A Reliable Correlation of SPT-CPT Data for Southwest of

A Reliable Correlation of SPT-CPT Data
for Southwest of Sweden
Abbas Abbaszadeh Shahri
Researcher, Department of Earth Sciences, Uppsala University, Uppsala, Sweden
e-mail: [email protected] (Corresponding author)
Christopher Juhlin
Professor, Department of Earth Science, Uppsala University Uppsala, Sweden
e-mail: [email protected]
Alireza Malemir
Associated professor, Department of Earth Science, Uppsala University Uppsala,
Sweden
e-mail: [email protected]
ABSTRACT
The requirement for reliable SPT–CPT correlation can be useful for application of CPT data
in the existence of SPT design correlations and when only SPT data were available, for
someone who is more familiar with CPT, it is possible to convert the SPT data to CPT cone
resistance. Hence, our emphasis in this study is to determine a reliable correlation of CPTSPT by a detail comparison with other researchers in various mathematical relations for Lilla
Edet area in southwest of Sweden. To get the aim, by “Abbas Converter 3.01” a generated C#
GUI computer code which is developed for CPT data processing, a high accuracy data
processing and interpretation were implemented and the soil types were determined. After
reviewing of the published CPT-SPT correlations we eliminate some of them because of not
taking into account the statistical procedures. In next step of this study by use of arithmetic
average method, Student t-test and statistical analysis for field and normalized data set and
then using a filtering procedure for elimination of far from trend data and application of
several mathematical curve fitting tools, the correlation for three condition (linear with zero
intercept, linear, power) were obtained and compared to each other. Comparison of obtained
results by previous works showed good agreement and moreover, the results showed that
filtered data have higher correlation coefficient but because of the applied accuracy in data
processing this differences is no significant.
KEYWORDS: CPT-SPT correlation, filtering procedure, soil type, “Abbas Converter
3.01.
INTRODUCTION
Among the various types of in situ tests, the Cone penetration Test (CPT) and the Standard
Penetration Test (SPT) are relied on for estimating soil properties or directly designing
foundations. The CPT is the most effective in-situ test method for obtaining practically
continuous soil properties reliably. It has used to determine the geotechnical engineering
properties of soils and delineating soil stratigraphy. It is becoming increasingly steadily, widely
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used and more popular for site investigation and geotechnical design and is one of the most used
and accepted methods for soil investigation worldwide. On the other hand, the SPT is one of the
oldest and most common in situ tests used for soil exploration in geotechnical applications and
foundation design in several countries in the world (Nixon, 1982; Décourt, 1990).
By attention to applicability of these two methods, correlations between SPT and CPT data
are of practical interest in the geotechnical engineering and several correlations have been
developed through regression analyses for collected CPT and SPT data.
It is very valuable to correlate the cone tip resistance (qc), to SPT (N-value) so that the
available database of the field performances and property correlations with N-value could be
effectively utilized. The main objective was to use CPT data in the well established SPT-based
design approaches, or alternatively convert SPT blow counts into CPT tip resistance in cases
where the CPT-based geotechnical correlations are more reliable.
The main objective of the present paper is to propose CPT-SPT relationships for various
recognized soil layers, particularly in clayey soils with significant clay content in an area in
southwest of Sweden. In this study, an indication of accuracy of the correlations provided and
then a comparison with the published measurements executed.
CORRELATION OF CPT-SPT
This started in the early 1980s with the early work of Douglas and Olsen (1981) and then
Robertson et al., (1983) carried out an extensive review of CPT –SPT correlations corrected to
60% energy ratio (N60). Although many authors proposed different correlations, it is quite
recognizable that authors did not indicate the geology and geomorphology in their correlative
works. The only indication of geology was given by Robertson et al. (1983), where they
mentioned over consolidation. Robertson et al. (1986) proposed a CPT-SPT correlation where the
ratio between normalized cone tip resistance (qc/Pa) and N60 was given for different soil types
determined from their soil behavior type classification chart. Kulhawy and Mayne (1990)
extended the Robertson et al. (1983) correlation based on additional data that became available to
them in the late 1980s and developed a mathematical expression for their updated SPT-CPT
correlation.
𝑞
Sanglerat (1972) cites Meyerhof (1965) who suggested a relationship 𝑛 = 𝑐 = 0.4 (qc in
𝑁
MPa); but further, Meigh and Nixon (1961) showed that this simple relationship did not take into
account the effect of grain size and made comparative tests in sand and gravel (Akca, 2003).
Lunne et al. (1997) cite Jefferies and Davies (1993) who presented a soil classification chart
estimating N- values. This new development considers qc by taking into account pore water
pressure (u) and overburden stress (σ’v0), using piezocone.
On the basis of available correlation forms between CPT and SPT, these relationships can be
categorized in four main groups. Most of the empirical correlations considered a constant value of
𝑞 +𝑓
qc/N and some others proposed constant values for 𝑛 = 𝑐 𝑠 for different soil types as shown in
𝑞
𝑁
table (1). New investigations suggested 𝑛 = 𝑐 as a function of mean grain size (Robertson et al.,
𝑁
1983; Seed & DeAlba, 1986; Kulhawy and Mayne, 1990; Stark and Olson, 1995; Emrem and
Durgunoglu, 2000) or fines content (Muromachi, 1981; Jamiolkowski et al., 1985; Kasim et al.,
1986; Chin et al., 1988; Kulhawy and Mayne, 1990; Jefferies and Davies, 1993).
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Since some design methodologies have only been developed for SPT blow counts, the CPT
tip resistance is sometimes correlated to SPT blow counts. It is recommended that the normalized
cone tip resistance (qc, 1) or the normalized cone tip resistance adjusted for the effects of “fines”
(qc, 1, mod) be normalized and corrected and then correlated to normalized SPT values N1, 60 or N1,
60, cs. Jefferies and Davies (1993) proposed the following equations to correlate the CPT tip
resistance to the SPT blow count.
𝑁1(60) =
𝑁1(60) =
Where;
𝑞𝑐,1
(1)
𝐼
8.5 (1− 𝑐 )
4.75
𝑞𝑐,1,𝑚𝑜𝑑
(2)
𝐼
8.5 (1− 𝑐 )
4.75
qc,1 = Normalized CPT cone tip resistance (ton/ft2)
qc,1,mod = Normalized CPT cone tip resistance adjusted for “fines” (ton/ft2)
Ic = Soil behavior type and computed using normalized tip resistance (QT), normalized sleeve
friction (FR), and normalized pore pressure (Bq) by the following equations.
𝑄𝑇 =
𝑞𝑐,1 −𝜎𝑣
𝐹𝑅 = (
𝐵𝑞 =
𝜎𝑣′
𝑓𝑠,1
𝑞𝑐,1 −𝜎𝑣
𝑢2 −𝑢0
𝑞𝑡 −𝜎𝑣
(3)
(4)
) × 100
(5)
2
𝐼𝑐 = ��3 − 𝐿𝑜𝑔 �𝑄𝑇 �1 − 𝐵𝑞 �� + [1.5 + (1.3𝐿𝑜𝑔(𝐹𝑅 ))]2
Where;
(6)
fs, 1 = Where fs is the normalized CPT cone tip resistance; σv'= Effective overburden
pressure; σv= Total overburden pressure; u2 = Pore pressure measurement located on the tip
shoulder; u0 = Hydrostatic water pressure.
Robertson et al. (1983) and Kulhawy and Mayne (1990) proposed a mathematical form of
CPT –SPT correlation on the basis of soil median size and fine content (FC) as below.
𝑞
� 𝑐�
�
𝑝𝑎
� = 7.735 (𝐷50 )0.28
𝑁60
𝑞
� 𝑐�
�
𝑝𝑎
𝑁60
𝑞
� 𝑐�
�
𝑝𝑎
𝑁
� = 6.53 (𝐷50 )0.26
� = 4.25 −
𝐹𝐶
41.3
(7)
(8)
(9)
Lunne et al. (1997) upgraded the CPT-SPT correlation developed by Robertson et al. (1986)
to overcome the discontinuity in the correlation when moving from one Ic to another. They
developed a mathematical continuous expression using a modified version of the Ic of Jefferies
and Davies (1993) in the following form.
𝑞
� 𝑐�
�
𝑝𝑎
𝑁60
� = 8.5 − (1 −
𝐼𝑐
4.6
)
(10)
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Table 1: Obtained relationships for CPT-SPT
Researcher (s)
De Alencar Velloso (1959)
Meigh and Nixon (1961)
Engineers Franki Piles (1960)
(From Acka, 2003)
Schmertmann (1970)
Barata et al., (1978)
Ajayi and Balogun (1988)
Chang (1988)
Danziger and De Valleso (1995)
* qc/N (bar/30cm)
Danziger et al., (1998)
* qc/N (bar/30cm)
Emrem and Durgunoglu (2000)
Acka (2003)
Soil type
Clay and silty clay
Sandy clay and silty sand
Sandy silt
Fine sand
Sand
Coarse sand
Gravelly sand
Sand
Clayey sand
Silty sand
Sandy clay
Silty clay
Clays
Silt, sandy silt and silt-sand mix.
Fine to medium sand, silty sand
Coarse sand, sand with gravel
Sandy gravel and gravel
Sandy silty clay
Clayey silty sand
Lateritic sandy clay
Residual sandy clay
Sandy clayey silt
Clayey silt, sandy clayey silt
Silt, sandy silt and silt-sand
Fine to medium sand, silty sand
Coarse sand, sand with gravel
Sandy gravel and gravel
Silty sand
Sand
Silty sand, Silty clay
Clayey silt
Clay, silt and sand mixtures
Clayey sand and silty clay
Sandy clay
Clay
Turkey soils
Sand
Silty sand
Sandy silt
Proposed relationship
n=(qc/N)=0.35
n=(qc/N)=0.2
n=(qc/N)=0.35
n=(qc/N)=0.6
n=(qc/N)=1.00
n=(qc/N)=0.2
n=(qc/N)=0.3-0.4
n=(qc/N)=1.00
n=(qc/N)=0.6
n=(qc/N)=0.5
n=(qc/N)=0.4
n=(qc/N)=0.3
n=(qc/N)=0.2
n=([qc+fs]/N)=0.2
n=([qc+fs]/N)=0.3-0.4
n=([qc+fs]/N)=0.5-0.6
n=([qc+fs]/N)=0.8-1.0
n=(qc/N)*=1.5-2.5
n=(qc/N)*=2.0-3.5
n=(qc/N)*=3.2
n=(qc/N)*=4.2
n=(qc/N)*=2.1
n=(qc/N)*=1.8
n=([qc+fs]/N)=0.2
n=([qc+fs]/N)=0.3-0.4
n=([qc+fs]/N)=0.5-0.6
n=([qc+fs]/N)=0.8-1.0
n=(qc/N)*=7.0
n=(qc/N)*=5.7
n=(qc/N)*=5.0-6.4
n=(qc/N)*=3.1
n=(qc/N)*=1.0-3.5
n=(qc/N)*=4.6-5.3
n=(qc/N)*=1.8-3.5
n=(qc/N)*=4.5
n=(qc/N)=func (D50)
n=(qc/N)=0.77
n=(qc/N)=0.70
n=(qc/N)=0.58
STUDY AREA AND AVAILABLE DATA
The Göta River is the largest river in Sweden runs from Lake Vänern to Goteborg, following
the Göta River Zone, which is an approximately 4 km wide fault zone dipping towards the west,
characterized by varied countryside that has been formed through natural erosion and landslide
processes. A number of landslides of varying sizes occur along the river every year, and
landslides are much more common in this area than in other parts of the country (Göransson et
al., 2009; Löfroth et al., 2011). The primary reasons for the high frequency of landslides in the
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Göta River valley are its geological formation, with immense, soft clay layers that were once
deposited in a marine environment, the varying flow within the river which causes erosion, and
the effect of the expansion and activities of the society that surrounds it (Swedish Geotechnical
Institute, 2012).
The study area is located on the east side shoreline of the Göta River near a quick-clay
landslide scar occurred about 30-40 years ago, 7 km north of the municipality of Lilla Edet and
60 km north of Göteborg as shown in Figure 1. A total of 8 geotechnical test points (7201, 7202,
7203, 7205, 7206, 7207 and 7208) with a maximum depth of 38m were available for this study.
The detected ground water table in these points varies between 1 to 1.7m from the subsurface.
The CPT was performed in the test points 7203 and 7205 in the eastern part, 7202 in the landslide
scar and 7207 and 7208 in the western part of the studied area as presented in Figure 1.
Figure 1: Overlapped of distribution landslide risk (Geological Survey of Sweden;
http://www.sgu,se), location of know landslides in Sweden (Anderson-Sköld et al., 2013),
location of selected area and available test points
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ANALYSIS METHOD
All of CPT data set used in this study is from the shore line of Göta River. The collected data
separated on the basis CPT positions in the field, and hence the data without location map were
not used in the present paper. The used set test point locations in our study has no significant
distance and mostly has a distance less than 30m form each other.
The processing operation in this study was executed by developed graphical user interface C#
computer code namely as “Abbas Converter 3.01”. This code is developed on the platform of its
previous generated versions by Abbaszadeh Shahri (2010) and Abbaszadeh Shahri et al., (2012,
2013). This code is capable of reading geotechnical data, screening, standardizing, preparing a
unified applicable dataset from data source and performing corrections. It is also able to
determine the geotechnical site class.
Schmertmann (1978) and Douglas and Olsen (1981), introduced charts for data interpretation,
however, in recent years the chart proposed by Robertson et al. (1986) has become very popular
(Long, 2008). Therefore, one of the main advantages of this developed code is that it uses several
proposed criteria by Robertson et al (1986), Campanella and Robertson (1988), Lunne et al
(1997), Robertson (1990), Jefferies and Davies (1993), Robertson and Wride (1998), Boulanger
and Idriss (2004) and Youd et al. (2001) for data corrections and modification of performed
corrections.
By this knowledge that CPT has more readings in 30cm than SPT (only 1 reading), in
statistical point of view, the number of readings is not equal, then direct correlations is not
possible and hence, an average should take into account for CPT readings. In the present paper,
the cone resistance (qc) are the average values over a length of 30cm intervals where the
corresponding N-values were measured. This was compared with the SPT N-value situated over
the same depth range. When choosing the level, the first thing considered was what depth was the
SPT accomplished. Then, the cone resistance values were averaged over 30cm at the same level.
In this study for determination of the soil type index (ISBT), normalized cone resistance (QT), N
and N60, we use of Robertson et al (1986; 2010a, b, c), Liao and Whitman (1986), Liao et al
(1988), Youd et al (2001) by the following equations.
QT =
where;
qt −σv
p
pa
p
× � a′ �
σv
n
CN = ( a′ )n … . . Nfield = CN × N60
σv
σ′
n = 0.381(ISBT ) + 0.05 � v � − 0.15
pa
P
N60 = Nfield × � a′ �
σv
0.5
× ER
(11)
(12)
Pa: atmospheric pressure; n: Initial stress exponent that varies with SBT; CN: correction factor
for overburden pressure, Nfield: Measured SPT N-value; N60: normalized and corrected N-value
for 60% energy ratio
By using the conversion chart developed by Olsen (1988) and choosing the proposed criteria
by Olsen and Stark (2003), because of its formulation in terms of N60, the equivalent SPT N-value
and corrected N60 were computed. In this paper, the Nfield refers to obtained N-value and N60
refers to normalized and corrected N-value.
After determination of SPT-N for both case data (field and normalized), we set up this study
in several steps as soil layers recognition, computing the n-value and application of arithmetic
average method, application of student t-test, statistical analysis, data filtration, finding
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correlation between all and filtered data and finally application of original modified graphs to
verify the obtained results. The modular applied steps are presented in Figure 2.
Soil layers recognition
Because of reported of the type of occurred landslide in the studied area and its close relation
to fine-grained soil composed of clay and silt, the authors decided to have a detail investigation
on CPT data to determine the available soil layers. According to this point, the main recognized
soil layers in this area were categorized in sandy silt to clayey silt (0.361<qc<13.173, 1< Nfield<24,
2< N60<18), silty sand to sandy silt (0.241<qc<11.68, 1< Nfield<22, 2< N60<19), sensitive fine
grain clay (0.211<qc<2.5905, 1< Nfield<6, 1< N60<7), sand (0.47<qc<16.706, 1< Nfield<29, 1<
N60<30), gravelly sand to sand (0.478<qc<21.742, 1< Nfield<36, 2< N60<36) and sometimes clayey
silt to silty clay. In this case to modify the recognized soil types a chart analysis using original
proposed graph by Jefferies and Been (2006) for all data were executed and presented in Figure 3.
Figure 2: Modular connection of the applied steps in this study
Figure 3: Distribution of all CPT data for soil type description in the studied area
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Calculation of n-value and arithmetic average method
After recognition of the soil layers and types in the selected area, at the first, the variation of
qc-N for recognized soil types were plotted (Figure 4) and then the arithmetic average method for
𝑞
calculation of the correlation of 𝑛 = 𝑐 for each 30cm, were used and the results presented in
𝑁
table (2).
Table 2: Obtained results from arithmetic average method for all of available data
Soil Type
Number of n
Silty sand
Clay
Sandy silt
Sand
Gravelly sand to sand
77
85
36
36
18
n value (Nfield)
Max
Min
Ave.
0.602 0.205 0.374
0.541 0.166 0.367
0.602 0.234 0.423
0.614 0.229 0.529
0.983 0.398 0.572
n value (N60)
Max
Min
Ave.
0.673 0.121 0.332
0.549 0.125 0.277
0.749 0.156 0.358
0.647 0.228 0.533
0.820 0.239 0.609
Application of Student t-test, statistical analysis and
filtering procedure
In this step, to determine whether there is any relation between qc and SPT or not the Student
t-test is performed and a relation is observed between qc and SPT. Hence, after the arithmetic
average method and comparison of the results, we executed a statistical analysis. This analysis
was performed in two various states including all obtained data for each soil type and filtered
data. The filtering procedure which is defined as 𝑋� ± 2𝜎 (𝑋� is mean value of ‘n’ and 𝜎 is the
standard deviation of the mean value of n were disregarded by using 95% of the data is still
allowed in the investigation range) aimed to remove data situated far from the general trend. After
data filtering and elimination, the same trend should be confirmed to be maintained in the SPT-qc
plot. Then, to determine the correlation functions between qc-SPT depending on soil types and
using the least square method, the Matlab curve fitting tool and curve expert mathematical
software for linear (𝑞𝑐 = 𝑎𝑁, 𝑞𝑐 = 𝑎𝑁 + 𝑏) and power (𝑞𝑐 = 𝑎𝑁 𝑏 ) regression were
implemented. The correlation functions were determined for two cases data including all and
filtered in the both condition of Nfield ,N60. The results of this step are presented in Figure 5
(𝑞𝑐 = 𝑎𝑁) and Figure 6 (𝑞𝑐 = 𝑎𝑁 + 𝑏, 𝑞𝑐 = 𝑎𝑁𝑏 ) for all data, Figure 7 (𝑞𝑐 = 𝑎𝑁) and Figure 8
(𝑞𝑐 = 𝑎𝑁 + 𝑏, 𝑞𝑐 = 𝑎𝑁 𝑏 ) for filtered data. The numerical results of these correlations are
presented in table (3).
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22
20
Cone tip resistance (qc)
18
16
14
12
10
8
6
4
2
0
SPT-N value
0
2
4
6
8 10 12 14 16 18 20 22 24 26 28 30 32 34 36
Silty sand (Field)
Silty sand (Normalized)
Clay (Field)
Clay (Normalized)
Sandy silt (Field)
Sandy silt (Normalized)
Sand (Field)
Sand (Normalized)
Gravelly sand to sand (Field)
Gravelly sand to sand (Normalized)
Figure 4: Variation of qc-SPT for soil types in the selected area
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16
14
12
qc (MPa)
10
8
6
4
2
0
SPT-N
0
2
4
6
8
10 12 14 16 18 20 22 24 26 28 30
Silty sand (All Field data)
Linear (Nfield)
Silty sand (All normalized data)
Linear (N60)
Clay (All field data)
Linear (Nfield)
Clay (All normalized data)
Linear (N60)
Sandy silt (All field data)
Linear (Nfield)
Sandy silt (All normalized data)
Linear (N60)
Sand (All field data)
Linear (Nfield)
Sand (All normalized data)
Linear (N60)
Gravelly sand to sand (All field data)
Linear (Nfield)
Gravelly sand to sand (All normalized data)
Linear (N60)
Figure 5: Results of linear correlation (𝑞𝑐 = 𝑎𝑁) for all data
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Sand (All field data)
Linear (Nfield)
Power (Nfield)
4
4
6
8
10
12 14 16
Power (N60)
qc (MPa)
6
2
SPT-N
18 20 22
24 26 28
0
30
SPT-N
0
2
12
3.0
2.0
1.5
10
Power (Nfield)
8
7
Linear (N60)
6
8
10
12
14
16
18
20
22
24
Power (N60)
qc (MPa)
5
qc (MPa)
1.0
6
Power (Nfield)
Silty sand
(All normalized data)
Linear (N60)
9
Power (N60)
4
Silty sand (All field data)
Linear (Nfield)
11
Clay (All field data)
Clay (All Normalized data)
Linear (Nfield)
2.5
4
3
2
0.5
1
SPT-N
0
1
2
3
4
5
6
7
0
SPT-N
0
2
4
6
8
10
12
14
16
18
20
22
22
Gravelly sand to sand (All field data)
Linear (Nfield)
20
18
Power (Nfield)
16
12
Gravelly sand to sand
(All normalized data)
Linear (N60)
10
Power (N60)
14
8
qc (MPa)
0.0
Sandy silt
(All normalized data)
Linear (N60)
8
Power (N60)
2
Power (Nfield)
10
Sand (All normalized data)
Linear (N60)
0
Sandy silt (All field data)
Linear (Nfield)
12
qc (MPa)
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
0
1023
6
4
2
0
SPT-N
0
2
4
6
8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38
Figure 6: Results for linear (𝑞𝑐 = 𝑎𝑁 + 𝑏) and power (𝑞𝑐 = 𝑎𝑁 𝑏 ) correlations for all data
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24
22
Silty sand (Filtered)
Linear
Clay (Filtered)
Linear
Sandy silt (Filtered)
Linear
Sand (Filtered)
Linear
Gravelly sand to sand
Linear
20
18
16
14
12
10
qc (MPa)
8
6
4
2
0
SPT-N
0
2
4
6
8
10
12
14
16
18
20
22
24
26
Figure 7: Results of linear correlation (𝑞𝑐 = 𝑎𝑁) for filtered data
Table 3: Numerical results for all available data
Soil type
Field data
Silty sand
Clay
Sandy silt
Sand
Gravelly sand to sand
Normalized data
Silty sand
Clay
Sandy silt
Sand
Gravelly sand to sand
Filtered data
Silty sand
Clay
Sandy silt
Sand
Gravelly sand to sand
qc=aN
Correlation
qc=aN+b
qc=aNb
0.442N
(R2=0.83)
0.321N
(R2=0.71)
0.527N
(R2=0.88)
0.568N
(R2=0.88)
0.613N
(R2=0.84)
0.456N
(R2=0.80)
0.280N
(R2=0.70)
0.599N
(R2=0.86)
0.577N
(R2=0.86)
0.648N
(R2=0.85)
0.46N
(R2=0.87)
0.308N
(R2=0.82)
0.528N
(R2=0.88)
0.568N
(R2=0.88)
0.613N
(R2=0.84)
0.521N-0.437
(R2=0.85)
0.272N+0.165
(R2=0.74)
0.564N-0.377
(R2=0.89)
0.605N-0.842
(R2=0.89)
0.617N - 0.098
(R2=0.84)
0.608N-0.914
(R2=0.84)
0.287N-0.0245
(R2=0.70)
0.800N-1.375
(R2=0.88)
0.61N-0.755
(R2=0.87)
0.626N + 0.509
(R2=0.85)
0.534N-0.476
(R2=0.88)
0.253N+0.183
(R2=0.85)
0.563N-0.366
(R2=0.89)
0.605N-0.842
(R2=0.89)
0.617N - 0.098
(R2=0.84)
0.346N1.031
(R2=0.81)
0.432N0.739
(R2=0.81)
0.385N1.079
(R2=0.85)
0.336N1.158
(R2=0.87)
0.3975N1.13
(R2=0.88)
0.201N1.373
(R2=0.85)
0.274N1.015
(R2=0.71)
0.194N1.465
(R2=0.87)
0.271N1.235
(R2=0.86)
0.348N1.204
(R2=0.87)
0.282N1.212
(R2=0.89)
0.409N0.779
(R2=0.85)
0.397N1.066
(R2=0.87)
0.336N1.158
(R2=0.87)
0.3975N1.13
(R2=0.88)
Vol. 19 [2014], Bund. E
12
11
Clay (Filtered)
Linear
Power
Linear
Power
9
8
qc (MPa)
7
6
5
4
3
2
1
SPT-N
1
2
3
4
5
20
16
14
12
qc (MPa)
10
8
6
4
2
SPT-N
0
2
4
6
0
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
0
Gravelly sand to sand (Filtered)
Linear
Power
18
SPT-N
0
6
8 10 12 14 16 18 20 22 24 26 28 30
2
4
6
8
10
12
14
16
18
20
22
Sand (Filtered)
Linear
Power
qc (MPa)
0
22
0
Silty sand (Filtered)
10
qc (MPa)
1.9
1.8
1.7
1.6
1.5
1.4
1.3
1.2
1.1
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
1025
SPT-N
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
14
12
Sandy silt (Filtered)
Linear
Power
10
qc (MPa)
8
6
4
2
0
SPT-N
0
2
4
6
8
10
12
14
16
18
20
22
24
Figure 8: Results of linear (𝑞𝑐 = 𝑎𝑁 + 𝑏) and power (𝑞𝑐 = 𝑎𝑁 𝑏 ) correlations for filtered data
DISCUSSION
By refer to table (1), in this study the obtained n-value for Nfield for detected soils have good
adaptability with the defined range by other researchers and the differences can be interoperated
by soil conditions. In the studied area the general recognized soil types are fine grained and also
Vol. 19 [2014], Bund. E
1026
sometimes they have very thin layers in other recognized thicker layer and in the present study we
ignore this very thin layer and it may be the reason of differences between our results in n-value
with others. More than some of the proposed value is for a wide range of soils with different
characteristics. For example, the proposed values by Acka (2003) are for United Arabic Emirate
because of cemented layers, densification, shell fragments and occasionally gravel and gypsum
bands shows high values. This can be another logical reason for the differences between our
obtained results and other researchers. The comparison between the results of this study with
other researchers have provided in table (3).
Table 3: Comparison of obtained results in this study by other researchers
Researcher (s)
Soil type
De Alencar Velloso (1959)
Clay and silty clay
Sandy clay and silty sand
Sandy silt
Fine sand
Gravelly sand
Clayey sand
Silty sand
Clays
Silt, sandy silt and silt-sand mix.
Fine to medium sand, silty sand
Coarse sand, sand with gravel
Clayey silty sand
Silt, sandy silt and silt-sand
Fine to medium sand, silty sand
Coarse sand, sand with gravel
Sand
Silty sand, Silty clay
Clay, silt and sand mixtures
Sand
Silty sand
Sandy silt
Meigh and Nixon (1961)
Franki Piles (1960)
Schmertmann (1970)
Barata et al., (1978)
Chang (1988)
Danziger and De Valleso (1995)
Danziger et al., (1998)
Acka (2003)
Proposed
value
0.35
0.2
0.35
0.6
0.3-0.4
0.6
0.5
0.2
0.2
0.3-0.42
0.5-0.6
0.2-0.352
0.22
0.3-0.42
0.5-0.6
0.57
0.5-0.642
0.1-0.35
0.77
0.702
0.58
This study
Condition
0.367
0.374
0.423
0.529
0.572
0.529
0.374
0.367
0.423
0.374
0.572
0.374
0.423
0.374
0.572
0.529
0.374
0.367
0.529
0.374
0.423
OK
More
OK
OK
More
OK
Less
More
More
OK
OK
OK
More
OK
OK
OK
Less
OK
Less
Less
Less
Obtained correlations for detected soil types in this region is physically impossible for N=0.
However; these correlations are for the results of this area, but when the value of N from the SPT
is high (75≤N≤100) no correlation will be exist with qc. Acka (2003) mentioned that, Meyerhof
(1965) has proposed that the relationship between the two values not be extended beyond values
of qc greater than 20 MPa (Sanglerat, 1972).
Scale of this study and applied high accuracy in data processing are two main reasons that the
correlation coefficients for field and normalized data shows good values between 0.70- 0.89. In
the field data, linear correlation without intercept has better values for silty sand, sandy silt and
sand, but for clay and gravelly sand to sand the power correlation show higher values. In the case
of normalized data, linear correlation for sandy silt and sand is better than the others, but silty
sand, clay and gravelly sand to sand have better values in power correlation.
After application of filtering procedure and elimination of data far from the general trend, the
range of correlation coefficient varies between 0.82- 0.89 which shows higher values. In this case,
power correlation has higher value for silty sand, clay and gravelly sand to sand, but for sandy silt
and sand the linear correlation show higher values. However, in general, the obtained correlation
Vol. 19 [2014], Bund. E
1027
coefficients in this study are not very different from each other so that a simpler correlation can
be used.
To validate the obtained results we use the original published graph by Robertson et al
(1983), but it needs to mean grain sizes (D50). In this study mean grain sizes from the sieve
analysis were not available and hence we forced to use visual description of CPT test and the
available equations. By attention to this point that qc/N ratio increases with grain size increasing
(Robertson et al., 1983), all and then derived data from the arithmetic average and statistical
method plotted as shown in Figure s 9 and 10 which the recognized soils in the selected area,
shows better fit and good agreement with those found in the literature. However; in some cases,
because of ignoring from the very thin soil layers in our calculation, an extended distribution can
be seen. More that, scale of our study and also applied high accuracy in data processing and
interpretation could be the reason why the qc/N ratio found in the southwest of Sweden would be
similar to those shown in the original Figure . The reason for this may be that the statistical
analysis eliminates the data far from the general trend and gives artificially modified results.
Figure 9: Comparison of all available data with obtained results by Robertson et al (1983)
Vol. 19 [2014], Bund. E
1028
Figure 10: Comparison of the obtained results by arithmetic average and statistical analysis for
all and filtered data
CONCLUSION
In this paper we attempted to present and develop an efficient generated C# computer code
which uses several proposed equation for CPT data processing and corrections. Moreover we
used several known criteria to modify the corrected data and also applied geotechnical
characteristics in our work.
At the first by high accuracy, all of the processed CPT data were interpreted and relevant
soils according to corrected data in the studied area were classified and by this way we were able
to provide a high resolution dataset including field, normalized and filtered data according to soil
types for facilitate and better analysis.
Comparison between the obtained results of this study by arithmetic average method with
finding of by other researchers showed that our results for clay, silty clay and sandy silt have
good agreement with defined range by De Alencar Velloso (1959) and Danziger et al., (1998), but
for silty sand the better conditions can be observed with Schmertmann (1970), Danziger and De
Valleso (1995) and Barata et al., (1978). In case of sand the results with a good adaptability can
be found with De Alencar Velloso (1959), Engineer Franki Piles (1960) and Danziger and De
Valleso (1995) but for gravelly sand to sand our results shows better compatibility with
Vol. 19 [2014], Bund. E
1029
Schmertmann (1970) and Danziger and De Valleso (1995) and logical reasons of differences
between our results by available published ones were explained. To determine a reliable
mathematical relation between CPT and SPT, the Student t-test and statistical analysis approach
with a filtering procedure were applied and the best correlation (Linear with zero intercept, Linear
and power) for all, normalized and filtered data was found and compare with each other for any
recognized soil types. Obtained results from student t-test with statistical analysis show that in all
data section, the linear correlation for silty sand, sandy silt and sand has better coefficient but for
in normalized data the power correlation for silty sand, clay and gravelly sand to sand shows
higher coefficient, but linear correlation for sandy silt and sand is more than other ones. By
application of filtering procedure, the correlation coefficients were improved and in this case
sandy silt and sand followed the linear correlation but silty sand, power correlation for clay and
gravelly sand to sand shows better values.
Comparison of obtained results by original published graph for all and then derived data from
the arithmetic average and statistical method for recognized soils for all and filtered data in the
selected area, shows appropriate fit and good agreement with those found in the literature.
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