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Cone Penetration Testing 2022 – Gottardi & Tonni (eds)
© 2022 Copyright the Author(s), ISBN 978-1-032-31259-0
Open Access: www.taylorfrancis.com, CC BY-NC-ND 4.0 license
A CPT-based method for estimation of undrained shear strength of sands
and transitional soils
K. Kaltekis & J. Peuchen
Fugro, Nootdorp, The Netherlands
ABSTRACT: This paper presents a practical approach for developing a site-specific CPT-based method for
monotonic undrained shear strength (s
u
) in sands and transitional soils, using results of laboratory undrained tri-
axial compression (CU) tests on reconstituted and undisturbed specimens as reference. The methodology includes
use of net cone resistance values normalised to vertical effective stress, a procedure for pairing of CPT data with
CU test results, and definition of a practical failure criterion for deriving s
u
from CU test data. The presented
approach is particularly useful for application in offshore wind, where the economics of wind farm development
favour performing only a single cone penetration test (CPT) per wind turbine location. This setting drives develop-
ment of CPT-based methods for key geotechnical parameters for foundation design.
INTRODUCTION
The characterisation of undrained behaviour of sands
and transitional soils (e.g. silty sands, low plasticity
silts) is important for large foundations subject to sig-
nificant short-duration loading and cyclic loading.
Therefore, undrained shear strength (su) of sands and
transitional soils is an important geotechnical param-
eter that can be used (i) as direct input in calculation
models for fully undrained modelling, and (ii) for
defining a reference for normalisation of cyclic soil
parameter values.
This study presents a practical framework for
developing a site-specific CPT-based method for s
u
in
sands and transitional soils, using results of laboratory
undrained triaxial compression (CU) tests on reconsti-
tuted and undisturbed specimens as reference. The
methodology includes use of (1) net cone resistance
values (qn,defined as qn ¼qc þ ð1 aÞu2 σv,
where qc is cone resistance, α is net area ratio, u2 is
pore pressure at the cylindrical extension above the
base of the cone and σv is vertical total stress) nor-
malised to vertical effective stress (σ0), (2)
v
a procedure for pairing of CPT data with CU test
results, and (3) definition of a practical failure criter-
ion for deriving su from CU test data.
The approach outlined in this paper is particularly
useful for application in offshore wind, where the
economics of wind farm development can dictate
performing only a single cone penetration test (CPT)
per wind turbine location. This setting drives devel-
opment of CPT-based methods for key geotechnical
parameters for foundation design.
This paper includes an example of the site-
specific approach, using input data taken from two
wind farm sites offshore Netherlands, namely the
Hollandse Kust (west) site and the Hollandse Kust
(noord) site (Figure 1, HKW and HKN respect-
ively). The input data are in the public domain, as
per the European INSPIRE (2018) directive for spa-
tial information.
2 DATABASE
2.1 Geological setting
The HKW site and the HKN site are located in the
southern North Sea. Water depths are typically
between 15 m and 34 m relative to LAT.
The sites comprise Quaternary deposits with
a predominantly sandy sedimentary profile with
occasional clay layers associated with internal
channelling (RVO, 2019; RVO, 2020). Sands are
mainly fine and medium with occasional coarse size
in some of the soil units. The sites have been sub-
ject to evolution throughout the Pleistocene and the
Holocene. Sediments and processes from these time
periods dominate the geological framework. Geo-
logical formations present at the two sites within
the top 50 m below seafloor include (from older to
younger) Yarmouth Roads, Eem, Naaldwijk and
Southern Bight. These geological formations show
no evidence of cementation. Figure 2 illustrates
a microscopic photograph of a typical sand sample
from the HKW site.
DOI: 10.1201/9781003308829-68
486
Figure 1. Locations of HKW and HKN sites.
Figure 2. Microscopic photograph of a typical unwashed
sand sample from the HKW site.
2.2 CPT data
The available CPT data were acquired according to
ISO (2014). The data are available in digital tabular
format and include piezocone CPTs and seismic
piezocone CPTs performed in both non-drilling
mode (direct push from seafloor) and drilling mode
(vessel drilling, downhole push) deployment.
2.3 CU data
The database includes results of CU tests performed
according to ISO 17892-9:2018, using reconstituted
specimens, prepared by moist reconstitution, and
undisturbed specimens. Reconstituted specimens
were prepared based on estimated in situ density.
Other specimen density considerations are described
below (section titled ‘data pairing’). It is generally
recognised that reconstituted specimens may give
lower shear strength than undisturbed specimens
(Hoeg et al., 2000).
The specimens were recompressed to the esti-
mated in situ stress conditions, using conventional
back pressures for specimen saturation. No pre-
cycling was applied. Recompression conditions were
either isotropic or anisotropic, depending on the esti-
mated in situ stress state (K0 ¼1 for isotropic stress
state and K0≠1 for anisotropic stress state, where K0
is coefficient of earth pressure at rest).
Database screening was applied, considering
soil type and laboratory specimen homogeneity.
Soil type was assessed based on sample descrip-
tion, review of particle size distribution and
Atterberg limits. Particularly for undisturbed test
specimens of transitional soil, specimens contain-
ing interbedded or non-uniform material, distinct
strata/layer changes or gravel were excluded from
further analysis because they can adversely affect
undisturbed sample quality and test processing
results for a premise of a homogeneous labora-
tory test specimen.
The screened database includes laboratory results
from 33 CU tests on reconstituted soil specimens (26
in sand and 7 in transitional soil) and 5 CU tests on
undisturbed soil specimens in transitional soil. The
specimen test depths ranged from 2 m to 38 m below
seafloor. Table 1 presents classification parameters for
the database used. Figure 3 presents results of two typ-
ical triaxial tests from the database, one in sand and
one in transitional soil.
Table 1. Classification parameters.
Parameter Sand Transitional soil
D
r
(%) 55-110 35-85
FC (%) 1-8 20-80
CC (%) - 3-24
C
u
(-) 1.5-3.8 5.3-80
D
50
(mm) 0.17-0.35 0.02-0.15
quartz content 85-100 84-95
(%)
particle shape subangular to well subangular to
rounded rounded
Notes: Transitional soil = (very) silty sand, clayey sand,
low plasticity (clayey, sandy) silt; D
r
= relative density; FC
= fines content; CC = clay content; C
u
= coefficient of uni-
formity; D
50
= particle diameter where 50 % of the dry
mass of soil has a smaller particle diameter
It is generally inconsistent and impractical to use
peak deviator stress as a criterion for deriving su for
dense dilative soils such as many of the ones in the
database used for this study. In dilative specimens,
large negative pore pressures develop until the end of
the test (to about 20 % axial strain; see blue line in
bottom plot of Figure 3) or until cavitation occurs.
487
Figure 3. Example of typical triaxial test results from the
database (applied back pressures: CIUcBE11 – Sand: 1291
kPa; CAUcBE09 – Transitional soil: 687 kPa).
Cavitation depends on the back pressure applied to the
triaxial test specimen. Sufficiently high back pressure
should be applied to test specimens that are expected
to exhibit dilative behaviour while shearing. It should
be noted that large negative pore pressures can be sus-
tained in a laboratory setting with controlled applica-
tion of (high) back pressure, but are typically not
observed during cone penetration. All tests of the data-
base had a back pressure which was at least equal to
the hydrostatic pressure at the depth point of each test
specimen.
Common criteria were reviewed for deriving su
from the CU data, i.e. peak deviator stress, peak stress
ratio, peak pore pressure, zero excess pore pressure
and limiting strain (refer to Brandon et al. (2006) for
background information on the various criteria for
interpretation of su). Peak stress ratio was selected as
the most practically useful and most consistent criter-
ion across the database. Therefore, this paper defines
su at ,where and are the effective
principal stresses.
2.4 Data pairing
Pairing of CPT data (qn) with CU test results con-
sidered the following:
– Laboratory test data were considered as primary,
because of single data points versus CPT profiling
data;
– Selection of CPT values for comparison with
the laboratory data from reconstituted soil speci-
mens focused on estimation of an equivalent
in situ relative density Dr of the reconstituted
soil specimen based on (1) specimen density
and (2) estimated values for minimum and max-
imum (index) dry densities:
where emax is maximum index void ratio, emin is
minimum index void ratio and e is specimen
void ratio. Selection of values for emin and emax
included assessment of laboratory test results
per soil unit, per soil type and site-wide;
– Final selection of Dr involved some engineer-
ing judgement, particularly for transitional soil
specimens, since the estimation of Dr inevit-
ably involves significant uncertainty, which
increases with increase of percentage fines.
The uncertainty in the selected values for emin
and emax should also be noted, particularly
since there are various test methods commonly
used in the industry that can give significant
differences, especially for the maximum
(index) dry density (Lunne et al., 2019). The
equivalent value of qn was then back-
calculated based on the following equation by
Kulhawy & Mayne (1990):
where Pa is atmospheric pressure;
– Selection of CPT values for comparison with
the laboratory data from undisturbed soil speci-
mens focused on CPT-borehole proximity, use
of CPT data showing the lower qn values and
relatively high values of soil behaviour type
index Ic, thereby accounting for the expected
bias in selection of the laboratory test speci-
mens, and allowance for small (< 1 m) depth
offsets between nearby CPT and sample bore-
hole locations.
3 CPT-BASED METHOD
The approach to estimate continuous profiles of
su involved analysis of the relationship between
derived values of su and qn, normalised to
effective vertical stress . This led to a bi-linear
relationship that is presented in Figure 4 and
Equation 3:
488
Figure 4. Undrained shear strength derived from in CU
tests on sand and transitional soil as a function of net cone
resistance.
Comments on Equation 3 are as follows:
– A best fit relationship based on linear least
squares regression was considered for values of
(n ¼26, R2 = 0.87, S.E. = 0.59);
– A constant value of for values of
qn
=σ05124 considering a mean value for
v
within this range, the wide scatter and the
absence of a significant trend (R2 = 0.38)
between su and qn in transitional soil;
– The method is robust and allows for develop-
ment of continuous profiles of su in sand and
transitional soil based solely on input from CPT
data, though it is noted that for transitional soil
engineering judgement has been applied;
– CPT parameter uncertainty for strongly layered
soil will be higher than for uniform soil (Peu-
chen and Terwindt, 2015). Note also that CPT
results are influenced by uncertainty related to
undrained, partially drained or drained condi-
tions during cone penetration, particularly in
transitional soil with drainage conditions influ-
enced by factors such as soil constituents and
(post-)depositional settings. Any of these condi-
tions may apply (DeJong and Randolph, 2012);
– The method covers medium dense to very dense
normally consolidated to slightly overconsoli-
dated silty to clean sands and sandy silts;
– Derived values of su in sand correspond to values
for cone factor Nkt ranging between 85 and 176
for the range , which represents more
than 95 % of the values in sand across the
wind farm sites;
– Derived values of su in transitional soil corres-
pond to values for cone factor Nkt ranging
between 28 and 107 for the range
20 qn
=σ075, which represents more than
v
95 % of the values in transitional soil
across the wind farm sites;
– Derived values of su are in good agreement with
the scatter of derived values presented in
Andersen (2015).
4 DISCUSSION AND CONCLUSIONS
The method, in combination with an equivalent
method for clays, enables derivation of continuous su
profiles at any CPT location within a given site. This
is particularly useful for offshore wind farm develop-
ments where one or multiple CPTs are performed per
wind turbine location without availability of location-
specific laboratory data. Soil behaviour type (i.e.
sand, transitional soil or clay behaviour) can be dis-
tinguished directly from CPT with application of gen-
eral or site-specific limits of soil behaviour type
indices such as Ic or IB (Robertson, 2016). Figure 5
presents an example profile from the HKW site.
Equation 3 can be applied with appropriate modi-
fications to produce design profiles of characteristic
values of su for use in foundation design calcula-
tions. To this purpose, the modifications would need
to consider at least the following (ISSMGE, 2021):
– Calculation model and its specified principles;
– Limit state and mobilised zone of ground;
– Loading regime and field drainage conditions;
– Transformation uncertainty of derived values to
characteristic values;
– Statistical evaluation accounting for statistical
fitting uncertainties within the given dataset.
For the horizontal portion of the bi-linear relation-
ship (i.e. the cut-off value for qn
=σ05124, see Equa-
v
tion 3), the following particular considerations also
apply for selection of characteristic values:
– A probable low value for should be used
for slightly overconsolidated soil that would be
in the order of magnitude for conventional
clays;
– Allowance should be made for overestimation of
su derived from undisturbed transitional soil spe-
cimens due to sample disturbance and subsequent
reduction of water content during reconsolidation
that can lead to soil phase transformation from
contractive to dilative (Andersen, 2015).
The method appears robust for two particular sites
at the North Sea. The two sites include multiple geo-
logical units and multiple soil types; further optimisa-
tion should be feasible by differentiation on the basis
of geological unit and soil type. Soil type differenti-
ation can consider CPT-based soil behaviour type indi-
ces, with confirmation by index sample data that can
easily be acquired in an offshore laboratory, such as
particle size distribution and particle shape by image
analysis (ISO, 2006). Further differentiation may also
allow wider application of CPT-based methods.
489
Figure 5. Example profile of su from the HKW site comprising three soil types (i.e. sand, transitional soil and clay). Sup-
plementary profiles of qn,I
c and Nkt are also displayed. Soil type is distinguished based on Ic (Ic52:05: Sand,
2:055Ic52:6: Transitional soil, Ic42:6: Clay). Note that in clay a CPT-based correlation, similar to Equation 3, was used.
Various advanced regression algorithms can be
trialled in order for the optimum results to be
obtained in terms of statistical evaluation of datasets,
including opportunities for potentially making better
use of the data by means of automated advanced
data analytics such as machine learning and big data.
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