Relation Between C.B.R Values And Geotechnical Properties Of Expansive soil

IJSRD - International Journal for Scientific Research & Development| Vol. 4, Issue 1, 2016 | ISSN (online): 2321-0613

Relation Between C.B.R Values and Geotechnical Properties of Expansive Soil 1,2,3

Guruprasad Jadhav1 Vishwajeet Biradar2 Prof. Md Khaja Moniuddin3 bheemanna Khandre Institute of Technology Bhalki- 585328 Dist: Bidar, Karnataka

Abstractâ&#x20AC;&#x201D; Sub grade strength is mostly affected by thickness of pavement, in Highway design. California Bearing Ratio (CBR) and Unconfined Compressive Strength (UCS) are one of the method to determine the sub grade strength. CBR and UCS test are laborious and time consuming, and this method is proposed as same as CBR and UCS value with LL, PL, SL, PI, OMC and MDD. In the present study, different soils samples (having 20<LL<70) were collected from different locations. Other laboratory tests such as Consistency limit, Specific Gravity, Grain size analysis, CBR and compaction were done on the samples.

Many of the linear relationships between index properties and CBR and UCS of the samples were observed using simple and multiple linear regression analysis and also predictive equation estimating CBR from the experimental index values were developed.

Key words: UCS, CBR, MDD

Fig. 4.1 (a) Comparisons between CBR and L.L An attempt is made to correlate between liquid limit and CBR values of experimental results of soil samples collected for investigation. From Fig 4.1(a) shows that as liquid limit increases CBR value decreases Figure 4.1 (a) Comparisons between UCS and L.L

I. INTRODUCTION A. Background All civil engineering works such as the construction of highway, building structure, dam and other structure have a strong relationship with soil. All those structures need a strong layer of soil to make sure the structure are strong and stable. The weakness and failure of soil may capable make the structure which builds above of it become weak and collapse or fail. Therefore, the proper analysis of soil is necessary to ensure that these structures remain safe and free endue settling and collapse. In order to obtain the knowledge, soil sample must be collected from a required site and tested it to evaluate the engineering properties of soil.

Soil conditions vary from one location to another location; hence, virtually no construction site present soil conditions exactly like other site. In general, soils are the mixtures of particles of many parent materials, sizes, shapes.

Hence it is difficult to predict the behaviour of soil. As a result, soil conditions at every site must be thoroughly investigated for proper design.

B. Objectives of the Study 1) To understand relationship between CBR values and physical properties of soil (C L, shear strength parameters) samples collected from different location.

2) The main objective of the study is to carry out regression analysis from laboratory experimental results.

II. RESULTS AND DISCUSSION A. Simple Linear Regression Analysis (SLRA) SLRA has been carried out to develop the correlation between individual basic soil property and soaked CBR value. Statistical parameters of soaked CBR value predicted by various SLRA models are presented below.

Properties

Case -1

Case -2

G

2.71

2.67

(L.L)

66.1

52.3

(P.L)

35.4

32.1

Shrinkage limit

8.43

9.12

Plasticity Index

30.07

20.2

Shrinkage Index

57.6

51.58

I.S Classification

CH

CH

Gravel %

0.66

9.01

Sand %

24.65

23.03

Silt and Clay %

74.69

67.98

Consistency test

Grain Size Analysis

Compaction Characteristics M.D.D

1.55

1.69

O.M.C (%)

22.3

19

Engineering Properties

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Relation Between C.B.R Values and Geotechnical Properties of Expansive Soil (IJSRD/Vol. 4/Issue 1/2016/185)

California Bearing Ratio

1.32

1.51

Unconfined Compressive Strength

1.65

1.88

Table 4.3.1: Correlation between CBR and Liquid limit

Fig. 4.1 (c): Comparisons between experimental and predicted CBR values Figure 4.1(c) shows comparison between experimental and predicted (CBR value obtained from SLRA) CBR value.

1) Correlation graph between CBR and PL

predicted CBR values obtained from regression equation (y = -0.0578(PL) + 4.3159) shows greater variation with the experimental value.

2) Relationship between CBR and PI

Fig. 4.3(a): Comparisons between CBR and P.I The above Figure 4.3(a) represents the correlation between plasticity index and soaked CBR value of soil samples collected for investigation. Sample number 1, 14 and 17 are not considered for this regression analysis because these soils are non-plastic (NP) in nature

Fig. 4.3(b): Comparisons between UCS value and P.I Fig. 4.2(a): Comparisons between CBR and P.L Figure 4.2(a) represents the correlation between soaked CBR value and plastic limit of soil samples collected for investigation.

Fig. 4.2(b): Comparisons between UCS and PL

Fig. 4.2(c): Comparisons between Experimental and Predicted CBR value for P.L Figure 4.2(c) shows the comparison between experimental and predicted CBR value. It represents that

Fig. 4.3(c) Comparisons between Experimental and Predicted CBR Value for PI Figure 4.3(c) shows comparison between experimental and predicted CBR value. It is observed that results obtained from regression analysis are nearer to the laboratory results.

3) Correlation graph between CBR and Maximum dry density

Fig. 4.4(a): Comparisons between CBR value and M.D.D

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Relation Between C.B.R Values and Geotechnical Properties of Expansive Soil (IJSRD/Vol. 4/Issue 1/2016/185)

Figure 4.4(a) shows the correlation between maximum dry density and CBR value. It is observed thatislinearrelationship exists between MDD and CBR value.

Fig. 4.4(b): Comparisons between UCS value and M.D.D

Fig. 4.4(c): Comparisons between Experimental and Predicted CBR Value for MDD Figure 4.4(c) shows the comparison between experimental and predicted CBR value 4.3.5 Correlation graph between CBR and Optimum moisture content

Fig. 4.5(a): Comparisons between CBR value and O.M.C Figure 4.5(a) shows the graph between optimum moisture content and CBR values of experimental results of soil samples collected for investigation

Fig. 4.5(b): Comparisons between UCS value and O.M.C

Fig. 4.5(c): Comparisons between Experimental and Predicted CBR Value for optimum moisture content Figure 4.5(c) represents the comparison between experimental and predicted soaked CBR value. It shows that predicted CBR values are quite close to laboratory values.

4.3.6 Correlation graph between CBR and Shrinkage Index

Fig. 4.6(a): Comparisons between CBR value and SI

Fig. 4.6(b): Comparisons between UCS value and SI

Fig. 4.5(c): Comparisons between Experimental and Predicted CBR Value for optimum moisture content Figure 4.5(c) represents the comparison between experimental and predicted soaked CBR value. It shows that predicted CBR values are quite close to laboratory values.

4.3.6 Correlation graph between CBR and Shrinkage Index

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Relation Between C.B.R Values and Geotechnical Properties of Expansive Soil (IJSRD/Vol. 4/Issue 1/2016/185)

From the Equation-2, on the basis of extensive results for CBR value of soils having wide range of liquid limit and plastic limit, a generalized has been established using different empirical and analytical analysis.

Fig. 4.6(a): Comparisons between CBR value and SI

Fig. 4.8: Correlated shear strength parameters (Bearing Ratio) 3) MLRA between CBR and OMC, maximum dry density.

CBR = -4.8353 – 1.56856 (OMC) + 4.6351 (MDD) Equation-3 (R2= 0.82)

Figure 4.6(b) Comparisons between UCS value and SI

Fig. 4.6(c): Comparisons between Experimental and Predicted CBR Value for SI Figure 4.6(a) shows correlation between CBR value and shrinkage index of soil. The observed coefficient of correlation (R2) was 0.63. It indicates that no significant relation exists between these two parameters

Fig. 4.9 Correlated shear strength parameters (Bearing Ratio) Prediction of CBR from compaction characteristics such as M.D.D and O.M.C content is done by using multiple variable regression analysis is expressed in equation (3).

Where CBR value is considered as dependent and optimum moisture content, maximum dry density is taken as independent variable. 4.4.4. MLRA between CBR and plasticity index, maximum dry density.

CBR= -3.2353-0.06939 (PI) + 2.8 (MDD) - Equation-4 (R2=0.76)

B. MULTIPLE LINEAR REGRESSION ANALYSIS (MLRA) 1) MLRA between CBR and liquid limit, shrinkage limit, shrinkage index CBR = 5.09477 - 0.09323 (LL) + 0.10939 (SL) + 0.022566 (SI) - - - - - - - Equation-1 (R2= 0.66) Fig. 4.10: Correlated shear strength parameters (Bearing Ratio) 4) MLRA between CBR and optimum moisture content and plasticity index.

CBR= 6.5452 – 0.07703(OMC) - 0.10395 (PI) Equation-5 R2=0.75

Fig. 4.7: Correlated shear strength parameters (Bearing Ratio) 2) MLRA between CBR and liquid limit, PI. CBR = 5.813 – 0.007826 (LL) + 0.12097 (PL) Equation-2 (R2=0.72)

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Relation Between C.B.R Values and Geotechnical Properties of Expansive Soil (IJSRD/Vol. 4/Issue 1/2016/185)

Assessment and Management (ISEUSAM). pp. 833847.

[4] Danistan Joseph, C. Vipulanandan. “Correlation between california bearing ratio (cbr) and soil parameters”. Centre for

Fig. 4.11: Correlated shear strength parameters (Bearing Ratio) Figure 4.11 shows comparison between experimental and predicted CBR results using multiple regression analysis by considering CBR as dependent variable and III. CONCLUSION The present study was undertaken to develop regression based models to estimate CBR values for coarse and fine grained soils. A total of 20 soil cases were tested for CBR values. The equations are developed using Microsoft Excel.

The following conclusion can be drawn from the results of the present study: 1) The results of the CBR and index properties tests are interpreted together to infer and to understand the relation between them.

2) Based on experimental results and SLRA, there is no significant relation exists to predict CBR value from liquid limit and plastic limit.

3) Linear relation exists between plasticity index and CBR value with a coefficient of correlation of R2=0.72.

4) It is found that good empirical relations y=4.99MDD5.711 (R2=0.78) and y=-0.2443OMC+7.5264 (R2=0.71) obtained by SLRA to predict CBR value from MDD and OMC. 5) The empirical relation CBR= -4.8353–1.56856(OMC) +4.6351(MDD) (R2=0.82) obtained from multiple linear regression analysis (MLRA) shows good relation to predict CBR value from MDD and OMC. 6) The model developed by SLRA for good correlation can be obtained from the model developed using MLRA by showing R2 value of 0.72.

7) From the correlation analysis it is clear that, large variation can be observed between experimental and predicted CBR value particularly in case of high compressible clays (CH).

REFERENCES [1] Dilip Kumar Talukdar. “A Study of correlation between California Bearing Ratio (CBR) values with other properties of soil”. International Journal of Emerging Technology and Advanced Engineering, volume 4 (2014). pp. 559-562.

[2] Patel.Rashmi S. and Desai.M.D. “CBR Predicted by Index properties for Alluvial soils of South Gujarat”.

Indian Geotechnical Conference (2010). pp. 79-82..

[3] B.C. Chattopadhyay and J. Maity. “Prediction of CBR of different groups of Alluvial soils for design of flexible pavement”. Proceedings of the International Symposium on Engineering under Uncertainty: Safety

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