DEVELOPMENT OF EMPIRICAL CORRELATIONS
BETWEEN CALIFORNIA BEARING RATIO (CBR)
AND SOIL INDEX PROPERTIES.
K.V.S.D. Jayamali
(09/8812)
Degree of Master of Engineering
Department of Civil Engineering
University of Moratuwa
Sri Lanka
September 2013
i
DEVELOPMENT OF EMPIRICAL CORRELATIONS
BETWEEN CALIFORNIA BEARING RATIO (CBR)
AND SOIL INDEX PROPERTIES.
Kevitiyagala Vithanage Sanjeewani Disna Jayamali
(09/8812)
Thesis submitted in partial fulfillment of the requirement for the degree
Master of Engineering in Foundation Engineering and Earth Retaining Systems
Department of Civil Engineering
University of Moratuwa
Sri Lanka
September 2013
ii
DECLARATION
I hereby declare that the work presented in this dissertation is my own research except
as cited in the references. I also declare that this report has not been accepted for any
other University or Institute and no part of this project report has been submitted
earlier or concurrently for same or any other Degree or Diploma, to the best of my
knowledge.
……………………….
K.V.S.D. Jayamali
Admission No :
Date :
Certified By
……………………….
Dr. U.P. Nawagamuwa
(Supervisor)
iii
DEDICATION
This dissertation is dedicated to my loving husband and my parents and family, for
their endless love, support and encouragement. I won’t be here without their love,
patience, bear and understanding throughout my life. My wholehearted thanks go to
all of you giving me strength to achieve my dreams and lightened up my spirit to
finish this study and this thesis.
iv
ACKNOWLEDGEMENT
I would like to take this opportunity to express my sincere appreciation to all people
and organization that had contributed towards the successful completion of this
project.
First and foremost, I would like to extend gratitude to my supervisor, Dr. U.P.
Nawagamuwa, Senior Lecturer, Department of Civil Engineering, University of
Moratuwa, Sri Lanka for his guidance and support throughout this study, and
especially for his confidence on me. Once more I thank him for his able supervision
with spending his precious time and valuable advices in completion of this research
successfully.
Secondly, I wish to extend by sincere thanks to Geotech (Pvt) Ltd where I am
employed, for the cooperation by funding me in successful completion of this project.
My special thanks goes to all the staff of Group Engineering Laboratories (Pvt) Ltd
for their assistant to carry out laboratory experiments, and laboratory manager of
Engineering & Laboratories (Pvt) Ltd who provided relevant information and data for
achieving the research requirements while sharing her knowledge with me.
Last but not least, not to forget the full supports that has been given by my family
during my study.
v
ABSTRACT
The California Bearing Ratio is a penetration test for evaluation of the mechanical
strength of road sub-grades and base-courses. This can be used as a mean of designing
the road pavement required for a particular strength of sub-grade by comparing the
strength of different sub-grade materials.
However; civil engineers always encounter difficulties in obtaining representative
CBR value for design of pavement. Over the years, many correlations had been
proposed by various researchers in which the soil index properties were used to
develop these correlations.
A study was carried out to find correlations between CBR value with soil index
properties those best suit the type of soils in Sri Lanka. Analyses were carried out
based on the published correlations and soil data obtained from several Sri Lankan
project sites. Based on the results, it is observed that the current published correlations
are not in good agreement with Sri Lanka soils. In addition, no typical range could be
found based on the soil index properties.
Mechanical Strength of soil depends not only on the soil type but also on the
observable physical characteristics which significantly influence on a soil’s behavior.
Therefore, a method is proposed for correlating soaked CBR value and compaction
parameters with such index properties, for Sri Lankan soils. This research covers the
entire soil types according to Unified Soil Classification System which are generally
used as sub-grades and base-courses.
Among the several soil index properties, Atterberg Limits and grain size distribution
data are used in this regard as these tests are much more economical and rapid than
Compaction and CBR tests. The correlations are established in the form of an
equation as a function of different soil properties by the method of regression
analysis. Finally, results of the laboratory test are used to compare with the results of
regression equation for the compiled data for the validation of the correlation.
Key Words : California Bearing ratio, Compaction Parameters, Index Properties,
Regression Analysis
vi
TABLE OF CONTENTS
PAGE
DECLARATION II
DEDICATION III
ACKNOWLEDGEMENTS IV
ABSTRACT V
TABLE OF CONTENTS VI
LIST OF TABLES IX
LIST OF FIGURES X
LIST OF APPENDICES XIII
LIST OF SYMBOLS XIV
CHAPTER 1 : INTRODUCTION
1.1 Background 1
1.2 Problem Statement 3
1.3 Aim and Objectives of Study 3
1.4 Scope of Study 4
1.5 Significance of the Study 4
CHAPTER 2 : LITERATURE REVIEW
2.1 Introduction 5
2.2 Index Properties of Soils 5
2.2.1 Particle Size Distribution 5
2.2.2 Atterberg Limits 7
2.3 Soil Classification 9
2.4 Soil Compaction 14
2.5 California Bearing Ratio 15
2.5.1
2.5.2
2.5.3
Applications of California Bearing Ratio
Advantages and Disadvantaged of CBR Test
Alternative to CBR & its Limitations
17
18
19
vii
2.6 Existing Correlations between CBR and Index Tests 20
2.6.1
2.6.2
2.6.3
2.6.4
2.6.5
2.6.6
2.6.7
2.6.8
2.6.9
2.6.10
2.6.11
Black (1962)
de Graft – Johnson and Bhatia (1969)
Agarwal and Ghanekar (1970)
The Highway Agency (1994)
National Cooperative Highway Research Program (2001)
Kin (2006)
Vinod and Cletus (2008)
Roy et al (2009)
Ayodele et al (2009)
Patel and Desai ( 2010)
Breytenbach (2009)
20
22
23
24
25
26
27
28
28
28
28
2.6.12
2.6.13
Singh et al (2011)
Agarawal et al (2011)
33
34
CHAPTER 3 : METHODOLOGY
3.1 Introduction 36
3.2 Data Collection 38
3.2.1 Data Selection and Grouping 38
3.3 Laboratory Test Methods 40
3.4 Data Analysis 41
3.4.1 Regression Analysis 41
CHAPTER 4 : RESULTS AND DISCUSSION
4.1
4.2
4.3
4.4
Introduction
Data Categorization
Data Filtration
Data Analysis
43
43
44
51
4.4.1
4.4.2
Individual Parameter Analysis by Graphical Representations
Evaluation and Modification of Published Correlations
51
62
4.4.3 Development of New Empirical Correlations 92
4.4.4 General Discussion 107
4.4.5 Validation of developed correlations 109
viii
CHAPTER 5 : CONCLUSIONS AND RECOMMENDATIONS
5.1 Conclusion 111
5.2 Recommendations for Future Study 112
REFERENCES 114
ix
LIST OF TABLES TABLE
NO. TITLE PAGE
Table 2.1 :
Table 2.2(a):
Table 2.2(b):
Table 2.3 :
Table 2.4 :
Table 2.5 :
Table 2.6 :
Table 2.7 :
Table 2.8 :
Table 4.1 :
Table 4.2 :
Table 4.3 :
Table 4.4 :
Table 4.5 :
Table 4.6 :
Table 4.7 :
Table 4.8 :
Table 4.9 :
Different methods of Proctor Compaction Test
Typical Values of CBR by Unified Soil Classification System (Semen,2006)
Typical Values of CBR by Unified Soil Classification System(Semen,2006)
Subgrade CBR estimation of British soils compacted at natural moisture
content (The Highway Agency, 1994)
Statistics of Data Used by International Journal of Computer Applications
Correlation Equations Obtained by SRA
Statistical Results of SRA.
Correlation Equations Obtained By MRA
Statistical Results of MRA
Data Filtration
Statistics of fine grained plastic soil data compacted to 100% MDD and
OMC in standard manner
Statistics of coarse grained plastic soil data compacted to 100% MDD and
OMC in standard manner
Statistics of coarse grained non-plastic soil data compacted to 100% MDD
and OMC in standard manner
Statistics of coarse grained plastic soil data compacted to 95% MDD and
OMC in modified manner
Statistics of coarse grained non-plastic soil data compacted to 95% MDD
and OMC in modified manner
Individual relationship between CBR and index properties of fine grained
soils compacted to 100% MDD & OMC in Standard manner
Individual relationship between CBR and index properties of coarse grained
soils compacted to 100% MDD & OMC in Standard manner
Individual relationship between CBR and index properties of coarse grained
soils compacted to 95% MDD & OMC in Modified manner
14
16
17
24
34
35
35
35
35
44
46
47
47
48
48
53
57
61
x
LIST OF FIGURES
FIG NO. TITLE PAGE
Fig.2.1 : Soil Grading Curve 6
Fig 2.2 : Soil Plasticity Stages 7
Fig 2.3: Unified Soil Classification of fine grained soils 11
Fig 2.4: Unified Soil Classification of Coarse grained soils 12
Fig 2.5: Plasticity Chart 13
Fig 2.6: Relationship between CBR and plasticity index at various liquidity index
values (Black ,1962)
21
Fig 2.7: Correction of CBR values for partial saturation (Black ,1962) 21
Fig 2.8( a): Relationship between suitability index and soaked CBR values for Ghanaian
Lateritic Soils (de Graft – Johnson et al ,1969)
22
Fig 2.8(b): Relationship between suitability index and soaked CBR values for Ghanaian
Lateritie-Quartz Gravel (de Graft – Johnson et al ,1979)
23
Fig 2.9: Correlation of CBR, Maximum Dry Density and Optimum Moisture Content
(Kin, 2006)
27
Fig 2.10 : CBR estimation proposed by Stephenson et al (1967) 31
Fig 3.1 : Flow Chart on Overall Research Methodology 37
Fig 3.2 : Hierarchical Chart of Data Grouping 39
Fig 4.1 : Variation of CBR Values compacted to 100% MDD and OMC in a Standard
Manner
45
Fig 4.2 : Variation of CBR Values compacted to 95% MDD and OMC in a Modified
Manner
45
Fig 4.3 : Gradation curve for the sieve analysis 49
Fig 4.4 : Graph for determination of liquid limit test 49
Fig 4.5 : Compaction curve for determination of MDD and OMC 50
Fig 4.6 : Graph showing load vs. penetration for determination of CBR value 50
Fig. 4.7(a): Scattered plots of individual soils parameters Vs. CBR for fine grained plastic
soils compacted to 100% MDD and OMC in standard manner (i)
51
Fig. 4.7(b):
Scattered plots of individual soils parameters Vs. CBR for fine grained plastic
soils compacted to 100% MDD and OMC in standard manner (ii)
52
Fig. 4.8(a): Scattered plots of compaction parameters Vs. CBR for coarse grained soils
compacted to 100% MDD and OMC in standard manner
54
Fig. 4.8(b):
Scattered plots of grading parameters Vs. CBR for coarse grained soils
compacted to 100% MDD and OMC in standard manner
55
xi
Fig. 4.8(c): Scattered plots of key particle sizes Vs. CBR for coarse grained soils
compacted to 100% MDD and OMC in standard manner
56
Fig. 4.8(d) Scattered plots of Atterberg Limits parameters Vs. CBR for coarse grained
soils compacted to 100% MDD and OMC in standard manner
56
Fig. 4.9(a)
Scattered plots of compaction parameters Vs. CBR for coarse grained soils
compacted to 95% MDD and OMC in modified manner
58
Fig. 4.9(b): Scattered plots of grading parameters Vs. CBR for coarse grained soils
compacted to 95% MDD and OMC in modified manner
59
Fig. 4.9(c): Scattered plots of key particle sizes Vs. CBR for coarse grained soils
compacted to 95% MDD and OMC in modified manner
60
Fig. 4.9(d): Scattered plots of Atterberg Limits parameters Vs. CBR for coarse grained
soils compacted to 95% MDD and OMC in modified manner
60
Fig 4.10(a): Comparison of de Graft – Johnson and Bhatia (1969) with Sri Lankan data 63
Fig 4.10(b): Relationship between SI & CBR for local soils 63
Fig 4.10(c): Relationship between MDD/PI & CBR for local sandy soils 64
Fig 4.11(a): Comparison of Agarwal and Ghanekar (1970) with Sri Lankan data 65
Fig 4.11(b): Comparison of Modified Correlation to Agarwal and Ghanekar (1970) 66
Fig 4.12(a): Comparison of NCHRP (2001) with local soil data 67
Fig 4.12(b): Relationship between wPI & CBR for local soils 68
Fig 4.12(c): Comparison of modified relationships between PI, P075 & CBR for local soils 69
Fig 4.13(a): Comparison of Kin(2006) with local soil data 70
Fig 4.13(b): Power relationship between the ratio of CBR/OMC vs. MDD for local soils 70
Fig 4.13(c): Linear relationship between the ratio of CBR/OMC vs. MDD for local soils 71
Fig 4.14(a): Comparison Graph of Vinod and Cletus (2008) with local plastic soils 72
Fig 4.14(b): Relationship between WLM and CBR for local plastic soils 72
Fig 4.14(c): Modified Relationship between WLM and CBR for plastic soils compacted to
95% MDD & OMC in Modified Manner
73
Fig 4.15: Comparison of Roy et el (2009) with local soil data 74
Fig 4.16(a): Comparison of Ayodele et al (2009) for soils compacted to 100% MDD &
OMC in a Standard Manner
76
Fig 4.16(b):
Comparison of Ayodele et al (2009) for soils compacted to 95% MDD &
OMC in a Modified Manner with Sri Lankan data
76
Fig 4.17(a): Comparison of Patel and Desai ( 2010) for local plastic soils 77
Fig 4.17(b): Comparison of modified Patel and Desai (2010) for local plastic soils 78
xii
Fig 4.18(a): Comparison of South African Railways (1970) with Sri Lankan data 79
Fig 4.18(b): Relationship between If and CBR for coarse grained soils compacted to 95%
MDD and OMC using Mod. Effort
80
Fig 4.18(c): Comparison of new regression correlation between CBR and LL, PL and P075 80
Fig 4.19(a): Comparison of Sood et al. (1978) and Dhir et al(1987) with Sri Lankan data 81
Fig 4.19(b): Comparison of modified correlation to Sood et al. (1978) and Dhir et al(1987) 82
Fig 4.20(a):
Comparison of Haupt(1980) for plastic soils compacted to 100% MDD &
OMC in a Standard Manner
83
Fig 4.20(b):
Comparison of Haupt (1980) for plastic soils compacted to 95% MDD &
OMC in a Modified Manner
84
Fig 4.21(a): Comparison of Agarawal et al (2011) Eq. 2.28 with local plastic soils 87
Fig 4.21(b): Comparison of Agarawal et al (2011) Eq. 2.29 with local plastic soils 87
Fig 4.21(c): Comparison of Agarawal et al (2011) Eq. 2.30 with local plastic soils 87
Fig 4.21(d): Comparison of Agarawal et al (2011) Eq. 2.31 with local plastic soils 88
Fig 4.21(e): Comparison of Agarawal et al (2011) Eq. 2.32 with local plastic soils 88
Fig 4.22(a): Comparison of Agarawal et al (2011) Eq.2.33 with local plastic soils 89
Fig 4.22(b): Comparison of Agarawal et al (2011) Eq.2.34 with local plastic soils 89
Fig 4.22(c): Comparison of Agarawal et al (2011) Eq.2.35 with local plastic soils 89
Fig 4.22(d): Comparison of Agarawal et al (2011) Eq.2.36 with local plastic soils 90
Fig 4.22(e): Comparison of Agarawal et al (2011) Eq.2.37 with local plastic soils 90
Fig 4.22(f):
Comparison of modified Agarawal et al (2011) Eq.2.35 for plastic soils
compacted to 95% MDD and OMC in a modified manner
92
Fig 4.23 :
Comparison of developed empirical correlation for non-plastic coarse grained
soils compacted to 100% MDD and OMC in a standard manner
95
Fig 4.24 :
Comparison of developed empirical correlation for non-plastic coarse grained
soils compacted to 95% MDD and OMC in a modified manner
97
Fig 4.25 :
Comparison of developed empirical correlation for plastic fine grained soils
compacted to 100% MDD and OMC in a standard manner
98
Fig 4.26 : Comparison of developed empirical correlation for plastic fine grained soils of
CBR below 20% compacted to 100% MDD and OMC in a standard manner
99
Fig 4.27 : Comparison of developed empirical correlation for plastic coarse grained soils
compacted to 100% MDD and OMC in a standard manner
100
Fig 4.28(a): Comparison of developed empirical correlation for SC soils compacted to
100% MDD and OMC in a standard manner
103
xiii
Fig 4.28(b): Comparison of developed empirical correlation for SC soils with CBR below
25% compacted to 100% MDD and OMC in a standard manner
104
Fig 4.29 : Comparison of developed empirical correlation for plastic coarse grained soils
compacted to 95% MDD and OMC in a modified manner
105
Fig 4.30: Comparison of developed empirical correlation for ‘SC’ and ‘SM-SC’ soils
compacted to 95% MDD and OMC in a modified manner
107
Fig 4.31 : Verification of developed new correlation - Eq.4.39 109
Fig 4.32 : Verification of developed new correlation - Eq.4.47 110
Fig 4.33 : Verification of developed new correlation -Eq.4.46 110
LIST OF APPENDICES
Appendix 1 - Summary of Compiled Data for Model Derivation
Appendix 2 - Summary of Tests Performed for Model Verification
xiv
LIST OF ABBREVIATIONS AND SYMBOLS
CBR - California Bearing Ratio
CBRTOP
- CBR value at top face of soil sample
CBRBOTTOM
- CBR value at bottom face of soil sample
DCP - Dynamic Cone Penetrometer
D60
- Diameter at 60% passing from grain size distribution (mm)
D50
- Diameter at 50% passing from grain size distribution (mm)
D30
- Diameter at 30% passing from grain size distribution (mm)
D10
- Diameter at 10% passing from grain size distribution (mm)
LL - Liquid Limit
PL - Plastic Limit
PI - Plasticity Index
MDD - Maximum Dry Density
OMC - Optimum Moisture Content
SI - Suitability Index
γd max - Maximum Dry Density
γw - Density of Water
f - Fines Content (%)
MC - Moisture Content
SRA - Simple Regression Analysis
MRA - Multiple Regression Analysis
C - Fraction coarser than 0.425 mm sieve
WLM - Modified Liquid Limit, equal to LL(1 - C/100)
A - % retain on 2.4mm sieve
USCS - Unified Soil Classification System
Cu - Coefficient of Uniformity
Cc - Coefficient of Curvature
P 425 - % Passing 0.425mm sieve
R 425 - % retain on 0.425mm BS sieve
P2360 - % Passing 2.4mm sieve
R2360 - % retain on 2.36mm sieve
P075 - % Passing 0.075 mm sieve
ASTM - American Society for Testing and Materials
BS - British Standards
AASHTO - American Association of State Highway and Transportation Officials
ANOVA - Analysis of Variance