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Compositional and environmental factors role on compression index
Compositional and environmental factors role on compression index
Compositional and environmental factors role on compression index
Compositional and environmental factors role on compression index
Compositional and environmental factors role on compression index
Compositional and environmental factors role on compression index
Compositional and environmental factors role on compression index
Compositional and environmental factors role on compression index
Compositional and environmental factors role on compression index
Compositional and environmental factors role on compression index
Compositional and environmental factors role on compression index
Compositional and environmental factors role on compression index
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Compositional and environmental factors role on compression index

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  • 1. International Journal of CivilJOURNAL OF CIVIL (IJCIET), ISSN 0976 – AND INTERNATIONAL Engineering and Technology ENGINEERING 6308(Print), ISSN 0976 – 6316(Online) Volume 3, Issue 2, July- December (2012), © IAEME TECHNOLOGY (IJCIET)ISSN 0976 – 6308 (Print)ISSN 0976 – 6316(Online)Volume 3, Issue 2, July- December (2012), pp. 392-403 IJCIET© IAEME: www.iaeme.com/ijciet.aspJournal Impact Factor (2012): 3.1861 (Calculated by GISI)www.jifactor.com © IAEME COMPOSITIONAL AND ENVIRONMENTAL FACTORS ROLE ON COMPRESSION INDEX Ch. Sudha Rani1, K Mallikarjuna Rao 2 1 (Associate Professor, Dept of Civil Engineering, Sri Venkateswara University, Tirupati, India-517502. E-mail: sudhajawahar@gmail.com)2 (Professor, Dept of Civil Engineering, Sri Venkateswara University, Tirupati, India-517502, E-mail: kmr_svuce@yahoo.com)ABSTRACT Empirical correlations developed by several investigators for prediction ofCompression Index either in terms of Liquid Limit/Plasticity Index, represent compositionand Dry Density/initial Moisture Content/ initial Void Ratio reflect the state/environment ofthe soil. In this investigation an attempt has been made to find the influence of each of thecompositional and environmental factors on Compression Index through experimentalinvestigations. Fifteen regression models were developed after carrying out linear regressionanalyses for prediction of Compression Index (Cc) in terms of the environmental factorsalone, compositional factors alone and combined environmental and compositional factors.The degree of influence of each of the variables on the dependant variable was found byestimating partial correlation coefficient. Plasticity Index (IP), Initial Dry Density (γd), InitialMoisture Content (mc) and Liquid limit (wL) were found to have influence on CompressionIndex (Cc) in that order. Comparison of predicted and observed Compression Index ofseventy soils collected from literature indicate that the models developed using all the fourinfluencing parameters or atleast one compositional factor but both the environmental factorshave more general applicability than other models.KEY WORDS: Consolidation, Compression Index, Regression coefficient, Compositionalfactors, Environmental factors, Partial correlation coefficient.1. INTRODUCTION Compression Index is widely used in Geotechnical Engineering practice forevaluation of settlement of structures resting on clayey soils. Compressibility of soils isrepresented by Compression Index (Cc), the slope of virgin part of the compression curve 392
  • 2. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308(Print), ISSN 0976 – 6316(Online) Volume 3, Issue 2, July- December (2012), © IAEMEobtained from One-Dimensional Consolidation test on undisturbed samples. However,collection of undisturbed samples and conduct of consolidation test involves considerabletime and money apart from the services of the domain experts and trained technicians. Henceseveral attempts have been made in the past to develop simple correlations for prediction ofCompression Index using properties which can be easily determined. Ever since Casagrandefound that the Atterberg limits provide more reliable indication of engineering properties,several investigators developed correlations for prediction of Compression Index in terms ofLiquid Limit (Skempton 1944, Terzaghi&Peck 1967, and Bowles 1979), Plasticity Index(Jian-Hua Yin 1999, AmithNath and DeDalal 2004) or Shrinkage Index (Sridharan andNagraj 2001) based on tests conducted on a limited number of soils pertaining to certainregion. Another group of investigators expressed Compression Index in terms of in-situ VoidRatio (Nishida 1956, Hough 1957, and Bowles 1979) or in-situ Moisture Content (Bowles1979, and Koppula 1981) or in-situ Dry Density (Oswald 1980) presuming that thecompressibility is mainly a function of state of soil. However, the engineering properties ofsoils are now said to be dependent on the composite effect of compositional andenvironmental factors (Mitchel, 1993). None of the currently used correlations or modelsaccount for both compositional and environmental factors in their development.Conventionally, Atterberg limits or indices derived from it are used as indicators of soilcomposition as direct determination of mineralogical composition is both difficult and notroutinely carried out in any soil investigation. Liquid Limit and Plasticity Index are known toreflect compositional factors while in-situ Dry Density and natural Moisture Content are theimportant environmental factors that influence the engineering properties significantly. Theobjective of this investigation is to assess the degree of association between CompressionIndex and each of the influencing parameters namely Liquid limit (wL),Plasticity Index (IP),Initial Dry Density (γd) and Initial Moisture Content (mc) and to develop a model accountingfor all the influencing parameters. Such a model is expected to have a more generalapplicability.2. EXPERIMENTAL INVESTIGATION Undisturbed and Representative but disturbed clayey soil samples from different partsof India are collected from open trial pits at depths ranging from 2.0m to 2.5m depths afterthorough saturation. Undisturbed samples are obtained using 100mm diameter thin walledsampling tubes essentially satisfying the specifications laid in IS: 2132, 1986. OneDimensional Consolidation tests and identification and classification tests are conducted onall these 15 samples as per the specifications given in special publication (SP 36 Part I, 1987)published by Bureau of Indian Standards (BIS). The loading sequence followed inconsolidation test is 5, 10,20,40,80,160, and 320 kPa, the load increment ratio being one andnominal surcharge being 5 kPa. Each load is sustained for at least 24 hours before applyingnext load increment. The index properties of soils used, placement conditions andcompression Index of all soils tested are presented in Table 1. From Table 1 it can beobserved that for the soil samples tested, the Liquid Limit is ranging from 30% to 140%, DryDensity is varying from 14kN/m3 to 21kN/m3, Moisture Content is ranging from 10% to 32%and Plasticity Index is ranging from 15% to 105%. The range of each of the parametersconsidered is so wide that it covers practically most of the soils that are likely to beencountered in general practice. The fifteen soils used in the series of tests were designatedas CS1, CS2, CS3, CS4, CS5, CS6, CS7, CS8, CS9, CS10, CS11, CS12, CS13, CS14 andCS15 for convenience. 393
  • 3. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308(Print), ISSN 0976 – 6316(Online) Volume 3, Issue 2, July- December (2012), © IAEME Table 1. Results of the Tested Soils Placement Atterberg Limits Conditions Compressi Soil In- S.No In on Index Designation wL(% wP IP situ -situ (Cc ) ) (%) (%) γd kN/ 3 mc (%) m 22.0 41.0 10.5 1. CS1 63.00 20.80 0.290 0 0 0 2 C 8 3 4 1 2 0 3 C 6 2 4 1 1 0 4 C 5 2 1 2 1 0 5 C 7 2 3 1 2 0 6 C 3 1 1 2 1 0 7 C 3 2 1 1 1 0 8 C 1 3 9 1 3 0 9 C 9 1 7 1 2 0 1 C 1 3 1 1 2 0 1 CS11 5 2 2 1 2 0 1 C 6 3 3 1 3 0 1 C 4 1 3 1 2 0 1 C 5 3 2 1 2 0 1 CS15 5 3 2 1 2 03. RESULTS AND DISCUSSIONS Typical e-log p plots obtained from One-Dimensional Consolidation tests are shownin Fig 1. The initial portion of these plots is observed to be fairly flat upto a stress of about 50kPa. This is owing to the fact that the soil samples are collected at depths ranging from 2.0mto 2.5m, the insitu overburden pressure being about 50 kPa. Compression Index valuesdenoted by Cc are obtained by taking the slope of the virgin portion of e-log p plots (slope ofthe average straight line beyond 50 kPa) of all the soils tested and are summarized in Table1. The Compression Index of the soils is ranging from as low as 0.10 to as high as 0.50. TheCompression Index of the soils may be expressed as given below in terms of compositionalfactors (liquid limit, plasticity index) and environmental factors (dry density and moisturecontent): Cc = f ((wL, mc, γd, IP)) … (1) The Compression Index may also bear relationship with any one or combination of theabove said four parameters provided there is some interaction amongst the parametersthemselves. However, such interactions may or may not be unique for all soils. Consequently 394
  • 4. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308(Print), ISSN 0976 – 6316(Online) Volume 3, Issue 2, July- December (2012), © IAEMEall the correlations may or may not be valid for all the soils in general. Hence, linear regressionanalyses were carried out to develop correlations for prediction of Compression Index (Cc) in terms ofeach of the compositional factors namely, wL and IP and the environmental factors namely, mc and γd.Further, multiple linear regression analyses were carried out to correlate Cc with all possiblecombinations of environmental factors alone, compositional factors alone and combinedenvironmental and compositional factors. The details of multiple linear regression analysis correlatingdependent variable with more than one independent variable may be found in Applied Statistics forEngineers by Montgomery and Runger (1999) or in any standard text book on Applied Statistics. .Statistical software like SPSS or Data Analysis tool Pack of Microsoft excel supports a function orsubroutine for carrying out multiple linear regression analysis. Data Analysis tool pack of Microsoftexcel is used In this investigation .The regression models so developed along with correlationcoefficients are presented in Table 2. These correlations are designated as E1 to E15 for convenience.Regression models E1, E2, and E3 consider only compositional factors whereas the models E4, E5,and E6 accounts for only environmental factors in the development of models. Rest of the modelsfrom E7 to E15 considers all the possible combinations of both compositional and environmentalfactors. The correlation coefficient (R2) values of models E1, E2, and E3 indicate that Cc has verygood correlation with any of the compositional factors wL or IP and also with the combination of wLand IP. The three models namely E4, E5, and E6 which are developed considering the environmentalfactors alone are found to yield very low correlation coefficient. Regression model E4 relates Cc withenvironmental factor ‘mc’ and the correlation coefficient R2 is 0.11 and model E5 relates Cc withenvironmental factor γd and the R2 = 0 .125 whereas model E6 relates Cc with combination of thesetwo environmental factors namely mc and γd and the R2 value is 0.136. Hence, it may be concludedthat the correlations involving environmental factors alone (models E4, E5, and E6) are notsatisfactory. All the models E7 to E15 which relate Cc with all possible combination ofenvironmental and compositional factors are observed to yield good correlation coefficients. In otherwords, when the environmental factors are combined with any one of the compositional factorsnamely wL and IP there is a considerable improvement in the correlation coefficient. In fact thestandard deviation of residuals is lowest for two models E11 and E15 which involve both theenvironmental factors apart from compositional factors. Further, correlations involving compositionalfactors alone (models E1, E2, and E3) are also good. This clearly brings out that even though thecompositional factors play dominant role in determining the compressibility of clayey soils, inclusionof environmental factors improve the model efficiency and possibly the general applicability toowhich needs to be ascertained by comparing with others data. 1.5 CS12 CS11 CS9 1.25 Void Ratio 1 0.75 1 10 100 1000 Pressure(kPa) Fig. 1 Typical e-log p plots 395
  • 5. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308(Print), ISSN 0976 – 6316(Online) Volume 3, Issue 2, July- December (2012), © IAEME4. DEGREE OF INFLUENCE OF COMPOSITIONAL AND ENVIRONMENTALFACTORS ON COMPRESSION INDEX From Table 2, it can be observed that the correlation coefficient is good for all modelsexcept for the models relating Cc with environmental factors (mc or γd or mc & γd) alone (i.e.models E4,E5, & E6). The fact that compression index bears good correlation with any ofthe compositional factors (models E1, E2, and E3) and any combination of compositional andenvironmental factors (models E7 to E15) indicate that there is some interaction among thefactors themselves. However, such interactions may or may not be unique for all soils. Themodel which accounts for all the influencing parameters is expected to have a more generalapplicability. Hence there is a need to identify the degree of association between compressionindex and each of the compositional and environmental factors in order to arrive at the bestamongst the remaining 13 models from the view point of general applicability. Regressionmodel E15 correlates dependent variable Cc with all the independent variables namely, wL,mc, γd and IP. Multiple correlation coefficient (R2) of this regression model is 0.991 which ishighest among all the fifteen models developed. The regression coefficients of wL, mc, γdand IP are 0.0027, 0.007, 0.031, and 0.002 respectively. The regression coefficient is highestfor γd followed by mc, wL, and IP in that order. The degree of influence of each of theindependent variables (wL, mc, γd, and IP) on dependant variable Cc cannot be estimated basedon either regression coefficients or multiple correlation coefficients alone (Yevjevich 1972).In other words, neither the multiple correlation coefficients nor the regression coefficients area measure of association between dependant and independent variables. However, the degreeof influence of each of the variables on the dependant variable can be found statistically byestimating partial correlation coefficient ( r1−i ). The partial correlation coefficients measurethe association of each independent variable with the dependent one, after the influence ofcertain related variables has been accounted for (Chandra Sekhar et.al. 2005, Yevjevich1972). The influence of the parameters considered are found out by keeping aside only one ofthese parameters at a time and finding the multiple correlation coefficient, thereby partialcorrelation coefficient. Estimation of partial correlation coefficient ( r1−i ) involves thedetermination of: (a) The multiple correlation coefficient R12 between dependant variable Cc and all theindependent variables wL, mc, γd and IP. Multiple correlation coefficients R 12− i between dependant variable Cc and all theindependent variables except the chosen independent variable xi (choosing one among wL,mc, γd and IP at a time for xi) whose association with the dependant variable is to be assessed.The variable xi is referred as influencing parameter. The partial correlation coefficient r1-i is determined by r1−i = (1 – ((1 – R12 ) / (1 – R12−i ))) … (2) The partial correlation coefficients estimated using the above equation choosing wL, mc,γd and IP as influencing parameters in that order are given in Table 3. The partial correlationcoefficients are 0.809, 0.554, 0.725 and 0.632, respectively. Since all the partial correlationcoefficients are significant, it may be concluded that all the four parameters have significantinfluence on compression index. It is also supported by the fact that the standard deviation ofresiduals is low for all the models. However, the partial correlation coefficient is highest and 396
  • 6. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308(Print), ISSN 0976 – 6316(Online) Volume 3, Issue 2, July- December (2012), © IAEMEstandard deviation of residuals is lowest when IP is chosen as the influencing parameter. Inother words, the IP may be expected to have highest influence on Compression Index. Theobservation made by Sridharan and Nagraj (2001) indicates that soils having same wL butdifferent IP have different Cc values, serves as an evidence for this. Further, it may beobserved that environmental factors mc, and γd have more association with Cc than wLindicated by the partial correlation coefficients. Hence the models E11, E12 and E15 areexpected to have more general applicability than other models as they account for either allor most of the influencing parameters in the development of these models. Table 2. Regression Models Developed for Prediction of Compression Index Multiple Standard Model Parameters Correlation Deviation S.No. Regression Model No. Used Coefficient of (R2) Residuals Compositional Factors alone 1. E1 wL 0.934 0.54 (0.046+ (0.003* wL)) 2. E2 IP 0.959 1.14 (0.130+(0.0347* IP)) 3. E3 wL, IP 0.968 0.54 (0.090 + (0.001* wL) + (0.002* IP)) Environmental Factors alone 4. E4 mc 0.11 0.42 (0.168+(.0048* mc)) 5. E5 γd 0.125 0.66 (0.556- (0.016* γd) 6. E6 mc, γd 0.136 1.14 (1.250 - (0.009* mc) - (0.045* γd)) Combined Compositional and Environmental Factors 5. E7 wL, mc 0.945 0.63 (0.070 + (0.003* wL) – (0.002* mc)) 6. E8 wL, γd 0.951 0.55 (-0.087 + (0.003* wL) + (0.007* γd))) 9. E9 mc, IP 0.971 0.65 (0.160 - (0.002* mc) + (0.004* IP)) 10. E10 γd, IP 0.973 0.74 (0.014+ (0.006* γd)+ (0.0001* IP)) 11. E11 wL, mc, γd 0.968 0.17 (-1.020 + (0.003* wL) + (0.012* mc) + (0.040* γd)) 12. E12 mc, γd, IP 0.974 0.42 (-0.200 + (0.003* mc) + (0.010* γd) + (0.003* IP)) 13. E13 wL, mc, IP 0.981 0.79 (1.270 - (0.001* wL) - (0.002* mc) + (0.002* IP)) 14. E14 wL, γd, IP 0.985 0.79 (-0.038 + (0.001* wL) + (0.007* γd) - (0.002* IP)) 15. E15 wL, mc, γd, IP 0.991 0.19 (-0.629 + (0.0027* wL) + (0.007* mc) + (0.031* γd ) + (0.002* IP)) 397
  • 7. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308(Print), ISSN 0976 – 6316(Online) Volume 3, Issue 2, July- December (2012), © IAEME Table 3. Partial Correlation Coefficients for Different Influencing Parameters Influenci Multiple Partial Std ng Model No. Correlation correlation Deviation of Paramete coefficient coefficient Residuals r E15 - 0.991 - 0.190 E11 IP 0.968 0.809 0.174 E12 wL 0.974 0.554 0.417 E13 γd 0.981 0.725 0.791 E14 mc 0.985 0.632 0.7905. VERFICATION WITH THE REPORTED DATA The statistical analysis of the test results presented in this investigation reveal that theCompression Index is significantly influenced by the parameters IP, γd, mc, and wL in thatorder. Hence regression models E15, E11 and E12 are expected to have more generalapplicability than the other models. In order to verify the same the test data reported byOswald (1980) is used. Oswald (1980) reported about 100 soils consolidation test data,obtained from United States Army Corps of Engineers (USACE) records covering the officesthroughout the Continental United States. Amongst them only seventy one soils test datawere used for verification purpose, as either liquid limit or in-situ void ratio was not reportedfor remaining soils. The details of these seventy one soils test data are summarized in Table4. The compression index of all the seventy one soils test data is predicted using theregression models E1 to E3 and E7 to E15. The observed Cc values are plotted against Ccvalues for all twelve models and the typical plots are shown in Figs 2 to 5. The solid line inthe plots is the line of equality. Careful observation of these plots indicate that thepredictability of 3 models namely E11, E12 and M15 appear to be fair to good since most ofthe points are falling close to the line of equality. All other models are found to either underpredicting or over predicting the Compression Index. This indicates that environmentalfactors mc, and γd have more association with Cc and the models E11, E12, and E15 whichinvolve both the environmental factors apart from compositional factors have more generalapplicability. 398
  • 8. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308(Print), ISSN 0976 – 6316(Online) Volume 3, Issue 2, July- December (2012), © IAEME Table 4. Data Base Used for Verifying the Compression Index Models Developed S.No WP WL γd mc % IP Cc . % % kN/m3 1 31.00 87.00 32.70 13.86 56.0 0.13 2 26.00 51.00 26.80 14.80 0 25.0 0.31 3 23.00 92.00 45.60 11.93 0 69.0 0.39 4 28.00 55.00 30.30 14.32 0 27.0 0.14 5 30.00 65.00 28.70 14.27 0 35.0 0.09 6 27.00 60.00 41.70 12.54 0 33.0 0.34 7 28.00 81.00 44.00 12.34 0 53.0 0.37 8 24.00 55.00 37.30 13.33 0 31.0 0.21 9 27.00 83.00 48.30 11.82 0 56.0 0.38 10 31.00 84.00 45.60 11.91 0 53.0 0.45 11 22.00 67.00 35.20 13.94 0 45.0 0.26 12 25.00 64.00 34.70 13.85 0 39.0 0.34 13 24.00 57.00 40.00 12.76 0 33.0 0.29 14 37.00 92.00 30.90 13.96 0 55.0 0.27 15 25.00 80.00 26.90 14.57 0 55.0 0.22 16 22.00 54.00 21.60 16.63 0 32.0 0.09 17 23.00 85.00 38.70 13.12 0 62.0 0.18 18 22.00 53.00 26.10 15.45 0 31.0 0.20 19 28.00 52.00 51.80 11.00 0 24.0 0.46 20 27.00 91.00 39.70 12.27 0 64.0 0.44 21 24.00 77.00 39.30 12.91 0 53.0 0.30 22 27.00 60.00 44.30 11.89 0 33.0 0.22 23 22.00 58.00 28.50 14.79 0 36.0 0.17 24 24.00 69.00 45.60 11.32 0 45.0 0.36 25 17.00 38.00 21.00 16.77 0 21.0 0.14 0 25.0 26 15.00 40.00 23.20 16.09 0.18 27 25.00 45.00 17.60 17.40 0 20.0 0.08 28 20.00 47.00 31.00 14.27 0 27.0 0.27 29 20.00 45.00 40.50 12.86 0 25.0 0.26 30 18.00 35.00 26.10 15.96 0 17.0 0.07 31 17.00 38.00 22.70 16.61 0 21.0 0.12 32 19.00 45.00 34.40 14.03 0 26.0 0.23 33 19.00 45.00 34.40 13.72 0 26.0 0.23 34 18.00 47.00 26.40 15.58 0 29.0 0.17 35 18.00 42.00 22.20 16.70 0 24.0 0.11 36 18.00 39.00 25.50 15.85 0 21.0 0.10 0 399
  • 9. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308(Print), ISSN 0976 – 6316(Online) Volume 3, Issue 2, July- December (2012), © IAEME γd S.No. WP WL mc kN/m3 IP Cc % % % 37 17.00 38.00 22.70 16.63 21.0 0.12 38 16.00 48.00 24.60 15.77 32.0 0.22 39 22.00 45.00 20.30 16.71 23.0 0.12 40 18.00 46.00 32.00 14.32 28.0 0.26 41 16.00 40.00 25.20 16.06 24.0 0.19 42 22.00 37.00 13.50 19.17 15.0 0.14 43 23.00 36.00 12.40 18.57 13.0 0.14 44 23.00 36.00 14.60 18.73 0 13.0 0.09 45 20.00 37.00 17.50 18.28 0 17.0 0.11 46 22.00 41.00 19.30 17.79 0 19.0 0.11 47 20.00 39.00 21.80 16.60 0 19.0 0.15 48 22.00 43.00 22.20 16.94 0 21.0 0.14 49 21.00 39.00 19.20 17.13 0 18.0 0.13 50 21.00 35.00 16.80 17.91 0 14.0 0.13 51 14.00 33.00 19.20 15.40 0 19.0 0.23 52 16.00 33.00 16.90 16.57 0 17.0 0.18 53 14.00 27.00 20.70 16.09 0 13.0 0.17 54 18.00 34.00 20.80 15.61 0 16.0 0.25 55 18.00 34.00 21.20 15.61 0 16.0 0.19 56 12.00 24.00 20.70 16.14 0 12.0 0.17 57 18.00 32.00 26.50 14.90 0 14.0 0.18 58 20.00 30.00 20.00 15.21 0 10.0 0.08 59 18.00 27.00 13.60 17.10 0 9.00 0.11 60 45.00 112.00 88.10 7.34 67.0 0.87 61 43.00 120.00 101.0 6.69 0 77.0 1.07 62 45.00 122.00 0 108.5 6.85 0 77.0 0.90 63 45.00 130.00 0 111.5 6.40 0 85.0 1.00 64 38.00 96.00 0 65.80 9.35 0 58.0 0.50 65 46.00 104.00 93.60 7.29 0 58.0 0.99 66 70.00 164.00 132.7 5.49 0 94.0 1.43 67 44.00 124.00 0 101.4 7.09 0 80.0 1.02 68 43.00 109.00 0 103.9 6.90 0 66.0 0.82 69 69.00 166.00 0 129.3 5.70 0 97.0 1.42 70 42.00 121.00 0 109.4 6.51 0 79.0 1.13 71 16.00 29.00 0 13.40 17.46 0 13.0 0.14 0 400
  • 10. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308(Print), ISSN 0976 – 6316(Online) Volume 3, Issue 2, July- December (2012), © IAEME 2.00 2.00 1.50 1.50 (Cc)predicted (Cc)Predicted 1.00 1.00 0.50 0.50 0.00 0.00 0.50 1.00 1.50 2.00 0.00 0.00 0.50 1.00 1.50 2.00 (C bserved c)O (C bserved c)O Fig 2 Predicted Vs Observed Cc Fig 3 Predicted Vs Observed Cc (Model E11) (Model E12) 2.00 2.00 1.50 (Cc)Predicted 1.50 (C c )P r ed ic te d 1.00 1.00 0.50 0.50 0.00 0.00 0.00 0.50 1.00 1.50 2.00 0.00 0.50 1.00 1.50 2.00 (C bserved c)O (Cc)Observed Fig 4 Predicted Vs Observed Cc Fig 5 Predicted Vs Observed Cc (Model E13) (Model E15) 401
  • 11. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308(Print), ISSN 0976 – 6316(Online) Volume 3, Issue 2, July- December (2012), © IAEME6. CONCLUSIONS Based on One-Dimensional Consolidation tests on fifteen different soils, fifteenregression models were developed relating Compression Index with each of thecompositional factors (Liquid Limit and Plasticity Index) and environmental factors (DryDensity and Initial Moisture Content) alone as well as with all the possible combinations ofthese parameters. Compression Index is found to bear good correlation with any of thecompositional factors and any combination of compositional and environmental factors. Thedegree of association between compression index and each of the compositional andenvironmental factors is assessed statistically by evaluating the partial correlationcoefficients. Statistical evaluation revealed that all the four parameters namely Liquid Limitand Plasticity Index among the compositional factors and Dry Density and Initial MoistureContent among the environmental factors are found to have significant influence onprediction of Compression Index. Hence the model developed using all the four influencingparameters is expected to have more general applicability than any other model which isconfirmed by verification with the others data . The models developed using atleast onecompositional factor and both the environmental factors were also found to be fair to good.7. REFERENCESJournal Papers 1. AMITHNATH and DEDALAL, S.S. (2004). The role of plasticity index in predicting Compression Index behaviour of clays. Electronic Journal of Geotechnical Engineering, http://www. ejge.com/2004/Per0466/Ppr0466.htm 2. CHANDRASEKHAR, M., MALLIKARJUNA, P. and PRADIPKUMAR, G.N. (2005). Empirical modeling and correlation analysis of evapotranspiration: A case study. ISH Journal of Hydraulic Engineering, Vol. 11, No.2, pp. 1-17. 3. JIAN- HUA YIN (1999). Properties and Behaviour of Hong Kong Marine Deposits with Different Clay Contents. Canadian Geotechnical Journal, Vol 36, pp. 1085 - 1095. 4. KOPPULA, S. D. (1981). Statistical Estimation of Compression Index. ASTM Geotechnical Testing Journal, Vol 4, No.2, pp 68 -73. 5. NISHIDA, Y. (1956). A Brief Note on the Compression Index of Soil. Journal of Soil Mechanics and. Foundation Division, American Society of Civil Engineers, Vol 82, No.3, pp1-14. 6. OSWALD, R. H. (1980). Universal Compression Index Equation. Journal of Geotechnical. Engineering Division: American Society of Civil Engineers, Vol 106, pp.1179-1200. 7. SKEMPTON, A. W. (1944). Notes on the Compressibility of Clays. Quarterly Journal of Geotechnical Society. London, Vol 100, pp.119-135. 8. SRIDHARAN, A. and NAGARAJ, H.B. (2001). Compressibility behaviour of remoulded fine-grained soils and correlation with index properties. Canadian Geotechnical Engineering Journal, No. 38, pp. 1139-1154. 9. N.Ganesan, Bharati Raj, A.P.Shashikala and Nandini S.Nair, “Effect Of Steel Fibres On The Strength And Behaviour Of Self Compacting Rubberised Concrete” International Journal of Civil Engineering & Technology (IJCIET), Volume3, Issue2, 2012, pp. 94 - 107, Published by IAEME 402
  • 12. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308(Print), ISSN 0976 – 6316(Online) Volume 3, Issue 2, July- December (2012), © IAEME 10. S.R.Debbarma and S.Saha, “An Experimental Study On Growth Of Time-Dependent Strain In Shape Memory Alloy Reinforced Concrete Beams And Slabs” International Journal of Civil Engineering & Technology (IJCIET), Volume3, Issue2, 2012, pp. 108 - 122, Published by IAEME 11. Sadam Hade Hussein, Kamal Nasharuddin Bin Mustapha, Zakaria Che Muda, Salmia Budde, “Modeling Of Ultimate Load For Lightweight Palm Oil Clinker Reinforced Concrete Beams With Web Openings Using Response Surface Methodology” International Journal of Civil Engineering & Technology (IJCIET), Volume3, Issue1, 2012, pp. 33-44, Published by IAEME 12. Sadam H. Hussein, Kamal Nasharuddin Bin Mustapha, and Zakaria Che Muda, “Modeling Of First Crack For Lightweight Palm Oil Clinker Reinforced Concrete Beams With Web Openings By Response Surface Methodology” International Journal of Civil Engineering & Technology (IJCIET), Volume2, Issue2, 2011, pp. 13 - 24, Published by IAEME 13. A.S Jeyabharathy, Dr.S.Robert Ravi, and Dr.G.Prince Arulraj “Finite Element Modeling Of Reinforced Concrete Beam Column Joints Retrofitted With Gfrp Wrapping” International Journal of Civil Engineering & Technology (IJCIET), Volume2, Issue1, 2011, pp. 35-39, Published by IAEME Books: 14. BOWLES, J. W. (1979). Physical and Geotechnical Properties of Soils. New York: McGraw Hill. 15. HOUGH, B. K. (1957). Basic Soil Engineering. New York: Ronald. 16. IS: 2132 (1986) (Reaffirmed 1997). Code of Practice for Thin-Walled Tube Sampling of Soils, New Delhi: Bureau of Indian Standards. 17. SP: 36(Part I) (1987)). Compendium of Indian Standards on Soils for Civil Engineering Purposes, New Delhi: Bureau of Indian Standards. 18. MITCHELL, J.K. (1993). Fundamentals of Soil Behavior. New York: John Wiley and Sons. 19. MONTGOMERY, D.C. and RUNGER, G.C. (1999). Applied Statistics and Probability for Engineers. 2nd Edition, pp 483-560, New York: John Wiley and Sons. 20. TERZAGHI, K. and PECK. R. B. (1967). Soil Mechanics in Engineering Practice. New York: John Wiley and Sons. 21. YEVJEVICH, V. (1972). Probability and Statistics in Hydrology. Colarado: Water Resources Publications. 403

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