Draft · 2020. 11. 18. · Draft 4 77 on effective cone resistance and sleeve friction from CPT and...

Draft

Marine soil behaviour classification using CPTu and borehole records

Journal: Canadian Geotechnical Journal

Manuscript ID cgj-2019-0571.R2

Manuscript Type: Article

Date Submitted by the Author: 13-Apr-2020

Complete List of Authors: Yin, Kesheng; Hong Kong University of Science and Technology, Dept of Civil and Environmental EngineeringZhang, L.M.; Hong Kong University of Science and Technology, Wang, Haojie; Hong Kong University of Science and Technology, Dept of Civil and Environmental EngineeringZou, Haifeng; Hong Kong University of Science and Technology, Dept of Civil and Environmental EngineeringLi, Lisa Jinhui; Harbin Institute of Technology, Dept of Civil and Environmental Eng

Keyword: Cone penetration, soil behaviour, borehole, offshore geotechnics, soil classification

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Issue? :Not applicable (regular submission)

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Canadian Geotechnical Journal

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1 Marine soil behaviour classification using CPTu and borehole records

2

3 K.S. Yin 1, L. M. Zhang 2,*, H.J. Wang3, H.F. Zou4, and J.H. Li5

4

5 1PhD Candidate, Department of Civil and Environmental Engineering, The Hong Kong

6 University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong.

7 [email protected]

8 2Chair Professor, Department of Civil and Environmental Engineering, The Hong Kong


10 [email protected]

11 3PhD Candidate, Department of Civil and Environmental Engineering, The Hong Kong


13 [email protected]

14 4Post-doctoral Research Fellow, Department of Civil and Environmental Engineering, The

15 Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong

16 Kong. [email protected]

17 5Professor, Department of Civil and Environmental Engineering, Harbin Institute of

18 Technology (Shenzhen), Shenzhen, China. [email protected]

19

20 *Corresponding author

21

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23

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27 Abstract

28 Several CPTu-based soil behaviour classification systems (SBCs) have been developed for

29 standard sites, where clays, silt and sand dominate. However problems can occur when

30 applying the SBCs to offshore sites, where the marine soils may be decomposed from rocks or

31 mixed with artificial fills. This study evaluates the accuracy of six CPTu-based SBCs for

32 marine soils at a site offshore Hong Kong based on 16 CPTu soundings with 25,367 data points

33 by comparing with composition-based SBCs from borehole records in the vicinity of each

34 sounding. The soil types are determined from six commonly CPTu-based SBCs. The

35 interpretation of CPTu data is first performed to generate soil type variables comparable to

36 borehole data, followed by a cross-validation study. The soil classification performance of each

37 SBCs is quantified by the weighted kappa coefficient and the Kendall correlation coefficient

38 between the soil types generated by the CPTu-based and composition-based SBCs. The

39 classification accuracy for each soil type is also evaluated via the root mean squared error and

40 the mean absolute error. The classified soil types from the CPTu data are associated with a

41 median degree of consistency, indicating the need for combining CPTu-based and

42 composition-based SBCs for marine soil classification.

43

44 Keywords: Cone penetration, soil behaviour, soil classification, borehole, offshore

45 geotechnics.

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52 Introduction

53 Offshore site investigation is increasingly performed in Hong Kong for land reclamation

54 projects and coastal facilities, where the seabed is formed by very soft marine mud with

55 seashells or organic matters, marine deposits that are red-brown or yellow-brown and grey

56 clays, silt mixtures, clayey sands or sands, and decomposed soils from weathering of rocks in-

57 situ. The cone penetration test (CPT) is commonly applied for site investigation to understand

58 the soil strata for its affordability in terms of time and budget (Ching et al. 2015; Crisp et al.

59 2019; Knuuti and Länsivaara 2019). Modern electrical cones with piezometer elements,

60 referred to as CPTu, measure the cone tip resistance (qc), sleeve friction (fs) and shoulder pore

61 water pressure (u2) along sounding depth with close intervals ranging between 10 mm to 50

62 mm (Baligh et al. 1980; Tumay et al. 1981; Lunne et al. 1997; Cai et al. 2010). The soil

63 behaviour type can be determined, and the capacity and settlement of foundations can be

64 estimated based on results from such CPTu tests. Through extensive CPT soundings, the

65 uncertainties in design and construction can be minimized.

66 Various classification methods have been developed to predict the soil types from the

67 results of CPT or CPTu (Begemann 1965; Douglas and Olsen 1981; Senneset and Janbu 1985;

68 Robertson 1990, 2009; Jefferies and Davies 1991, 1993; Olsen and Mitchell 1995; Eslami and

69 Fellenius 1997; Jung et al. 2008; Schneider et al. 2008; Cetin and Ozan 2009; Li et al. 2016).

70 Douglas and Olsen (1981) developed a soil type classification chart based on piezocone tests,

71 which used liquidity index, earth pressure coefficient and sensitivity as soil type information.

72 Wroth (1984) introduced the normalized cone resistance and friction ratio, and Olsen (1994),

73 Olsen and Mitchell (1995), and Robertson and Wride (1998) performed cone resistance

74 normalization for different soil types. Robertson (1990) established a soil classification system

75 based on the normalized cone resistance and friction ratio and Jefferies and Davies (1991, 1993)

76 introduced a classification index. Eslami and Fellenius (1997) further developed a chart based

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77 on effective cone resistance and sleeve friction from CPT and CPTu data with boring and

78 sampling records from 18 sources in different countries.

79 Zhang and Tumay (1999) proposed a soil classification index and an in-situ state index to

80 investigate the accuracy of CPT-based soil classification and developed a fuzzy subset method.

81 Several statistical studies were performed for stratification identification with unsupervised

82 (Hegazy and Mayne 2002; Jung et al. 2008; Liao and Mayne 2007) and supervised approaches

83 (Wang et al. 2019). The uncertainties in soil stratification were modelled by utilizing Bayesian

84 frameworks (Wang et al. 2013; Cao et al. 2018).

85 Another soil classification system, composition-based classification, is broadly applied in

86 engineering practice, which classifies soils according to their relative components of sand, silt

87 and clay by soil properties in terms of soil morphology, observable attributes and laboratory

88 tests on borehole samples. Common composition-based engineering soil classification systems

89 include the Unified Soil Classification System (USCS), European Soil Classification System,

90 AASHTO Soil Classification System, etc. The engineering soil classification for the study area

91 in this paper follows Geo-guide 3 (GEO 1997), which is slightly modified from British Soil

92 Classification Systems (BSCS).

93 Although many CPTu-based and composition-based SBCs have been established, the

94 correlation between these SBCs is rarely investigated. The classification capability of these

95 SBCs and key factors influencing the classification accuracy of marine soils remain unclear.

96 Eslami and Fellenius (2004) pointed out that most soil behaviour classification charts are

97 locally developed based on limited types of soils and CPT soundings. The cone tip resistance

98 and sleeve friction are also influenced by various factors including equipment design, in-situ

99 stress and stratigraphy. Therefore, it is necessary to evaluate the accuracy and applicability of

100 existing soil behaviour charts when they are applied to specific marine regions and investigate

101 the impact of local soil compositions on the classification accuracy.

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102 The objective of this research is to evaluate the capability of six common soil behaviour

103 classification systems (i.e. Campanella et al. 1985; Robertson 1990; Jefferies and Davies 1993;

104 Eslami and Fellenius 1997; Robertson and Wride 1998; Robertson 2009) at a marine site

105 offshore Hong Kong. Data of 16 CPTu soundings and their adjacent borehole records are

106 utilised for this purpose and the accuracy of each method is indicated through consistency and

107 cross-correlation analysis. This study also intends to quantify the degrees of consistency and

108 similarity between the CPTu- and composition-based SBCs towards the observed soil types

109 along the depth. Therefore, a correlation analysis is performed to inspect the consistency of the

110 classified soil types, and to assess the extent to which specific marine soils, i.e., clays, silts,

111 sand and gravels, affect the profiling accuracy of the SBCs by evaluating the classification

112 errors for each soil type.

113

114 Study area and data

115 The CPTu data are acquired from the Hong Kong bay area, where superficial deposits

116 were formed within the Quaternary period in the last two million years (Langford 1994).

117 Marine clays are by far the most widely distributed type of marine sediment in Hong Kong's

118 coastal waters (Holt 1962). A typical geological vertical profile contains young marine

119 sediments at the top, older alluvial sediments (firm to stiff clay and residual soils) at the middle

120 and bedrock at the bottom (Maunsell 1991). Although Hong Kong marine clays are a mixture

121 of sand, silts and clays contributed by either land or marine sources, the impact induced by the

122 source of material on the engineering properties is insignificant as locally derived soils (Berry

123 1962; Lumb 1977). Lumb (1977) classified Hong Kong marine clays into 3 exposure groups

124 but the geographical distribution of the zones was not distinct. Yeung and So (2001) reported

125 that the Hong Kong marine clays are normally consolidated in general. The average

126 compression indices for alluvial clays and marine clays at 0.6 and 0.2, respectively, with the

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127 ratio of the secondary compression index to the compression index ranging between 0.005 and

128 0.15. The study area is overlapped with some contaminated muds, with the compression index

129 ranging between 0.4 and 0.84. The consolidation process was predicted to complete in

130 approximately 60 years since the completion of the contaminated mud disposal in 2000 (Yip

131 2001).

132 Two methods have been applied in offshore CPTu tests to push the cone penetrometer

133 into the seabed. One is to directly push from the seafloor until refusal or a predetermined

134 penetration, traditionally named as the seabed mode that gives high-quality results (Peucehn

135 2000) and was applied at the site in this study. A penetration of 40-50 m below the seabed can

136 be achieved using this method. The other is to drill a borehole and push the penetrometer into

137 the soil at bottom of the borehole, named as the down-hole mode or drilling mode (Lunne 2001),

138 which can achieve much deeper penetration depths and go through hard layers. The

139 terminology of seabed drilling methods is specified in ISO 19901-8 (ISO 2014).

140 The locations of CPTu soundings and the boreholes in this study are marked in Fig. 1,

141 from which three cross-sections are generated to investigate the characteristics of marine

142 deposits along with specific directions. Fig. 2 illustrates the soil profiles of the selected cross-

143 sections from borehole records. The soil strata of the study area are composed of top clay

144 sediments, bottom silt and sandy sediments, following the general geological condition

145 described by Maunsell (1991). The CPTu data in this study are from 16 CPTu soundings

146 offshore Hong Kong, containing 25,367 data points, with a 20 mm measurement interval. A

147 validation borehole is available for each CPTu sounding within a separation distance closer

148 than 5 m, chosen from 211 boreholes in the study area. Table 1 presents the information of

149 each CPTu sounding including the ground level, depth, and the separation distance between

150 the CPTu sounding and its corresponding borehole.

151

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152 Soil behaviour classification systems based on CPTu

153 The development of piezocone promotes the role of CPTu in characterizing subsoil and

154 soil layering boundaries. With the advent of different soil behaviour charts, the accuracy of soil

155 behaviour type classification is also improved (Eslami and Fellenius 2004; Ku et al. 2010; Cai

156 and Liu 2015). This study evaluates the classification accuracy of the SBCs proposed by

157 Campanella et al. (1985), Robertson (1990), Jefferies and Davies (1993), Eslami and Fellenius

158 (1997), Robertson and Wride (1998) and Robertson (2009) when applied to marine soils in

159 Hong Kong. Most SBCs originated from Campanella et al. (1985) are included, because these

160 SBCs and their derivative versions (e.g. Robertson 1990) have been applied in engineering

161 practices in the study area. These derivative versions are under the similar classification logical

162 framework, but differ in the number of soil zones, boundaries of soil zones, varieties of input

163 classifying data and normalization methodology.

164 Campanella et al. (1985) were the first to establish a soil behaviour chart based on the

165 corrected cone resistance and the friction ratio:

166 [1] 2 (1- )t cq q u

167 [2] 100%sf

t

fRq

168 where qt is the corrected cone resistance; qc is the measured cone resistance; u2 is the measured

169 pore pressure at the cone shoulder, α is the net area ratio, which is set at 0.59 in this study from

170 CPTu records; Rf is the friction ratio; fs is the sleeve friction. The soil behaviour types are

171 separated into 12 zones shown in Fig. 3 with the soil zone descriptions summarized in Table 2.

172 Robertson (1990) revised the SBCs of Campanella et al. (1985) with the normalized cone

173 resistance, Qt, and the normalized friction ratio, Fr:

174 [3] 0

0'

-t vt

v

qQ

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175 [4] 100%sr

t vo

fFq

176 where σv0 and σ'v0 is the total overburden pressure and effective overburden pressure,

177 respectively. When calculating the overburden pressure and effective overburden pressure, the

178 saturated unit weight of soil is assumed as 18 kN/m3 referring to local site investigation records.

179 The water level is at the sea level since the CPTu is performed in a marine area. Figure 3 also

180 presents the SBCs of Robertson (1990), which identifies 9 soil type zones shown in Table 2.

181 The sensitive fine-grained, clay and clay mixture are interpreted as zones 1 to 3 according

182 to the SBCs of Campanella et al. (1985) and Robertson (1990). For the other soil types, the

183 reduced soil zone number provides a more straightforward comparison between the normalized

184 and un-normalized soil behaviour type (Robertson 2010). Robertson (2010) stated that these

185 two SBCs tend to perform consistently when the in-situ vertical effective stress is between 50

186 kPa to 150 kPa.

187 Jefferies and Davies (1993) modified the soil classification system of Robertson (1990)

188 based on the revised grouping unification of CPTu data proposed by Houlsby (1988) and Been

189 et al. (1989) by applying the soil type index as a classification indicator rather than the direct

190 data locations related to the soil zones’ boundaries on the SBC chart. The soil type index, Ic, is

191 expressed as,

192 [5] 2 2

3- log 1- 1.5 1.3 logc t q rI Q B F

193 [6] 2 0

0

--q

t v

u uBq

194 where Bq is the normalised pore pressure parameter ratio and u0 is the equilibrium pore water

195 pressure. The soil type index is applied as an indicator of soil types, as summarized in Table 3.

196 Compared with the soil zones determined following Robertson (1990), some zones are

197 neglected as these zones can be regarded as artificial distinction (Jefferies and Davies 1993).

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198 Eslami and Fellenius (1997) argued that the application of Fr and Qt might distort data

199 because the variable (qt – σv0) is plotted versus its inverse value, and thus they proposed a new

200 SBC chart based on the direct use of sleeve frictional resistance and “effective” cone resistance,

201 qE:

202 [7] E t 2q = q - u

203 This profiling method does not estimate the effective stress and total stress due to the generation

204 of qE. The soils are classified into five categories, shown in Fig. 3 and Table 2.

205 Robertson and Wride (1998) adjusted the SBCs of Jefferies and Davies (1993) to avoid

206 the use of pore water pressure in unsaturated or dilative soils in which loss of saturation may

207 lead to imprecise Bq values. The modification focuses on the normalization of the cone

208 resistance and the determination of the soil type index. The modified normalized cone

209 resistance and soil type index are expressed as:

210 [8] 2 23.47 log 1.22c tn rI Q F

211 [9] 0

0

n

t v atn

a v

q PQP

212 where Pa is the atomistic pressure; Qtn is the normalized cone resistance with stress exponent

213 n. The process of soil type normalization is iterative with updated Ic, n, and Qtn. Table 3

214 summarizes the correlation between the range of final estimated Ic and the soil types.

215 Robertson (2009) modified the iterative estimation algorithm for soil classification based

216 on Robertson and Wride (1998). The normalized cone resistance and soil type index are still

217 generated by Eqs. (8) and (9), while the stress exponent is iteratively determined using Eq. (10)

218 rather than set as fixed values at 0.5, 0.75 and 1 by Robertson and Wride (1998):

219 [10] 00.381 0.05 0.15vC

a

n IP

220 where n is initially set as 1 for iteration and required to be smaller than 1. An iterative analysis

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221 is performed until a convergence criterion, ∆n ≤ 0.01, is achieved.

222

223 Evaluation of soil behaviour classification systems

224 To evaluate the performance of the SBCs, first, the observed results from borehole records

225 were digitalized and rescaled to the same soil type ordinal variable as the classified soil types

226 from CPTu data for further analysis. Then the two sets of soil types for each SBCs are evaluated

227 in terms of consistency degree and correlation. The consistency degree represents the interrater

228 agreement for categorical ratings. The analysis of observer or interrater agreement often

229 provides a useful means of assessing the reliability of a rating system (Banerjee et al. 1999). In

230 this study, the soil type index is regarded as an ordinal categorical rating, and the rating system

231 is composed of the SBCs and observed borehole records. A weighted kappa coefficient is

232 calculated as an index to evaluate the consistency degree, a higher kappa coefficient indicating

233 a higher degree of consistency. Correlation analysis is performed by generating the Kendall

234 correlation coefficient between the observed soil types from borehole records and the soil b

235 types determined by the SBCs. b determines the monotonic relationship between the two sets

236 of soil type variables and describes the soil type variation trend along with depth. To understand

237 the impact of soil compositions on the classified accuracy, the consistency and correlation

238 analyses are also performed for three cross-sections of the study area, followed by a

239 classification error analysis for each soil type.

240

241 Data rescaling

242 Before the statistical analysis, the occasional extreme spikes in CPTu raw data, possibly

243 caused by electrical noises or a depth triggering system, were discarded to ensure the

244 representativeness of CPTu measurements. After the generation of the parameters following

245 the steps and equations for each SBCs, the normalized values are plotted as input to the

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246 behaviour charts to estimate the specific soil type of each data point for the whole study area,

247 followed by the digitalization of the borehole records.

248 All borehole records are digitalized to the soil type indices based on the obtained principal

249 components, which will be further analyzed with the interpreted soil types from CPTu-based

250 SBCs. The data densities from borehole records and CPTu are not equal, since the soil types

251 from each CPTu sounding have an average of approximately 1,500 data points at 20 mm

252 interval, while the soil types from borehole records have irregular sampling intervals from a

253 few centimetres to more than 6 m. It is necessary to modify the two datasets of soil types to a

254 comparable level before performing any statistical evaluation. Hence the soil types from the

255 borehole records are rescaled to the same point-based soil type ordinal variable at 20 mm

256 interval within each sampling range. Fig. 4 shows rescaled datasets of the generated soil types

257 from CPTu data and borehole records for an illustrative sounding.

258

259 Consistency of classified soil types

260 The agreement degree of the two data sets of soil types, determined by CPTu and borehole

261 records, is evaluated through the weighted kappa coefficient w, which is an indicator for the

262 consistency check of classified ordinal variables, proposed by Cohen (1968):

263 [11] 1 1

1 1

1

k k

ij oiji j

w k k

ij ciji j

w p

w p

K

264 [12] cij i j p p p

265 where k is the total number of cells in the computing matrix; wij is the disagreement weight;

266 poij is the observed cell proportion of agreement, and pcij is the chance expected cell proportion

267 of agreement, which is the product of the proportions for the row, pi, and column, pj. An

268 illustrative matrix for computing the weighted kappa coefficient of the SBCs by Eslami and

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269 Fellenius (1997) is shown in Table 4. The proportion of the agreement between the soil type

270 determined by borehole records and CPTu is indicated by cross-frequency scatterplots, shown

271 in Fig. 5, which can be applied to compute pi and pj for each SBCs.

272 The application of the weighted kappa coefficient to check the degree of agreement

273 between the soil types generated by CPTu and borehole records is under the premise of two

274 conditions. First, the pre-processed data needs to be independent categorical variables with

275 consistent analysis objects, e.g., soil type for every data point normalized and rescaled to the

276 same level in this study. Second, every observing object is required to be classified into the

277 same index, and the classified models are independent, e.g., the results of this study are

278 modified to the soil zone numbers of each SBC chart for CPTu and boreholes. These

279 assumptions are satisfied. The results of the calculated weighted kappa value and the

280 corresponding 95% confidence interval are summarized in Table 5. The weighted kappa values

281 for most of all the applied SBCs in this study can reach 0.4, which is a median degree of

282 consistency in dealing with the soil classification from CPTu in the marine area. Table 5 also

283 shows that the SBCs developed by Robertson and Wride (1998) is the best for evaluating the

284 consistency degree of marine soils in Hong Kong, with a κw value of 0.489, followed by those

285 by Robertson (2009) and Robertson (1990) closely, with κw values of 0.487 and 0.471

286 respectively. Landis and Koch (1977) suggested criteria for measuring the strength of

287 agreement using the kappa coefficient: less than 0 (poor), 0.01–0.2 (slight), 0.21–0.4 (fair),

288 0.41–0.6 (moderate), 0.61–0.8 (substantial), and 0.81–1 (almost perfect). The CPTu based

289 SBCs have a moderate strength of agreement.

290

291 Variation trend of classified soil types

292 The weighted kappa coefficient expresses the degree of consistency but does not evaluate the

293 capability of the SBCs to represent the variation trend towards the observed soil types along

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294 the depth. Therefore, a correlation analysis is performed to inspect the degree of trend similarity

295 of the classified soil types. Since the soil types in this study are rescaled to integer indices for

296 both CPTu and borehole records, regarded as ordinal categorical variables, the Mantel-

297 Haenszel Chi-square test, also called ‘test for linear trend’, was conducted to verify the

298 presence of a linear association between the two sets of ordinal categorical data (Norman et al.

299 2008). The results are summarized in Table 6. For the six SBCs, the Chi-squared values all

300 reach around 10,000, with the p-value (the probability that a linear correlation does not exist)

301 less than 0.001, indicating a linear correlation between the observed soil types from borehole

302 records and the simulated soil type indices from the CPTu based SBCs.

303 The strength of this ordinal association is determined by the Kendall correlation b

304 coefficient, measuring the similarity of the orderings of the data when ranked by each of the

305 quantities (Kendall 1938). Since the soil types determined by CPTu and borehole records are

306 rescaled to integers, regarded as noncontinuous ordinal variables, b describes the similarity

307 degree of the variation trend of the soil types for the two data sets along with depth. A higher

308 correlation coefficient indicates the SBC system that better reflects the observed variation trend

309 of soil types from the borehole records. b is calculated by,

310 [13]

1 1

( 1) ( 1)1 12 2

c db s t

i i i ii i

m mm m m mu u v v

311 where m is the number of total data pairs (two sets data of soil types along depth); mc is the

312 number of concordant pairs; md is the number of discordant pairs; s is the number of groups of

313 ties for the first quantity (soil types from borehole records); ui is the number of tied values in

314 the i-th group of ties for the first quantity; t is the number of group of ties for the second quantity

315 (soil types determined by SBCs of CPTu); vi is the number of tied values in the i-th group of

316 ties for the second quantity. The results of the generated Kendall correlation coefficients b

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317 for each SBCs are summarized in Table 7. The generated p-values (the probability that the

318 generated value has no difference with zero) are all less than 0.001, showing the calculated

319 Kendall correlation coefficient has statistical significance for all SBCs. b

320 Guidelines for interpreting correlation coefficients were proposed by Schober et al. (2018)

321 and others. A coefficient of <0.1 indicates a negligible relationship and >0.9 a very b b

322 strong relationship; values in between are disputable and depend on the context and purposes.

323 The generated correlation coefficient in this study serves as an inter-relation index between the

324 SBCs and observed borehole records. According to the Kendall correlation coefficient for b

325 each SBCs in Table 7, the Eslami and Fellenius (1997) and Campanella (1985) systems have

326 the highest values, = 0.667 and 0.664 respectively, indicating their good capability in b

327 estimating the actual variation trend towards soil types along with depth in the marine area.

328 The average for the SBCs is around 0.6, indicating a medium level of correlation.b

329

330 Specific soil type classification error

331 The consistency and correlation analysis are also performed for each CPTu sounding with

332 the paired borehole records of the three cross-sections in Fig. 6, reflecting the trend of

333 fluctuation among different soundings between the Kendall correlation and the weighted b

334 kappa coefficient. The difference in the consistency degree and the correlation strength of the

335 generated soil types between each CPTu sounding and its paired borehole is apparent, but the

336 peak values mainly appear at the same CPTu soundings for both indices. These phenomena

337 can be possibly introduced by the varied natural soil compositions which affect the consistency

338 degree and correlation strength of the classified soil behaviour types to a certain extent, leading

339 to varying classification accuracies for different types of soil.

340 To estimate the classification accuracy of the SBCs on every observed soil type of all

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341 soundings, the mean absolute error (MAE) and root mean squared error (RMSE) between the

342 generated soil types from CPTu and borehole records are calculated:

343 [14] 1i

N

ii

x yMAE

N

344 [15] 2

1i

N

ii

x yRMSE

N

345 where N is the total number of data pairs of each soil type; xi is the generated soil types from

346 the CPTu records; yi is the rescaled soil types from the borehole records. The results are plotted

347 in Fig. 7 for each SBCs.

348 All SBCs generate high errors when classifying the gravels in the study area, which are

349 almost twice that for the second-highest soil type. One cause for the large errors is the limited

350 amount of sampling data for gravels and rocks, which exist only at the bottom of some CPTu

351 soundings. For the SBCs of Robertson (2009), Robertson and Wride (1998), Jefferies and

352 Davies (1993) and Robertson (1990), the lowest and second-lowest errors appear for soil type

353 indies 6 and 3, indicating these SBCs have the best performance in classifying sands (clean

354 sand to silty sand) and clays (clay to silty clay) from the CPTu records. The accuracy of

355 classifying silt mixtures (clayey silt to silty clay) is ahead of that for sand mixtures (silty sand

356 to sandy silt). It can be inferred that the mixtures are associated with larger classification errors

357 than single component soils like sand and clay when applying the SBCs. Compared with sands

358 and clays, the fuzzy description of the mixtures of silt and sand from borehole records leads to

359 lower classification accuracy. The SBCs’ description towards mixture deposits is fuzzy when

360 referring to detailed borehole records, e.g. ‘organic slightly sandy SILTY CLAY with little

361 gravel and shells’ is a typical description in borehole records. The compositions are

362 complicated but a soil type of ‘silty clay’ is chosen referring to its principal components; the

363 other components besides the principal components may lead to another soil type interval. This

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364 type of classification error can be avoided for simple composite soil types such as clay and

365 sand. Besides, a narrow classification area for each type of soil on the SBC chart and dense

366 classification intervals can lead to larger classification errors.

367

368 Conclusions

369 The capability of six CPTu based soil behaviour type classification systems when

370 applying to marine soils offshore Hong Kong was evaluated, providing various performance

371 indicators regarding consistency and correlation. Several conclusions can be drawn:

372 1. The composition of the marine soil affects the classification accuracy of the soil profiling

373 methods based on CPTu. When the soil stratum is mainly composed of gravels, higher

374 classification errors than other types of soils can result. Compared to single component

375 deposits such as sands and clays, mixtures of sediments are also associated with lower

376 classification accuracy.

377 2. When the practical purpose is to identify the variation trend of soil types with depth, the

378 SBCs developed by Eslami and Fellenius (1997) and Campanella et al. (1985) are

379 recommended. When the focus is on the agreement between the classified soil types from

380 CPTu data and borehole records, the SBCs developed by Robertson (2009) and Robertson

381 and Wride (1998) are better options.

382 3. The classified soil types from CPTu data in the marine area are associated with a median

383 degree of consistency based on the calculated weighted kappa coefficient and correlation

384 coefficient, averaging at around 0.4 and 0.6 respectively for the six CPTu based SBCs.

385 Hence the current SBCs still need to be supplemented by local geological conditions and

386 composition-based SBCs from borehole records, rather than used as sole sources for soil

387 profiling.

388

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389 References

390 Baligh, M.M., Vivatrat, V., and Ladd, C.C. 1980. Cone penetration in soil profiling. Journal of

391 Geotechnical Engineering, 112(7): 727–745.

392 Banerjee, M., Capozzoli, M., McSweeney, L., and Sinha, D. 1999. Beyond kappa: A review of

393 interrater agreement measures. Canadian Journal of Statistics, 27(1): 3-23.

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514

515 List of table captions

516 Table 1. Details of boreholes and CPTu soundings.

517 Table 2. Soil behaviour types in classification systems.

518 Table 3. Soil behaviour types from the classification index.

519 Table 4. Illustrative matrix of proportions for computing weighted kappa coefficient of SBCs 520 per Eslami and Fellenius (1997).

521 Table 5. Estimated weighted kappa coefficient.

522 Table 6. Results of Mantel-Haenszel Chi-squared tests.

523 Table 7. Estimated Kendall correlation coefficient.b

524

525 List of figure captions

526 Fig. 1. Locations of boreholes and CPTu soundings.

527 Fig. 2. Soil profiles from borehole records: (a) cross section 1; (b) cross section 2; (c) cross 528 section 3.

529 Fig. 3. Profiling charts with localized CPTu data points per (a) Campanella et al. (1985); (b) 530 Robertson (1990); (c) Eslami and Fellenius (1997).

531 Fig. 4. Example of comparison of rescaled soil types from borehole and CPTu using six soil 532 behaviour classification systems: (a) Robertson (2009); (b) Robertson and Wride (1998); (c) 533 Eslami and Fellenius (1997); (d) Jefferies and Davies (1993); (e) Robertson (1990); (f) 534 Campanella et al. (1985).

535 Fig. 5. Scatterplots of soil types determined by borehole and CPTu using different soil 536 behaviour classification systems: (a) Robertson (2009); (b) Robertson and Wride (1998); (c) 537 Eslami and Fellenius (1997); (d) Jefferies and Davies (1993); (e) Robertson (1990); (f) 538 Campanella et al. (1985).

539 Fig. 6. Kendall correlation coefficients for (a) cross section 1; (b) cross section 2; (c) cross b540 section 3, and weighted kappa coefficients for (d) cross section 1; (e) cross section 2; (f) cross 541 section 3. Refer to Fig. 1 for the CPTu sounding locations.

542 Fig. 7. Root mean squared error and mean absolute error of each type of soil between CPTu 543 and borehole records using different soil behaviour classification systems: (a) Robertson 544 (2009); (b) Robertson and Wride (1998); (c) Eslami and Fellenius (1997); (d) Jefferies and 545 Davies (1993); (e) Robertson (1990); (f) Campanella et al. (1985). RMSE = Root mean squared 546 error; MAE = Mean absolute error.

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Table 1. Details of boreholes and CPTu soundings.

Labeled No.

Depth of borehole (m)

Ground level of borehole (m)

Length of CPTu sounding (m)

Ground level of CPTu (m)

Separation distance (m)

1 86.45 -6.85 40.75 -6.40 0.12 2 63.10 -6.85 39.75 -6.55 1.33 3 68.03 -6.90 35.18 -6.75 1.35 4 78.15 -7.10 28.63 -6.96 1.63 5 37.53 -6.45 33.35 -5.62 1.96 6 72.84 -7.00 31.68 -6.49 2.15 7 90.60 -6.35 38.01 -5.99 2.38 8 90.23 -5.20 27.84 -5.41 2.82 9 51.80 -6.55 43.74 -6.16 3.17 10 46.35 -6.85 45.56 -5.83 3.28 11 25.55 -5.85 39.77 -4.88 3.47 12 46.60 -6.81 36.73 -6.27 4.14 13 42.80 -6.05 42.27 -6.03 4.30 14 40.70 -6.25 28.93 -5.70 4.39 15 58.00 -6.55 36.06 -5.97 4.75 16 60.42 -5.65 39.65 -5.09 4.97

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Table 2. Soil behaviour types in classification systems.Soil behaviour classification method

Soil zone No. Soil types

1 Sensitive fine-grained soil2 Organic soil3 Clay4 Silty clay to clay5 Clayey silt to silty clay6 Sandy silt to clayey silt7 Silty sand to sandy silt8 Sand to silty sand9 Sand10 Gravelly sand to sand11 Very stiff fine-grained soil

Campanella et al. (1985)

12 Over-consolidated or cemented sand to clayey sand

1 Sensitive fine-grained soil2 Organic soils and peat3 Silty clay to clay4 Clayey silt to silty clay5 Silty sand to sandy silt6 Sand to silty sand7 Gravelly sand to sand8 Very stiff fine-grained soil

Robertson (1990)

9 Very stiff, fine-grained, over-consolidated or cemented soil

1 Collapsive soil to sensitive soil2 Soft clay to soft silt3 Silty clay to stiff clay4 Silty sand to sandy silt

Eslami and Fellenius (1997)

5 Sand to gravelly sand

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Table 3. Soil behaviour types from the classification index.Soil type index (IC) per Jefferies and Davies (1993)

Soil type index (IC) per Robertson and Wride (1998)

Soil zone number Soil types

IC<1.25 IC<1.31 7 Gravelly sands1.25<IC<1.9 1.31<IC<2.05 6 Clean sand to silty sand (Sands)

1.9<IC<2.54 2.05<IC<2.60 5 Silty sand to sandy silt (Sand mixtures)

2.54<IC<2.82 2.60<IC<2.95 4 Clayey silt to silty clay (Silt mixtures)

2.82<IC<3.22 2.95<IC<3.6 3 Silty clay to clay (Clays)IC>3.22 IC>3.6 2 Organic soils and peats

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Table 4. Illustrative matrix of proportions for computing weighted kappa coefficient of SBCs per Eslami and Fellenius (1997).Soil type index determined by CPTu

i, j 1 2 3 4 5 pi

1a 0 1 2 32 0.171b 0.299c 0.170 0.255 0.026 0 0.058 0 0.129 0 0.554

2 1 0 1 23 0.059 0 0.058 0 0.009 0.032 0.020 0.067 0.044 0.090 0.189

3 2 1 0 14 0.074 0.005 0.074 0.049 0.011 0.015 0.025 0.036 0.056 0.136 0.240

4 3 2 1 05 0.005 0.006 0.005 0.002 0.001 0 0.002 0.001 0.004 0.007 0.017

Soil type index determined from borehole

pj 0.309 0.306 0.046 0.105 0.233 1Notes:a. Disagreement weight, wij;b. Chance expected cell proportion of agreement, pcij= pi* pj;c. Observed cell proportion of agreement, poij.

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Table 5. Estimated weighted kappa coefficient.Soil behavior classification systems

Weighted kappa coefficient

P value Lower 95% asymptotic CI bound

Upper 95% asymptotic CI bound

Robertson (2009) 0.487 < 0.001 0.48 0.495Robertson and Wride (1998) 0.489 < 0.001 0.482 0.497Eslami and Fellenius (1997) 0.423 < 0.001 0.419 0.428Jefferies and Davies (1993) 0.376 < 0.001 0.369 0.383Robertson (1990) 0.471 < 0.001 0.463 0.479Campanella (1985) 0.421 < 0.001 0.415 0.426

Table 6. Results of Mantel-Haenszel Chi-squared tests.

Soil behavior classification systems

Chi-squared value of linear by linear

association

Degree of freedom P value

Robertson (2009) 11423.68 1 < 0.001Robertson and Wride (1998) 11544.149 1 < 0.001Eslami and Fellenius (1997) 13402.583 1 < 0.001Jefferies and Davies (1993) 9553.978 1 <0.001Robertson (1990) 10943.843 1 <0.001Campanella (1985) 10755.392 1 <0.001

Table 7. Estimated Kendall b correlation coefficient.Soil behavior classification system Kendall b P valueRobertson (2009) 0.617 <0.001Robertson and Wride (1998) 0.619 <0.001Eslami and Fellenius (1997) 0.667 <0.001Jefferies and Davies (1993) 0.566 <0.001Robertson (1990) 0.603 <0.001Campanella (1985) 0.664 <0.001

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Fig. 1. Locations of boreholes and CPTu soundings.

0 1000 2000 3000 4000 5000 6000 70000

500

1000

1500

2000

2500

3000

3500

4000

1

7

1510 13

63

5

11

8

41614

12

2

Borehole locationCPTu location Cross section 1 for soil profiling Cross section 2 for soil profiling Cross section 3 for soil profiling

Dis

tanc

e (m

)

Distance (m)

9

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Fig. 2. SSoil profiles fromm borehole reco

ords: (a) cross seection 1; (b) crooss section 2; (cc) cross section 3.

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Fig. 3. Profiling charts with localized CPTu data points per (a) Campanella et al. (1985); (b) Robertson (1990); (c) Eslami and Fellenius (1997).

Effe

ctiv

e co

ne re

sist

ance

, qE

(MPa

)

12

3

4

5

Nor

mliz

ed c

one

resi

stan

ce, Q

t

12

3

4

5

6

78

9

12

34

5

6

7

8

9

1012

11

100

10

1

0.1Cor

rect

ed c

one

resi

stan

ce, q

t (M

Pa)

1000

100

10

1Nor

mal

ized

con

e re

sist

ance

, Qt

Friction ratio, Rf (%) Normalized friction ratio, Fr (%) Sleeve friction, fs (kPa)

100

10

1

0.1Effe

ctiv

e co

ne re

sist

ance

, q(M

Pa)

E

(a) (b) (c)

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Fig. 4. Example of comparison of rescaled soil types from borehole and CPTu using six soil behaviour classification systems: (a) Robertson (2009); (b) Robertson and Wride (1998); (c) Eslami and Fellenius (1997); (d) Jefferies and Davies (1993); (e) Robertson (1990); (f) Campanella

et al. (1985).

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FRFig. 5. ScatterplRobertson and W

lots of soil typeWride (1998); (

es determined by(c) Eslami and F

y borehole and Fellenius (1997)

CPTu using dif); (d) Jefferies a

fferent soil behaand Davies (199

aviour classifica93); (e) Roberts

ation systems: (ason (1990); (f) C

a) Robertson (2Campanella et a

2009); (b) al. (1985).

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Fig. 6. Kendall b correlation

cross sect

n coefficients fo

tion 1; (e) cross

or (a) cross secti

s section 2; (f) c

ion 1; (b) cross

cross section 3.

section 2; (c) cr

Refer to Fig. 1

ross section 3, a

for the CPTu so

and weighted ka

ounding location

appa coefficient

ns.

ts for (d)

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Fig. 7. Root mean squared error and mean absolute error of each type of soil between CPTu and borehole records using different soil behaviour classification systems: (a) Robertson (2009); (b) Robertson and Wride (1998); (c) Eslami and Fellenius (1997); (d) Jefferies and Davies (1993);

(e) Robertson (1990); (f) Campanella et al. (1985). RMSE=Root mean squared error; MAE=Mean absolute error.

2(Soft clay to soft silt)

3(Silty clay to stiff clay)

4(Silty sand to sandy silt)


0 1 2 3 4

Soil

type

inde

x

RMSE or MAE

MAE RMSE

3(Clays)

4(Silt mixtures)

5(Sand mixtures)

6(Sands)

7(Gravelly sands)

0 1 2 3 4 5 6

RMSE or MAE

Soil

type

inde

x

MAE RMSE

3(Clays)

4(Silt mixtures)

5(Sand mixtures)

6(Sands)

7(Gravelly sands)

0 1 2 3 4 5

RMSE or MAE

Soil

type

inde

x MAE RMSE

(a)

(b)

(d)

(e)

(f)(c)

3(Clays)

4(Silt mixtures)

5(Sand mixtures)

6(Sands)

7(Gravelly sands)

0 1 2 3 4 5 6

Soi

l typ

e in

dex

RMSE or MAE

MAE RMSE

3(Clays)

4(Silt mixtures)

5(Sand mixtures)

6(Sands)

7(Gravelly sands)

0 1 2 3 4 5 6

RMSE or MAE

Soil

type

inde

x

MAE RMSE

4(Silty clay to clay)

5(Clayey silt to silty clay)

6(Sandy silt to clayey silt)


8(Sand to silty sand)

9(Sand)

10(Gravelly sand to sand)

0 1 2 3 4 5 6 7 8 9

RMSE or MAE

Soil

type

inde

x

MAE RMSE

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