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 - 1013 - 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. 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). Vol. 19 [2014], Bund. E 1015 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) Vol. 19 [2014], Bund. E 1016 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 Vol. 19 [2014], Bund. E 1017 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 Vol. 19 [2014], Bund. E 1018 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 Vol. 19 [2014], Bund. E 1019 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 Vol. 19 [2014], Bund. E 1020 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). Vol. 19 [2014], Bund. E 1021 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 Vol. 19 [2014], Bund. E 1022 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 Vol. 19 [2014], Bund. E 14 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 Vol. 19 [2014], Bund. E 1024 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. 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