalent to the angle of repose of loose
materials, implying a frictional resistance, which is low
in clay particles (Skempton, 1964) [19]. Residual friction
(a) (b)
Figure 1. (a) Showing mode l prediction vs actual peak FAs; (b) Showing mode l prediction vs actual residual FAs.

R. ROOPNARINE ET AL.
6
Figure 2. Soil map of Trinidad showing spatial distribution of peak friction angle categories.
angles like residual strength were all lower for all soil
which indicates that once soil aggregates are disturbed it
is much easy for particles especial clays to move, reali-
gning themselves and reducing friction (Skempton 1964)
[19]. The soils of the northern range showed the higher
friction angles than the soils of the other zones due to their
higher sand content and shallow depth, exposing uncon-
solidated material at the sampling depth.
The results of this study were consistent with the wo rk
of Tugrul & Zarif (1998) [23] and Seybold et al. (2008)
[40], which showed that there were strong correlations
between engineering properties of soils and particle size
distribution, and that particle size distribution was more
influential, than mineralogy. This inference is supported
by the non-significant relationship of CEC to percent sand
and clay and soil strength indices. Kulkarni (1972) [37]
indicated that CEC is more strongly associated with clay
mineralogy, than clay content. The data indicates that the
influence of soil properties such as the Atterberg limits
on SS and FA were masked by their relationship to the
primary properties of sand and clay. Statistically sig nify-
cant indicator properties were identified only for FA,
which supports the Mohr Coulomb failure criteria, FA is
a constant soil feature. Contrastingly shear strength is in-
fluenced by both spatial and temporal dynamic features
such as moisture content and vegetation (Haines, 1925)
[44].
Validation results justified the use of the prediction
equation. Seybold et al. (2008) [40] reported similar con-
fidence in the predictive models for Atterberg limits.
Generation of FAs for the soils of Trinidad plus their
geospatial distribution provides a valuable asset and re-
source for not only engineering uses but also natural re-
source and disaster management specifically landslide/
mass movement susceptibility mapping. FAs were cate-
gorized in small intervals (5 - 10 degrees) to ensure pre-
cise spatial representation. The lower FAs (0 - 20 degrees)
are associated with greater potential for slope instability
and soil movement especially when subjected to increase
moisture which decreases suction pressure between indi-
vidual soil particles (Krahn et al. 1989) [45]. How ever, a
significant proportion of the soils with low FAs are lo-
cated on flat to slightly sloping terrain, supporting the
need to use this data in combination with other soil and

R. ROOPNARINE ET AL. 7
Figure 3. Soil map of Trinidad showing spatial distribution of residual friction angle categories.
physical data. Combinin g the FA data with o ther availab-
le soil survey data like slope class, can provide critical in-
formation, useful for land and engineering design evalua-
tion. Having such data readily available also reduces on
the lengthy, expensive and time consuming laboratory
testing needed to estimate these soil strength indices.
5. Conclusion
Sand and clay content were the most highly correlated
and the only independen t variables in predicting FA. No-
ne of the index properties evaluated showed any rela-
tionship to SS, however there were many correlations
among these index properties. Strong prediction equa-
tions were generated for peak and residual FA, with R2
values of 0.80 and 0.70 respectively. Validation trials con-
firmed the accuracy and reliability of the models. Trans-
formation of the Brown and Bally (1966 [31], 1967 [3],
1970 [35]) dataset resulted in production of a geospatial
representation of peak and residual FAs of Trinidadian
soils. Where testing capabilities are limited, this map may
provide a useful tool along with other available soil sur-
vey information. This study sets the stage for generating
relevant engineering data in geospatial mode much like
what already exists for agricultural purposes.
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