Shari et al, 2014

Prévia do material em texto

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: abbas.abbaszadeh@geo.uu.se (Corresponding author) 
Christopher Juhlin 
Professor, Department of Earth Science, Uppsala University Uppsala, Sweden 
e-mail: christopher.juhlin@geo.uu.se 
Alireza Malemir 
Associated professor, Department of Earth Science, Uppsala University Uppsala, 
Sweden 
e-mail: alireza.malemir@geo.uu.se 
 
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 CPT-
SPT 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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mailto:christopher.juhlin@geo.uu.se
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Vol. 19 [2014], Bund. E 1014 
 
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. 
Sanglerat (1972) cites Meyerhof (1965) who suggested a relationship 𝑛 = 𝑞𝑐
𝑁
= 0.4 (qc in 
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 
qc/N and some others proposed constant values for 𝑛 =
𝑞𝑐+𝑓𝑠
𝑁
 for different soil types as shown in 
table (1). New investigations suggested 𝑛 = 𝑞𝑐
𝑁
 as a function of mean grain size (Robertson et al., 
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) =
𝑞𝑐,1
8.5 (1− 𝐼𝑐4.75)
 (1) 
𝑁1(60) =
𝑞𝑐,1,𝑚𝑜𝑑
8.5 (1− 𝐼𝑐4.75)
 (2) 
Where; 
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
𝜎𝑣′
 (3) 
𝐹𝑅 = (
𝑓𝑠,1
𝑞𝑐,1−𝜎𝑣
) × 100 (4) 
𝐵𝑞 =
𝑢2−𝑢0
𝑞𝑡−𝜎𝑣
 (5) 
𝐼𝑐 = ��3 − 𝐿𝑜𝑔 �𝑄𝑇�1 − 𝐵𝑞���
2
+ [1.5 + (1.3𝐿𝑜𝑔(𝐹𝑅))]2 (6) 
Where; 
fs, 1 = Where fs is the normalized CPTcone 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. 
�
�𝑞𝑐𝑝𝑎
�
𝑁60
� = 7.735 (𝐷50)0.28 (7) 
�
�𝑞𝑐𝑝𝑎
�
𝑁60
� = 6.53 (𝐷50)0.26 (8) 
�
�𝑞𝑐𝑝𝑎
�
𝑁
� = 4.25 − 𝐹𝐶
41.3
 (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. 
�
�𝑞𝑐𝑝𝑎
�
𝑁60
� = 8.5 − (1 − 𝐼𝑐
4.6
) (10) 
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Table 1: Obtained relationships for CPT-SPT 
Researcher (s) Soil type Proposed relationship 
De Alencar Velloso (1959) Clay and silty clay n=(qc/N)=0.35 
 Sandy clay and silty sand n=(qc/N)=0.2 
 Sandy silt n=(qc/N)=0.35 
 Fine sand n=(qc/N)=0.6 
 Sand n=(qc/N)=1.00 
Meigh and Nixon (1961) Coarse sand n=(qc/N)=0.2 
 Gravelly sand n=(qc/N)=0.3-0.4 
Engineers Franki Piles (1960) Sand n=(qc/N)=1.00 
(From Acka, 2003) Clayey sand n=(qc/N)=0.6 
 Silty sand n=(qc/N)=0.5 
 Sandy clay n=(qc/N)=0.4 
 Silty clay n=(qc/N)=0.3 
 Clays n=(qc/N)=0.2 
Schmertmann (1970) Silt, sandy silt and silt-sand mix. n=([qc+fs]/N)=0.2 
 Fine to medium sand, silty sand n=([qc+fs]/N)=0.3-0.4 
 Coarse sand, sand with gravel n=([qc+fs]/N)=0.5-0.6 
 Sandy gravel and gravel n=([qc+fs]/N)=0.8-1.0 
Barata et al., (1978) Sandy silty clay n=(qc/N)*=1.5-2.5 
 Clayey silty sand n=(qc/N)*=2.0-3.5 
Ajayi and Balogun (1988) Lateritic sandy clay n=(qc/N)*=3.2 
 Residual sandy clay n=(qc/N)*=4.2 
Chang (1988) Sandy clayey silt n=(qc/N)*=2.1 
 Clayey silt, sandy clayey silt n=(qc/N)*=1.8 
Danziger and De Valleso (1995) Silt, sandy silt and silt-sand n=([qc+fs]/N)=0.2 
 Fine to medium sand, silty sand n=([qc+fs]/N)=0.3-0.4 
 Coarse sand, sand with gravel n=([qc+fs]/N)=0.5-0.6 
 Sandy gravel and gravel n=([qc+fs]/N)=0.8-1.0 
* qc/N (bar/30cm) Silty sand n=(qc/N)*=7.0 
Danziger et al., (1998) Sand n=(qc/N)*=5.7 
 Silty sand, Silty clay n=(qc/N)*=5.0-6.4 
* qc/N (bar/30cm) Clayey silt n=(qc/N)*=3.1 
 Clay, silt and sand mixtures n=(qc/N)*=1.0-3.5 
 Clayey sand and silty clay n=(qc/N)*=4.6-5.3 
 Sandy clay n=(qc/N)*=1.8-3.5 
 Clay n=(qc/N)*=4.5 
Emrem and Durgunoglu (2000) Turkey soils n=(qc/N)=func (D50) 
Acka (2003) Sand n=(qc/N)=0.77 
 Silty sand n=(qc/N)=0.70 
 Sandy silt 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 =
qt−σv
pa
× �pa
σv′
�
n
 n = 0.381(ISBT) + 0.05 �
σv′
pa
� − 0.15 (11) 
CN = (
pa
σv′
)n … . . Nfield = CN × N60 N60 = Nfield × �
Pa
σv′
�
0.5
× ER (12) 
where; 
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 werecomputed. 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 
calculation of the correlation of 𝑛 = 𝑞𝑐
𝑁
 for each 30cm, were used and the results presented in 
table (2). 
Table 2: Obtained results from arithmetic average method for all of available data 
Soil Type Number of n n value (Nfield) n value (N60) 
 Max Min Ave. Max Min Ave. 
Silty sand 77 0.602 0.205 0.374 0.673 0.121 0.332 
Clay 85 0.541 0.166 0.367 0.549 0.125 0.277 
Sandy silt 36 0.602 0.234 0.423 0.749 0.156 0.358 
Sand 36 0.614 0.229 0.529 0.647 0.228 0.533 
Gravelly sand to sand 18 0.983 0.398 0.572 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 = 𝑎𝑁𝑏) 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 = 𝑎𝑁𝑏) for filtered data. The numerical results of these correlations are 
presented in table (3). 
 
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SPT-N value
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36
C
on
e 
tip
 re
si
st
an
ce
 (q
c)
0
2
4
6
8
10
12
14
16
18
20
22
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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Vol. 19 [2014], Bund. E 1022 
 
SPT-N
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
qc
 (M
Pa
)
0
2
4
6
8
10
12
14
16
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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Vol. 19 [2014], Bund. E 1023 
 
SPT-N
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
qc
 (M
Pa
)
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Sand (All field data)
Linear (Nfield)
Power (Nfield)
Sand (All normalized data)
Linear (N60)
Power (N60)
SPT-N
0 2 4 6 8 10 12 14 16 18 20 22 24
qc
 (M
Pa
)
0
2
4
6
8
10
12
14
Sandy silt (All field data)
Linear (Nfield)
Power (Nfield)
Sandy silt 
(All normalized data)
Linear (N60)
Power (N60)
SPT-N
0 1 2 3 4 5 6 7
qc
 (M
Pa
)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Clay (All field data)
Clay (All Normalized data)
Linear (Nfield)
Power (Nfield)
Power (N60)
Linear (N60)
SPT-N
0 2 4 6 8 10 12 14 16 18 20 22
qc
 (M
Pa
)
0
1
2
3
4
5
6
7
8
9
10
11
12
Silty sand (All field data)
Linear (Nfield)
Power (Nfield)
Silty sand 
(All normalized data)
Linear (N60)
Power (N60)
 
SPT-N
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38
qc
 (M
Pa
)
0
2
4
6
8
10
12
14
16
18
20
22
Gravelly sand to sand (All field data)
Linear (Nfield)
Power (Nfield)
Gravelly sand to sand 
(All normalized data)
Linear (N60)
Power (N60)
 
Figure 6: Results for linear (𝑞𝑐 = 𝑎𝑁 + 𝑏) and power (𝑞𝑐 = 𝑎𝑁𝑏) correlations for all data 
 
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SPT-N
0 2 4 6 8 10 12 14 16 18 20 22 24 26
qc
 (M
Pa
)
0
2
4
6
8
10
12
14
16
18
20
22
24
Silty sand (Filtered)
Linear
Clay (Filtered)
Linear
Sandy silt (Filtered)
Linear
Sand (Filtered)
Linear
Gravelly sand to sand
Linear
 
Figure 7: Results of linear correlation (𝑞𝑐 = 𝑎𝑁) for filtered data 
Table 3: Numerical results for all available data 
 Soil type Correlation 
 qc=aN qc=aN+b qc=aNb 
Field data Silty sand 
 
0.442N 
(R2=0.83) 
0.521N-0.437 
(R2=0.85) 
0.346N1.031 
(R2=0.81) 
 Clay 
 
0.321N 
(R2=0.71) 
0.272N+0.165 
(R2=0.74) 
0.432N0.739 
(R2=0.81) 
 Sandy silt 
 
0.527N 
(R2=0.88) 
0.564N-0.377 
(R2=0.89) 
0.385N1.079 
(R2=0.85) 
 Sand 
 
0.568N 
(R2=0.88) 
0.605N-0.842 
(R2=0.89) 
0.336N1.158 
(R2=0.87) 
 Gravelly sand to sand 
 
0.613N 
(R2=0.84) 
0.617N - 0.098 
(R2=0.84) 
0.3975N1.13 
(R2=0.88) 
Normalized data Silty sand 
 
0.456N 
(R2=0.80) 
0.608N-0.914 
(R2=0.84) 
0.201N1.373 
(R2=0.85) 
 Clay 
 
0.280N 
(R2=0.70) 
0.287N-0.0245 
(R2=0.70) 
0.274N1.015 
(R2=0.71) 
 Sandy silt 
 
0.599N 
(R2=0.86) 
0.800N-1.375 
(R2=0.88) 
0.194N1.465 
(R2=0.87) 
 Sand 
 
0.577N 
(R2=0.86) 
0.61N-0.755 
(R2=0.87) 
0.271N1.235 
(R2=0.86) 
 Gravelly sand to sand 
 
0.648N 
(R2=0.85) 
0.626N + 0.509 
(R2=0.85) 
0.348N1.204 
(R2=0.87) 
Filtered data Silty sand 0.46N(R2=0.87) 
0.534N-0.476 
(R2=0.88) 
0.282N1.212 
(R2=0.89) 
 Clay 0.308N 
(R2=0.82) 
0.253N+0.183 
(R2=0.85) 
0.409N0.779 
(R2=0.85) 
 Sandy silt 0.528N 
(R2=0.88) 
0.563N-0.366 
(R2=0.89) 
0.397N1.066 
(R2=0.87) 
 Sand 0.568N 
(R2=0.88) 
0.605N-0.842 
(R2=0.89) 
0.336N1.158 
(R2=0.87) 
 Gravelly sand to sand 0.613N 
(R2=0.84) 
0.617N - 0.098 
(R2=0.84) 
0.3975N1.13 
(R2=0.88) 
 
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Vol. 19 [2014], Bund. E 1025 
 
SPT-N
0 1 2 3 4 5 6
qc
 (M
Pa
)
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
Clay (Filtered)
Linear 
Power
SPT-N
0 2 4 6 8 10 12 14 16 18 20 22
qc
 (M
Pa
)
0
1
2
3
4
5
6
7
8
9
10
11
12
Silty sand (Filtered)
Linear
Power
SPT-N
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
qc
 (M
Pa
)
0
2
4
6
8
10
12
14
16
18
20
22
Gravelly sand to sand (Filtered)
Linear
Power
SPT-N
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
qc
 (M
Pa
)
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Sand (Filtered)
Linear
Power
SPT-N
0 2 4 6 8 10 12 14 16 18 20 22 24
qc
 (M
Pa
)
0
2
4
6
8
10
12
14
Sandy silt (Filtered)
Linear
Power
 
Figure 8: Results of linear (𝑞𝑐 = 𝑎𝑁 + 𝑏) and power (𝑞𝑐 = 𝑎𝑁𝑏) 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 
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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 Proposed 
value 
This study Condition 
De Alencar Velloso (1959) Clay and silty clay 0.35 0.367 OK 
 Sandy clay and silty sand 0.2 0.374 More 
 Sandy silt 0.35 0.423 OK 
 Fine sand 0.6 0.529 OK 
Meigh and Nixon (1961) Gravelly sand 0.3-0.4 0.572 More 
Franki Piles (1960) Clayey sand 0.6 0.529 OK 
 Silty sand 0.5 0.374 Less 
 Clays 0.2 0.367 More 
 Silt, sandy silt and silt-sand mix. 0.2 0.423 More 
Schmertmann (1970) Fine to medium sand, silty sand 0.3-0.42 0.374 OK 
 Coarse sand, sand with gravel 0.5-0.6 0.572 OK 
Barata et al., (1978) Clayey silty sand 0.2-0.352 0.374 OK 
Chang (1988) Silt, sandy silt and silt-sand 0.22 0.423 More 
Danziger and De Valleso (1995) Fine to medium sand, silty sand 0.3-0.42 0.374 OK 
 Coarse sand, sand with gravel 0.5-0.6 0.572 OK 
 Sand 0.57 0.529 OK 
Danziger et al., (1998) Silty sand, Silty clay 0.5-0.642 0.374 Less 
 Clay, silt and sand mixtures 0.1-0.35 0.367 OK 
Acka (2003) Sand 0.77 0.529 Less 
 Silty sand 0.702 0.374 Less 
 Sandy silt 0.58 0.423 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 
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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) 
 
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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 
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Vol. 19 [2014], Bund. E 1029 
 
Schmertmann (1970) and Danziger and De Valleso (1995) and logical reasons of differences 
betweenour 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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