research article predictive shear strength models...

Hindawi Publishing CorporationJournal of EngineeringVolume 2013 Article ID 595626 8 pageshttpdxdoiorg1011552013595626

Research ArticlePredictive Shear Strength Models for Tropical Lateritic Soils

Oluwapelumi O Ojuri

Department of Civil Engineering Federal University of Technology Akure PMB 704 Ondo State Akure Nigeria

Correspondence should be addressed to Oluwapelumi O Ojuri ojuripyahoocom

Received 20 October 2012 Revised 24 January 2013 Accepted 24 January 2013

Academic Editor Guangming Xie

Copyright copy 2013 Oluwapelumi O Ojuri This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

This research focused on the indirect determination of soil shear strength using basic soil properties for a lateritic soil area Sampleswere collected from six different locations in the Federal University of Technology Akure Ondo State Nigeria These lateritic soilsfall within the A-2 A-6 and A-7 groups in the AASHTO classification system Exploratory analysis of the six independent variableswas performed using the statistical analysis of principal componentsThe stepwisemultiple regression was used to establish the bestpredictive models for the California bearing ratio (CBR) and undrained shear strength (119878

119906) A remarkable significant model for

undrained shear strength [119878119906= minus547713 + 0381MDD minus 9104GI] with (119865

23= 229476 119875 lt 0005 Adjusted 119877

2= 0989 Std Error

= 75638) emerged This model accounts for 994 of variation in the dependent variable For the included predictor variables beta= 0722 119875 lt 0005 and beta = minus0417 119875 lt 0005 for MDD and GI respectively where other variables are excluded from the modelThis makes a quick evaluation of the shear strength of lateritic soils by using their maximum dry density and group index valuespossible

1 Introduction

The springing up of various building construction projectsin many growing urban centres in Nigeria and within theuniversity campus has necessitated a critical examination ofthe sitesoil investigation methods for the purpose of safetyand economic sustainability The attempt of an indirect soilstrength test method is borne out of a concern for the realityof scarce functional detailed geotechnical testing facilitiesin most of our technicalengineering institutions in NigeriaProcedures that allow the prediction of the shear strength oftropical soils or the minimization of the number of tests orcosts needed to measure them are advantageous because ofthe limited availability of state-of-the art facilities for testingand oftentimes lack of the required degree of expertise indeveloping countries A simplified procedure is proposed inthis paper to estimate the shear strength of a tropical soil

Lateritic soils can be described broadly as all productsof tropical weathering with red reddish brown or darkbrown colour with or without nodules or concretions andthat generally (but not exclusively) found below hardenedferruginous crusts or hard pan [1] Numerous studies have

been made on the engineering properties and behaviourof laterite soils [2ndash6] Several previous studies on lateritesoils by Gidigasu [7] for Ghanaian soils Malomo [8] forBrazilian soils and Madu [9] Ola [10ndash12] and Ogunsanwo[13 14] Malomo [15] Mesida [16] for Nigerian soils havedemonstrated the susceptibility of laterite soils to degradationunder load Their soil grains which are derived from thecementation of smaller ones break down to smaller sizeson application of load Fall et al [17] tested laterites derivedfrom different rock-parents and examined the stress-strainbehaviour of the compacted laterite soils They observed thatthe moulding moisture content and the confining pressurehave little influence on shear strength

Shear strength forms an important engineering propertyin the design of numerous geotechnical and geoenvironmen-tal structures such as earth dams retaining walls pavementsliners and covers Several procedures have been proposed inthe literature to predict the shear strength of an unsaturatedsoil These procedures use the soil-water characteristic curveas a tool either directly or indirectly along with the saturatedshear strength parameters cohesion (1198881015840) and angle of internalfriction (1206011015840) to predict the shear strength function for

2 Journal of Engineering

Table 1 Summary of geotechnical test results for the six locations

Sample Depth(m) 119882

119871() 119882

119901() Plasticity index

(PI)

Natural moisturecontent()

MDD(kgm3)

OMC()

UCS(kPa)

119878119906

(kPa)CBR value

()

A 15 32 24 8 25 1652 206 71 355 35B 15 36 241 119 24 1742 178 106 53 45C 15 48 239 241 21 1826 147 122 61 50D 15 32 19 13 18 1970 116 412 206 77E 15 34 20 14 16 2015 101 375 1875 87F 15 38 21 17 20 1796 168 216 108 48

an unsaturated soil [18ndash21] Artificial neural networks andmultivariate regression have also been applied to estimate theshear strength of soils [22ndash24]

According to Murthy [25] soil survey ranges from visualinspection of trail pits to an extensive borehole investigationwith deep and numerous boreholes and extensive samplingand testing of the soil usually by investigation specialistSoil investigation is a necessary process which should becarried out prior to commencement of actual constructionwork Tests to determine the shear strength or density ofsoils in situ are a valuable means of investigation sincethese characteristics can be obtained directly without thedisturbing effect of boring and sampling [15 17]

2 Research Methodology

The method used in the sample collection is the trial pitmethod Undisturbed block samples were collected from six(6) different trial pits each in different sites in FUTA Thesesites and their locations are listed below

(i) proposed site for school of mines and earth sciencewhich is sample A

(ii) new library complex which is sample B(iii) centre for continuing education (CCE) which is sam-

ple C(iv) engineering workshop complex which is sample D(v) malu road that is behind Abiola Hall sample E(vi) proposed site for hostel accommodation which is

sample F

21 Test or Trial Pits This is the method of site explorationthat was used in this research work A test pit is simply ahole dug in the ground that is large enough for a ladderto be inserted thus permitting a close examination of thesides With this method relatively undisturbed samples ofsoils were collected

The depth of each trial pit was 15m (5 ft) and about 4 ft times4 ft wide that is 12m times 12m times 15m pitThe types of samplescollected for the laboratory analysis are as follows

(i) Disturbed samples(ii) Undisturbed samples

The pit was sunk by hand excavationwith the aid of spade anddigger

22 Undisturbed Sample Undisturbed samples are requiredfor shear strength tests on the soil Undisturbed block sampleswere cut by hand from the bottom of the pits During cuttingthe samples are protected from water wind and sun to avoidany change in water content The samples are covered withblack polythene bag immediately they are brought to thesurface and the samples are carefully labelled respectivelyThe block undisturbed samples collected had a size of about225mm times 225mm times 225mm

23 Disturbed Sample Disturbed samples are used mainlyfor soil classification and compaction tests These were alsocollected from the trial pits

24 Laboratory Work After collection of the samples thefollowing basic and detailed tests were conducted

(A) classification tests

(i) moisture content(ii) liquid limit(iii) plastic limit(iv) particle size analysis(v) specific gravity

(B) soil improvementproperty tests

(i) compaction test(ii) in situ density

(C) strength tests

(i) unconfined compression test(ii) california bearing ratio (CBR) test

Tests which were classified as basic tests include

(1) moisture content test(2) liquid Limit test(3) plastic limit test(4) particle size analysis test

Journal of Engineering 3

Table 2 Summary of soil identification and classification for thelateritic soils

Sample A Fine grained reddish clayey silt A-4 (5)

Sample B Mottled (brown and yellow) fine grainedclayey silt A-4 (6)

Sample C Reddish brown silty clay A-7-6 (10)

Sample D Mottled (yellowish brown) coarse grainedsandy clay with gravel A-2-6 (0)

Sample EMottled (yellowish brown) medium tocoarse grained clayey sand withgravel A-2-6 (3)

Sample F Mottled (yellowish brown and reddish)sandy clay A-6 (4)

Table 3 Summary of the CBR results with other soil parameters

CBR()

OMC()

NMC() GI BulD

(kgm3)MDD(kgm3)

InD(kgm3)

35 206 25 5 20485 1652 1638345 178 24 6 2124 1740 1712950 147 21 10 22011 1826 1819177 116 18 0 23144 1970 1961487 101 16 3 23354 2015 2013148 168 20 4 22651 1796 18876

(5) specific gravity test(6) compaction test(7) in situ density test

While the detailed tests include

(1) unconfined compression test(2) california bearing test

The unconfined compression test is the simplest form ofshear strength test It cannot be made for cohesionless soil oron clay and silt which are too soft to stand in the machinewithout collapsing before the load is applied [26] In the caseof fissured or brittle soils the results are lower than the truein situ strength of these soils The strength of a subgradesubbase and base course materials for road construction isexpressed in terms of their California bearing ratio (CBR)value The CBR value is the resistance to a penetration of25mm of a standard cylindrical plunger of 50mm diameterexpressed as a percentage of the known resistance of theplunger to 25mm in penetration in crushed aggregate (takenas 132 kN)

All tests were performed in accordance to BS1377 [27]and BS5930 [28] Statistical analysis was performed using theStatistical Product and Service Solutions (SPSS) software

3 Results

The result of the particle size distribution analysis for thesamples tested for different trial pits is shown in Figure 1

This gives the percentage occurrence of different grainsizes within the soil mass which in turn is used to describe

Table 4 Summary of the undrained shear strength (119878119906) and other

soil parameters

119878119906

(kPa)OMC()

NMC() GI BulD

(kgm3)MDD(kgm3)

InD(kgm3)

355 206 25 5 20485 1652 1638353 178 24 6 2124 1740 1712961 147 21 10 22011 1826 18191206 116 18 0 23144 1970 196141876 101 16 3 23354 2015 20131108 168 20 4 22651 1796 18876

the soil The result of the sieve analyses shows that the rangeof sand fraction for the samples that lies between 34 and93 while the range of clay and silt fraction is from 0 to47 Atterberg limit tests performed include the liquid limit(119882119871) and the plastic limit (119882

119875) The plasticity index (PI) is

the difference between the liquid limit and the plastic limit Itis the behaviour of the soil in relation to the mass of waterin the soil that is the consistency limits The test result isas shown in the summary in Table 1 The natural moisturecontent ranges from 16 to 25

General soil identification and classification from thevarious tests are presented in Table 2 To evaluate the qualityof a soil as a highway subgrade material a number called thegroup index (G1) must be incorporated with the AASHTOgroups and subgroups of the soil This index is written inparentheses after the group or subgroup designation Thegroup index is determined based on the Atterberg limits andthe percentage of soil particles finer than 0075mm Groupindex values near 0 indicate good soils while values of 20or more indicate very poor soils [29] These lateritic soilsfall within the A-2 A-6 and A-7 groups in the AASHTOclassification system with group index between 0 and 10

Themoisture-density relationship was determined by theuse of standard proctor method A summary of the result ofcompaction tests on the six different samples is in Table 1Themaximumdry density varies from 1652 to 2015 kgm3 and theoptimum moisture content varies from 101 to 206 Themaximum dry density (MDD) increases with the decreasein optimum moisture content The higher the MDD themore stable the soilThe result of the unconfined compressivestrength test on the soil samples obtained from the fieldis summarized in Table 1 For cohesive soil the unconfinedcompressive strength (UCS) is related to the undrained shearstrength (119878

119906) such a relationship is suggested in BS 5930 [27]

From the results samples D and E soils are regarded as hardwhile the remaining samples range from stiff to very stiffThe laboratory California bearing ratio (CBR) (BS1377 [27]Part 4 7) test measures the shearing resistance of a soil undercontrolledmoisture anddensity conditionsTheCBRnumberis used to rate the performance of soils primarily for the useas base and subbase courses beneath the pavement of roadsand airfields The result of the CBR tests that were conductedin the laboratory is summarized in Table 1 Most of the soilscould not meet the 80 CBR value ideal for road baseconstruction but were good for road subbase construction


Table 5 Total variance explained

Component Initial eigenvalues Extraction sums of squared loadingsTotal of variance Cumulative Total of variance Cumulative

1 5127 85457 85457 5127 85457 854572 0724 12074 975313 0130 2172 997024 0018 0296 999985 0000 0002 1000006 minus63119864 minus 016 minus104119864 minus 014 100000Extraction method principal component analysis

0001

BS sieves

001 01 1 10 1000

10

20

30

40

50

60

70

80

90

100

Clay

Coarse

0002 006

Coarse

2

Silt

Fine Medium Fine

Sand

Medium Fine

Gravel

Medium Coarse

60

Cobble

Stone

Boulder

600

200

150

100

65 35

Perc

enta

ge fi

ner

()

Particle size distribution curve

Sample ASample BSample C

Sample DSample ESample F

Particle size (mm)

10 8 6 3

Figure 1 Result of particle size distribution analysis

Table 6 Component matrixa

Component 1InD (Kgm3) 0989NMC () minus0988MDD (Kgm3) 0982BulD (Kgm3) 0978OMC () minus0960GI minus0576Extraction method principal component analysisa1 components extracted

4 Discussion of the Test Results

A summary of the various soil parameters of the differentsoil samples for the comparative study is shown in Tables3 and 4 respectively Principal components analysis based

on the correlation matrix of the variables was used to findoptimal ways of combining the six correlated independentvariables namely in situ density (InD) bulk density (BulD)maximum dry density (MDD) optimum moisture content(OMC) natural moisture content (NMC) and group index(GI) into a small number of subsets Only one principalcomponent out of six had eigenvalue greater than 1 Com-ponent 1 explained 85457 of total variance All the othercomponents had eigenvalues less than 1 which account forless variance and so they are of little use (Table 5) Table 6displays each variablersquos loading on the extracted componentSince the correlation matrix was not positively definite theKMO (Kaiser-Meyer-Olkin) and Bartlettrsquos test conditionswere not satisfied for the principal component analysis to becommended Stepwise multiple regression was then used toestablish predictive equations for the dependent variables ofCalifornia bearing ratio (CBR) and undrained shear strength(119878119906)


Table 7 Correlations (CBR)

CBR () OMC () NMC () GI BulD (kgm3) MDD (kgm3) InD (kgm3)Pearson correlation

CBR () 1000 minus0965 minus0930 minus0587 0878 0980 0908OMC () minus0965 1000 0946 0409 minus0913 minus0994 minus0932NMC () minus0930 0946 1000 0494 minus0977 minus0965 minus0992GI minus0587 0409 0494 1000 minus0508 minus0498 minus0514BulD (kgm3) 0878 minus0913 minus0977 minus0508 1000 0936 0996MDD (kgm3) 0980 minus0994 minus0965 minus0498 0936 1000 0955InD (kgm3) 0908 minus0932 minus0992 minus0514 0996 0955 1000

Sig (1 tailed)CBR () mdash 0001 0004 0110 0011 0000 0006OMC () 0001 mdash 0002 0210 0006 0000 0003NMC () 0004 0002 mdash 0159 0000 0001 0000GI 0110 0210 0159 mdash 0152 0157 0148BulD (kgm3) 0011 0006 0000 0152 mdash 0003 0000MDD (kgm3) 0000 0000 0001 0157 0003 mdash 0002InD (kgm3) 0006 0003 0000 0148 0000 0002 mdash

NCBR () 6 6 6 6 6 6 6OMC () 6 6 6 6 6 6 6NMC () 6 6 6 6 6 6 6GI 6 6 6 6 6 6 6BulD (kgm3) 6 6 6 6 6 6 6MDD (kgm3) 6 6 6 6 6 6 6InD (kgm3) 6 6 6 6 6 6 6

Table 8 Model summaryb

Model R Rsquare

Adjusted Rsquare

Std error of theestimate

Durbin-Watson

1 0980a 0960 0950 4535 2035aPredictors (constant) MDD (kgm3)

bDependent variable CBR ()

Table 9 ANOVAb

Model Sum of squares df Mean square 119865 Sig

1Regression 1975726 1 1975726 96056 0001a

Residual 82274 4 20569Total 2058000 5

aPredictors (constant) MDD (kgm3)


41 Relationship between California Bearing Ration (CBR) andOther Soil Parameters For the stepwise multiple regressionthe CBR correlation table (Table 7) shows three things Firstit shows the value of the pearson correlation coefficientbetween every pair of variableTheMDD (kgm3) had a largepositive correlation with CBR () 119877 = 0980 Second theone tailed significance of each correlation is displayed TheCBR-MDD correlation is significant with 119875 lt 0001 The

number of cases contributing to each correlation 119873 = 6 isalso shown on this Table The correlation is extremely usefulfor getting a rough idea of the relationships between the pre-dictors (independent variables) and the outcome (dependentvariable) and for a preliminary look for multicollinearity Ifthere is no multicollinearity in the data then there shall beno substantial correlation The next section of the output(Table 8) describes the overall model and indicates whetherthe model is successful in predicting the CBR Since stepwise(hierarchical) regression is used each set of the summarystatistics was repeated for each stage in the hierarchy Table 8has only one model (model 1) Model 1 refers to the first andonly stage in the hierarchy when only the MDD (kgm3) isused as predictor In the column labelled 119877 is the value of thecorrelation coefficient between the predictor and the outcomewhen only MDD is used as a predictor This is the simplecorrelation between the predictor and the outcome variable(119877 = 0980) The next column gives the value of 1198772 whichis a measure of how much of the variability in the outcomeis accounted for by the predictor For the model its value is0960 which means that MDD alone accounts for 96 of thevariation in CBR The next part of the analysis contains theanalysis of variance (ANOVA) (Table 9) which test whetherthe model is significantly better at predicting the outcomethan using the mean as a ldquobest guessrdquo Specifically the 119865 ratiorepresents the ratio of the improvement in prediction thatresults from fitting the model relative to the inaccuracy thatstill exists in the model For the model 119865 ratio (119865) is 96056


Table 10 Coefficientsa

ModelUnstandardized coefficients Standardized coefficients119861 Std Error Beta

t Sig

1 (Constant) minus207754 27077 minus7673 0002MDD (Kgm3) 0144 0015 0980 9801 0001

aDependent variable CBR ()

Table 11 Correlations (119878119906)

119878119906(kPa) OMC () NMC () GI BulD (kgm3) MDD (kgm3) InD (kgm3)

Pearson correlation119878119906(kPa) 1000 minus0886 minus0915 minus0776 0908 0929 0920

OMC () minus0886 1000 0946 0409 minus0913 minus0994 minus0932NMC () minus0915 0946 1000 0494 minus0977 minus0965 minus0992GI minus0776 0409 0494 1000 minus0508 minus0498 minus0514BulD (kgm3) 0908 minus0913 minus0977 minus0508 1000 0936 0996MDD (kgm3) 0929 minus0994 minus0965 minus0498 0936 1000 0955InD (kgm3) 0920 minus0932 minus0992 minus0514 0996 0955 1000

Sig (1-tailed)119878119906(kPa) mdash 0009 0005 0035 0006 0004 0005

OMC () 0009 mdash 0002 0210 0006 0000 0003NMC () 0005 0002 mdash 0159 0000 0001 0000GI 0035 0210 0159 mdash 0152 0157 0148BulD (kgm3) 0006 0006 0000 0152 mdash 0003 0000MDD (kgm3) 0004 0000 0001 0157 0003 mdash 0002InD (kgm3) 0005 0003 0000 0148 0000 0002 mdash

N119878119906(kPa) 6 6 6 6 6 6 6

OMC () 6 6 6 6 6 6 6NMC () 6 6 6 6 6 6 6GI 6 6 6 6 6 6 6BulD (kgm3) 6 6 6 6 6 6 6MDD (kgm3) 6 6 6 6 6 6 6InD (kgm3) 6 6 6 6 6 6 6

Table 12 Model Summaryc

Model 119877 119877

squareAdjusted 119877square

Std error ofthe estimate

Durbin-Watson

1 0929a 0863 0829 3009002 0997b 0994 0989 75638 2451aPredictors (constant) MDD (kgm3)

bPredictors (constant) MDD (kgm3) GIcDependent variable 119878

119906(kPa)

which is very unlikely to have happened by chance (119875 lt0005) We can interpret this result meaning that the modelis significantly improved in ability to predict the outcomevariable Table of coefficients (Table 10) is concerned with theparameters of the model

The model include only the MDD (kgm3) as the predic-tor variable The model is of the form CBR = 119887

0+ 1198871MDD

Table 13 ANOVAc


1Regression 22807174 1 22807174 25190 0007a

Residual 3621635 4 905409Total 26428808 5

2Regression 26257175 2 13128588 229476 0001b

Residual 171633 3 57211Total 26428808 5



119906(kPa)

where 1198870and 119887

1are the model parameters and 119887

0is the

intercept from the table (Table 10) this value is minus2077541198871represents the gradient of the regression line from the

table this value is 0144 Although this value is the slope ofthe regression line it is more useful to think of this value as



ModelUnstandardized coefficients Standardized coefficients119861 Std error Beta

t Sig


2(Constant) minus547713 54963 minus9965 0002MDD (Kgm3) 0381 0028 0722 13451 0001GI minus9104 1172 minus0417 minus7766 0004

aDependent variable 119878

119906(kPa)

representing the change in the outcome associated with a unitchange in the predictorThe established model for predictingthe California bearing ratio (CBR) of the lateritic soils is

CBR = minus207754 + 0144MDD

(11986514= 96056 119875 lt 0005 Adjusted 1198772 = 0950

Std Error = 4535)

(1)

For the included predictor variable beta = 0980 119875 lt 0005forMDDwhere other variables are excluded from themodel

42 Relationship between Undrained Shear Strength (119878119906) and

Other Soil Parameters For the stepwise multiple regressionthe undrained shear strength (119878

119906) correlation table is Table 11

The overall model Table 12 has two models (models 1 and2) The first model uses MDD as the only dependent variablewith 1198772 of 0863 meaning that MDD alone accounts for863 of the variation in the dependent variable and themodel is given as

119878119906= minus791012 + 0491MDD

(11986514= 25190 119875 lt 001 Adjusted 1198772 = 0829

Std Error = 3009)

(2)

where other variables are excluded from this model themodel considers the MDD as the best variable that explainsthe dependent variable because it accounts for almost all thevariations in the dependent variable it does not account for100 minus 860 = 137 of the variation The second modelincludes another variable (Group index (GI)) into the modeland the new model is given as

119878119906= minus547713 + 0381MDD minus 9104GI

(11986523= 229476 119875 lt 0005Adjusted 1198772 = 0989

Std Error = 75638)

(3)

The 1198772 for (3) is 994 meaning that the second modelaccount for 994 of the variation in the dependent variablesince MDD alone has accounted for 863 therefore GIaccounts for 994 minus 863 = 131 Since the percentageexplained by the secondmodel is greater than the first model

the second model should be considered as the best modelthat explains the dependent variable and this model makesuse of MDD and GI as the explanatory variables while othervariables are excluded The analysis of variance (ANOVA)and model coefficients are summarized in Tables 13 and 14For the included predictor variables beta = 0722 119875 lt 0005and beta = minus0417 119875 lt 0005 for MDD and GI respectively

5 Conclusions

An increase in in situ density (InD) bulk density (BulD)and maximum dry density (MDD) gave a correspondingincrease in the dependent variables of California bearing ratio(CBR) and undrained shear strength (119878

119906)While a decrease in

optimummoisture content (OMC) natural moisture content(NMC) and group index (GI) of the soils led to an increasein the dependent variables for the soils The regressionmodels (1) and (3) are proposed for the estimation ofthe CBR and the undrained shear strength (119878

119906) of tropical

lateritic soils The high coefficients of determination for thevarious recommended relations allude to the reliability ofthe empirical relations and to a great extent allay the fear oferroneous shear strength prediction using them

The proposed relationships can serve as an indirectmethod of establishing soil shear strength and compressibil-ity The result of these tests will be useful to both individualsand government agencies involved in building constructionwhomay be worried about the huge cost of those detailed soilstrength tests and the time consumed in conducting them

These derived relations and equations can be used for theprediction of the shear strength of similar tropical lateriticsoils especially in the estimation of soil shear strength forthe preliminaryfirst phase engineering design of engineeringinfrastructure

Acknowledgments

The author is am grateful to project student J N Ejechi forhis involvement in data collection Dr Philip Oguntunde andMr Nurudeen Adegoke are acknowledged for their assistancein the statistical analysis

References

[1] H Singh and B B K Huat ldquoOrigin formation and occurrenceof tropical residual soilsrdquo in Tropical Residual Soils Engineering


B B K Huat G See-Sew and F H Ali Eds Taylor amp FrancisLondon UK 2004

[2] G Baldovin ldquoThe shear strength of lateritic soilsrdquo inProceedingsof the Specialty Session on Engineering Properties of LateriticSoiles of the 7th International Conference on Soil Mechanicsand Foundation Engineering vol 1 pp 129ndash142 Mexico CityMexico

[3] S Malomo ldquoThe compressibility characteristics of a compactedlaterite soilrdquoBulletin of the International Association of Engineer-ing Geology vol 24 no 1 pp 151ndash154 1981

[4] S Malomo ldquoStress-strain behaviour of some compacted lateritesoils from north-east Brazilrdquo Bulletin of the International Asso-ciation of Engineering Geology vol 28 no 1 pp 49ndash54 1983

[5] S Malomo ldquoMicrostructural investigation on laterite soilsrdquoBulletin of the International Association of Engineering Geologyvol 39 no 1 pp 105ndash109 1989

[6] O Ogunsanwo ldquoVariability in the shear strength characteristicsaf an amphibolite derived laterite soilrdquo Bulletin of the Interna-tional Association of Engineering Geology no 32 pp 111ndash1151985

[7] M D Gidigasu Laterite Soil Engineering-Pedogenesis and Engi-neering Principles Elsevier AmsterdamThe Netherlands 1976

[8] S Malomo ldquoStress-strain behaviour of some compacted lateritesoilsrdquo Revista Brasileira da Geologia vol 2 1980

[9] R M Madu ldquoAn investigation into the geotechnical andengineering properties of some laterites of Eastern NigeriardquoEngineering Geology vol 11 no 2 pp 101ndash125 1977

[10] S A Ola ldquoGeotechnical properties and behaviour of some sta-bilized Nigerian lateritic soilsrdquoQuarterly Journal of EngineeringGeology and Hydrogeology vol 11 pp 145ndash160 1978

[11] S A Ola ldquoPermeability of three compacted tropical soilsrdquoQuarterly Journal of Engineering Geology vol 13 no 2 pp 87ndash95 1980

[12] S A Ola ldquoGeotechnical properties and behaviour of someNigerian lateritic soilsrdquo inTropical Soils ofNigeria in EngineeringPractice S A Ola Ed pp 61ndash84 A A Balkema RotterdamThe Netherlands 1983

[13] O Ogunsanwo ldquoBasic index properties mineralogy andmicrostructure of an amphibolite derived laterite soilrdquo Bulletinof the International Association of Engineering Geology vol 33no 1 pp 19ndash25 1986

[14] O Ogunsanwo ldquoSome geotechnical properties of two lateritesoils compacted at different energiesrdquo Engineering Geology vol26 no 3 pp 261ndash269 1989

[15] SMalomo ldquoPenetration resistance and basic engineering prop-erties of laterite profile soilsrdquo in Proceedings of the 5th Interna-tional Association of Engineering Geology Congress (IAEG rsquo86)pp 821ndash828 Buenos Aires Argentina 1986

[16] E A Mesida ldquoThe relationship between the geology and thelateritic engineering soils in the northern environs of AkureNigeriardquo Bulletin of the International Association of EngineeringGeology vol 35 no 1 pp 65ndash69 1987

[17] M Fall J P Tisot and I K Cisse ldquoSpecifications for road designusing statistical data an example of laterite or gravel lateriticsoils from Senegalrdquo Bulletin of the International Association ofEngineering Geology vol 50 pp 17ndash35 1994

[18] L Miao S Liu and Y Lai ldquoResearch of soil-water character-istics and shear strength features of Nanyang expansive soilrdquoEngineering Geology vol 65 no 4 pp 261ndash267 2002

[19] M A Tekinsoy C Kayadelen M S Keskin and M SoylemezldquoAn equation for predicting shear strength envelope with

respect to matric suctionrdquo Computers and Geotechnics vol 31no 7 pp 589ndash593 2004

[20] S Nam M Gutierrez P Diplas and J Petrie ldquoDeterminationof the shear strength of unsaturated soils using the multistagedirect shear testrdquo Engineering Geology vol 122 no 3-4 pp 272ndash280 2011

[21] A Zhou D Sheng S W Sloan and A Antonio Gens ldquoInter-pretation of unsaturated soil behaviour in the stressmdashSaturationspace I volume change and water retention behaviorrdquoComput-ers and Geotechnics vol 43 pp 178ndash187 2012

[22] B S Narendra P V Sivapullaiah S Suresh and S N OmkarldquoPrediction of unconfined compressive strength of soft groundsusing computational intelligence techniques a comparativestudyrdquo Computers and Geotechnics vol 33 no 3 pp 196ndash2082006

[23] M Ajdari G Habibagahi and A Ghahramani ldquoPredictingeffective stress parameter of unsaturated soils using neuralnetworksrdquo Computers and Geotechnics vol 40 pp 89ndash96 2012

[24] G R Khanlari M Heidari A A Momeni and Y AbdilorldquoPrediction of shear strength parameters of soils using artificialneural networks and multivariate regression methodsrdquo Engi-neering Geology vol 131-132 pp 11ndash18 2012

[25] V N S Murthy Geotechnical Engineering Principles and Prac-tices of Foundation Engineering CRC Press Boca Raton FlaUSA 2nd edition 2009

[26] E S Reddy and K R Sastri Measurement of EngineeringProperties of Soils New Age International New Delhi India 1stedition 2002

[27] ldquoMethod of test for soil for civil engineering purposerdquo BS 1377British Standard Institute London UK 1990

[28] ldquoCode of practice for site investigationsrdquo BS 5930 BritishStandard Institute London UK 1999

[29] B M Das Principles of Geotechnical Engineering ThomsonLearning Stamford Conn USA 5th edition 2001

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Navigation and Observation



DistributedSensor Networks



Table 1 Summary of geotechnical test results for the six locations

Sample Depth(m) 119882

119871() 119882

119901() Plasticity index

(PI)

Natural moisturecontent()

MDD(kgm3)

OMC()

UCS(kPa)

119878119906

(kPa)CBR value

()

A 15 32 24 8 25 1652 206 71 355 35B 15 36 241 119 24 1742 178 106 53 45C 15 48 239 241 21 1826 147 122 61 50D 15 32 19 13 18 1970 116 412 206 77E 15 34 20 14 16 2015 101 375 1875 87F 15 38 21 17 20 1796 168 216 108 48

an unsaturated soil [18ndash21] Artificial neural networks andmultivariate regression have also been applied to estimate theshear strength of soils [22ndash24]

According to Murthy [25] soil survey ranges from visualinspection of trail pits to an extensive borehole investigationwith deep and numerous boreholes and extensive samplingand testing of the soil usually by investigation specialistSoil investigation is a necessary process which should becarried out prior to commencement of actual constructionwork Tests to determine the shear strength or density ofsoils in situ are a valuable means of investigation sincethese characteristics can be obtained directly without thedisturbing effect of boring and sampling [15 17]

2 Research Methodology

The method used in the sample collection is the trial pitmethod Undisturbed block samples were collected from six(6) different trial pits each in different sites in FUTA Thesesites and their locations are listed below

(i) proposed site for school of mines and earth sciencewhich is sample A

(ii) new library complex which is sample B(iii) centre for continuing education (CCE) which is sam-

ple C(iv) engineering workshop complex which is sample D(v) malu road that is behind Abiola Hall sample E(vi) proposed site for hostel accommodation which is

sample F

21 Test or Trial Pits This is the method of site explorationthat was used in this research work A test pit is simply ahole dug in the ground that is large enough for a ladderto be inserted thus permitting a close examination of thesides With this method relatively undisturbed samples ofsoils were collected

The depth of each trial pit was 15m (5 ft) and about 4 ft times4 ft wide that is 12m times 12m times 15m pitThe types of samplescollected for the laboratory analysis are as follows

(i) Disturbed samples(ii) Undisturbed samples

The pit was sunk by hand excavationwith the aid of spade anddigger

22 Undisturbed Sample Undisturbed samples are requiredfor shear strength tests on the soil Undisturbed block sampleswere cut by hand from the bottom of the pits During cuttingthe samples are protected from water wind and sun to avoidany change in water content The samples are covered withblack polythene bag immediately they are brought to thesurface and the samples are carefully labelled respectivelyThe block undisturbed samples collected had a size of about225mm times 225mm times 225mm

23 Disturbed Sample Disturbed samples are used mainlyfor soil classification and compaction tests These were alsocollected from the trial pits

24 Laboratory Work After collection of the samples thefollowing basic and detailed tests were conducted

(A) classification tests

(i) moisture content(ii) liquid limit(iii) plastic limit(iv) particle size analysis(v) specific gravity

(B) soil improvementproperty tests

(i) compaction test(ii) in situ density

(C) strength tests

(i) unconfined compression test(ii) california bearing ratio (CBR) test

Tests which were classified as basic tests include

(1) moisture content test(2) liquid Limit test(3) plastic limit test(4) particle size analysis test










CBR()

OMC()

NMC() GI BulD

(kgm3)MDD(kgm3)

InD(kgm3)

35 206 25 5 20485 1652 1638345 178 24 6 2124 1740 1712950 147 21 10 22011 1826 1819177 116 18 0 23144 1970 1961487 101 16 3 23354 2015 2013148 168 20 4 22651 1796 18876






3 Results




soil parameters

119878119906

(kPa)OMC()

NMC() GI BulD

(kgm3)MDD(kgm3)

InD(kgm3)

355 206 25 5 20485 1652 1638353 178 24 6 2124 1740 1712961 147 21 10 22011 1826 18191206 116 18 0 23144 1970 196141876 101 16 3 23354 2015 20131108 168 20 4 22651 1796 18876












0001

BS sieves

001 01 1 10 1000

10

20

30

40

50

60

70

80

90

100

Clay

Coarse

0002 006

Coarse

2

Silt

Fine Medium Fine

Sand

Medium Fine

Gravel

Medium Coarse

60

Cobble

Stone

Boulder

600

200

150

100

65 35

Perc

enta

ge fi

ner

()




Particle size (mm)

10 8 6 3














Model R Rsquare

Adjusted Rsquare


Durbin-Watson



Table 9 ANOVAb


1Regression 1975726 1 1975726 96056 0001a

Residual 82274 4 20569Total 2058000 5








t Sig









N119878119906(kPa) 6 6 6 6 6 6 6



Model 119877 119877



Durbin-Watson



119906(kPa)



0+ 1198871MDD

Table 13 ANOVAc


1Regression 22807174 1 22807174 25190 0007a

Residual 3621635 4 905409Total 26428808 5

2Regression 26257175 2 13128588 229476 0001b

Residual 171633 3 57211Total 26428808 5



119906(kPa)

where 1198870and 119887


0is the






t Sig




119906(kPa)



(11986514= 96056 119875 lt 0005 Adjusted 1198772 = 0950

Std Error = 4535)

(1)






119878119906= minus791012 + 0491MDD

(11986514= 25190 119875 lt 001 Adjusted 1198772 = 0829

Std Error = 3009)

(2)



(11986523= 229476 119875 lt 0005Adjusted 1198772 = 0989

Std Error = 75638)

(3)



5 Conclusions




119906) of tropical




Acknowledgments


References



































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation


















CBR()

OMC()

NMC() GI BulD

(kgm3)MDD(kgm3)

InD(kgm3)

35 206 25 5 20485 1652 1638345 178 24 6 2124 1740 1712950 147 21 10 22011 1826 1819177 116 18 0 23144 1970 1961487 101 16 3 23354 2015 2013148 168 20 4 22651 1796 18876






3 Results




soil parameters

119878119906

(kPa)OMC()

NMC() GI BulD

(kgm3)MDD(kgm3)

InD(kgm3)

355 206 25 5 20485 1652 1638353 178 24 6 2124 1740 1712961 147 21 10 22011 1826 18191206 116 18 0 23144 1970 196141876 101 16 3 23354 2015 20131108 168 20 4 22651 1796 18876












0001

BS sieves

001 01 1 10 1000

10

20

30

40

50

60

70

80

90

100

Clay

Coarse

0002 006

Coarse

2

Silt

Fine Medium Fine

Sand

Medium Fine

Gravel

Medium Coarse

60

Cobble

Stone

Boulder

600

200

150

100

65 35

Perc

enta

ge fi

ner

()




Particle size (mm)

10 8 6 3














Model R Rsquare

Adjusted Rsquare


Durbin-Watson



Table 9 ANOVAb


1Regression 1975726 1 1975726 96056 0001a

Residual 82274 4 20569Total 2058000 5








t Sig









N119878119906(kPa) 6 6 6 6 6 6 6



Model 119877 119877



Durbin-Watson



119906(kPa)



0+ 1198871MDD

Table 13 ANOVAc


1Regression 22807174 1 22807174 25190 0007a

Residual 3621635 4 905409Total 26428808 5

2Regression 26257175 2 13128588 229476 0001b

Residual 171633 3 57211Total 26428808 5



119906(kPa)

where 1198870and 119887


0is the






t Sig




119906(kPa)



(11986514= 96056 119875 lt 0005 Adjusted 1198772 = 0950

Std Error = 4535)

(1)






119878119906= minus791012 + 0491MDD

(11986514= 25190 119875 lt 001 Adjusted 1198772 = 0829

Std Error = 3009)

(2)



(11986523= 229476 119875 lt 0005Adjusted 1198772 = 0989

Std Error = 75638)

(3)



5 Conclusions




119906) of tropical




Acknowledgments


References



































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation













0001

BS sieves

001 01 1 10 1000

10

20

30

40

50

60

70

80

90

100

Clay

Coarse

0002 006

Coarse

2

Silt

Fine Medium Fine

Sand

Medium Fine

Gravel

Medium Coarse

60

Cobble

Stone

Boulder

600

200

150

100

65 35

Perc

enta

ge fi

ner

()




Particle size (mm)

10 8 6 3














Model R Rsquare

Adjusted Rsquare


Durbin-Watson



Table 9 ANOVAb


1Regression 1975726 1 1975726 96056 0001a

Residual 82274 4 20569Total 2058000 5








t Sig









N119878119906(kPa) 6 6 6 6 6 6 6



Model 119877 119877



Durbin-Watson



119906(kPa)



0+ 1198871MDD

Table 13 ANOVAc


1Regression 22807174 1 22807174 25190 0007a

Residual 3621635 4 905409Total 26428808 5

2Regression 26257175 2 13128588 229476 0001b

Residual 171633 3 57211Total 26428808 5



119906(kPa)

where 1198870and 119887


0is the






t Sig




119906(kPa)



(11986514= 96056 119875 lt 0005 Adjusted 1198772 = 0950

Std Error = 4535)

(1)






119878119906= minus791012 + 0491MDD

(11986514= 25190 119875 lt 001 Adjusted 1198772 = 0829

Std Error = 3009)

(2)



(11986523= 229476 119875 lt 0005Adjusted 1198772 = 0989

Std Error = 75638)

(3)



5 Conclusions




119906) of tropical




Acknowledgments


References



































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation
















Model R Rsquare

Adjusted Rsquare


Durbin-Watson



Table 9 ANOVAb


1Regression 1975726 1 1975726 96056 0001a

Residual 82274 4 20569Total 2058000 5








t Sig









N119878119906(kPa) 6 6 6 6 6 6 6



Model 119877 119877



Durbin-Watson



119906(kPa)



0+ 1198871MDD

Table 13 ANOVAc


1Regression 22807174 1 22807174 25190 0007a

Residual 3621635 4 905409Total 26428808 5

2Regression 26257175 2 13128588 229476 0001b

Residual 171633 3 57211Total 26428808 5



119906(kPa)

where 1198870and 119887


0is the






t Sig




119906(kPa)



(11986514= 96056 119875 lt 0005 Adjusted 1198772 = 0950

Std Error = 4535)

(1)






119878119906= minus791012 + 0491MDD

(11986514= 25190 119875 lt 001 Adjusted 1198772 = 0829

Std Error = 3009)

(2)



(11986523= 229476 119875 lt 0005Adjusted 1198772 = 0989

Std Error = 75638)

(3)



5 Conclusions




119906) of tropical




Acknowledgments


References



































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation












t Sig









N119878119906(kPa) 6 6 6 6 6 6 6



Model 119877 119877



Durbin-Watson



119906(kPa)



0+ 1198871MDD

Table 13 ANOVAc


1Regression 22807174 1 22807174 25190 0007a

Residual 3621635 4 905409Total 26428808 5

2Regression 26257175 2 13128588 229476 0001b

Residual 171633 3 57211Total 26428808 5



119906(kPa)

where 1198870and 119887


0is the






t Sig




119906(kPa)



(11986514= 96056 119875 lt 0005 Adjusted 1198772 = 0950

Std Error = 4535)

(1)






119878119906= minus791012 + 0491MDD

(11986514= 25190 119875 lt 001 Adjusted 1198772 = 0829

Std Error = 3009)

(2)



(11986523= 229476 119875 lt 0005Adjusted 1198772 = 0989

Std Error = 75638)

(3)



5 Conclusions




119906) of tropical




Acknowledgments


References



































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation












t Sig




119906(kPa)



(11986514= 96056 119875 lt 0005 Adjusted 1198772 = 0950

Std Error = 4535)

(1)






119878119906= minus791012 + 0491MDD

(11986514= 25190 119875 lt 001 Adjusted 1198772 = 0829

Std Error = 3009)

(2)



(11986523= 229476 119875 lt 0005Adjusted 1198772 = 0989

Std Error = 75638)

(3)



5 Conclusions




119906) of tropical




Acknowledgments


References



































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation









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