Evaluation of compaction parameters of fine-grained soils using standard and modified efforts
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Abstract
Compaction characteristics of the soil have the great importance for practically achieving the desired strength, permeability and compressibility of soil during the construction. Standard compaction test (SCT) and modified compaction test (MCT) are two very famous laboratory test methods to determine the compaction characteristics of soils worldwide. Modest efforts have been made in the past to correlate the compaction parameters drawn from these two tests with each other. In the present study, authors are established the models to predict the modified compaction parameters (γ_{dmax(m)} and w_{opt(m)}) by using standard compaction parameters (γ_{dmax(s)} and w_{opt(s)}) or vice versa for the fine-grained soils. Such models can extricate from performing additional tedious and laborious compaction tests. Moreover, the effect of plasticity on the compaction parameters obtained using standard and modified effort is also discussed. Total 156 disturbed fine-grained soil samples were collected from different areas of Pakistan. The index properties tests and laboratory compaction tests were performed using these soil samples. On the basis of index properties tests, these soil samples were classified into different sub-groups of fine-grained soil as per the Unified Soil Classification System. Relationships between the plasticity index (I_{P}) and compaction parameters of both MCT and SCT were also accomplished. Out of 156 soil samples, test results of 126 samples are used to develop the correlations and test results data of 30 samples was used to validate the developed correlations. The percentage error in the correlation between γ_{dmax(m)} and γ_{dmax(s)} is observed to be only ± 0.4% and for the correlation between w_{opt(m)} and w_{opt(s)} the percentage error is observed to be ± 2.7%.
Keywords
Compaction Fine-grained soil Maximum dry unit weight Optimum water content CorrelationIntroduction
Densification is a tool to improve the mechanical characteristics of soil for the various civil engineering projects. In the field, compaction is the best rapid method to improve the unit weight, strength, permeability and compressibility of soil during the construction by removing the air voids. Densification of soil through compaction depends upon the two factors, such as moisture content and compaction effort. A number of compaction tests are established to determine the unit weight of soil in the field and laboratory. In the laboratory, modified compaction test (MCT) and standard compaction test (SCT) are two major tests which are used to find out the compaction parameters i.e. maximum dry unit weight (γ_{dmax}) and optimum water content (w_{opt}). Standard compaction parameters are used for the light-weight infrastructures, and modified compaction parameters are used for the heavy-weight infrastructures. The compaction parameters obtained from different tests have a great significance for the ground improvement and are also used to satisfy the relative compaction requirements as per project specifications. However, the laboratory compaction tests require considerable time and effort. Therefore, to minimize the effort and to save the time, many researchers have proposed the regression models to predict the compaction parameters based on the physical properties of soils. Among them, these studies [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] are the most significant. The maximum dry density and optimum water content obtained depend on several soil parameters such as (a) the percent of fines, (b) specific gravity of soil solids (G_{s}), (c) amount of fine-grained soils, (d) liquid limit and plasticity index, (e) grain size distribution and shape factor of grains in the case of granular soils, and (f) compaction effort imported to the soil [17]. Blotz et al. [1] proposed a model to estimate the maximum dry unit weight (γ_{dmax}) and optimum water content (w_{opt}) of fine-grained soils (FGS) at any compaction effort (CE) based on the liquid limit (w_{L}) of the soils. Gurtug and Sridharan [2], Sridharan and Nagaraj [4] and Nagaraj et al. [18] discussed the dependence of standard compaction parameters (w_{opt(s)} and γ_{dmax(s)}) on the plastic limit and also discussed the relationship among them. Omar at el. [17] worked on the sandy soils of UAE and tested the three hundred and fifty sandy soils in the laboratory. The models for compaction parameters proposed by Omar at el. [17] depended upon the three independent parameters such as liquid limit, specific gravity (G_{s}) and retention on sieve no. 04. However, their proposed model was only applicable for the modified compaction parameters (w_{opt(m)} and γ_{dmax(m)}). Gurtug and Sridharan [3] proposed a model for the approximation of compaction parameters of cohesive soils with respect to compaction effort and plastic limit. Matteo et al. [6] developed a model to predict the compaction parameters of the modified compaction test by using the three independent variables liquid limit, plasticity index and specific gravity. Noor et al. [8] also established a model to predict the standard compaction characteristics of fine-grained soils with three variables such as plastic limit, plasticity index and specific gravity. Mujtaba et al. [9] collected 110 samples of sandy soils and developed the correlations of compaction parameters with uniformity coefficient (C_{u}) and compaction effort and validated these correlations by using the results of 40 similar samples. Moreover, modest efforts have also been made to apprehend the effect of plasticity on compaction parameters of the fine grained soils. Laskar and Kumar [19] discussed the effect of plasticity index on the optimum water content. Plasticity index and optimum water content were found to be directly proportional to each other [19]. In addition, further detailed discussions are required to briefly apprehend the effect of the plasticity of the fine-grained soil on compaction parameters based on the variety of fine-grained soils.
The values of α and β for poorly graded sand (SP) were proposed to be 1.067 and 0.800, for poorly graded sand (SP-SM) with silt 1.072 and 0.804, for silty sand (SM) 1.062 and 1.054 and for well graded sand (SW/SW-SM) 0.785 and 0.787 respectively.
However, these correlations were based on a small number of soil samples and only medium plastic fine grained soils were tested in this study. Moreover, not enough efforts have been made to correlate the standard compaction parameters (γ_{dmax(s)} and w_{opt(s)}) with the modified compaction parameters (γ_{dmax(m)} and w_{opt(m)}) for a variety of fine-grained soils. Whereas, the parameters obtained from both compaction tests are most widely used for the desirable densification of soil in the field. These tests are tedious and laborious, especially in the developing countries, where automatic types of compaction equipment are commonly unavailable at construction sites and in the geotechnical laboratories. Hence, for the quick prediction of compaction parameters based on one performed test, predictive models are required. Moreover, it is also very important to apprehend the effect of the plasticity of fine-grained soil on the compaction parameters to have a quick idea of compaction parameters based on the plasticity of fine-grained soils. Therefore, the aims of the present study are to develop the relationship between modified compaction parameters and standard compaction parameters based on the variety of fine-grained soils and also to examine the effect of plasticity index on compaction parameters.
Materials and experimental program
Summary of index properties and compaction parameters of 156 samples
Soil classification (ASTM D-2487 [24]) | No. of samples | Grain size distribution (ASTM D-698 [21]) | Atterberg’s limit (ASTM D-4318 [22]) | Sp. Gravity (ASTM D-854 [23]) | Standard compaction (ASTM D-698 [25]) | Modified compaction (ASTM D-1557 [26]) | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
USCS | Gravel (%) | Sand (%) | Silt (%) | (%) | w_{L} (%) | I_{P} (%) | G _{ s} | w_{opt(s)} (%) | γ_{dmax(s)} (kN/m^{3}) | w_{opt(m)} (%) | γ_{dmax(m)} (kN/m^{3}) | |
CL | 80 | 0–24 | 0–44 | 47–80 | 15–45 | 30–48 | 7.7–26.5 | 2.67–2.75 | 11.0–21.8 | 14.3–18.8 | 9–16.6 | 16.6–20.4 |
ML | 33 | 0–12 | 3–46.1 | 58–90 | 0–17 | 15–29 | N.P–5 | 2.55–2.76 | 10.5–19.9 | 15.9–19.3 | 8.4–13.5 | 17.8–20 |
CL-ML | 26 | 0–18 | 4.0–50 | 53–83 | 9.5–20 | 19–28 | 4.0–7 | 2.60–2.78 | 10–18.9 | 16.3–19.5 | 8.4–13 | 18–20.4 |
CH | 17 | 0 | 0–6 | 42–56 | 42–57 | 52–78 | 27.5–60 | 2.67–2.74 | 14.5–25 | 13.5–16.7 | 12.8–16 | 16–18.3 |
Overall range | 156 | 0–24 | 0–50 | 42–90 | 0–57 | 15–78 | N.P–60 | 2.55–2.78 | 10.0–26 | 13.5–19.7 | 8.4–16.6 | 16–20.4 |
Test results
Test results analysis and discussion
Effect of plasticity index on compaction parameters
Effect of plasticity index on optimum water content (w_{opt}) can be observed in Fig. 6. Figure 6 is also divided into the different regions according to soil groups. The curves were drawn between the average values of I_{P} and average values of w_{opt(m)} and w_{opt(s)}. The values of w_{opt(m)} and w_{opt(s)} increased with increasing the plasticity index. The values of w_{opt(m)} and w_{opt(s)} were 10.8% and 14% respectively at 2.3% of I_{P}-value and the values of w_{opt(m)} and w_{opt(s)} were 13.7% and 19.9% respectively at 41% of I_{P}-value, which showed that w_{opt(m)} and w_{opt(s)} values increased 21.2% and 29.6% respectively with the increment of 38.7% in the plasticity index. Based on the results of compaction tests, it can also be observed that w_{opt} of standard compaction test was more than the w_{opt} of modified compaction test and the difference between them increased with the increment of plasticity index. The value of w_{opt(m)} was 22.8% less than the value of w_{opt(s)} in the region of ML soils where the average value of plasticity was 2.3% and the difference between the w_{opt(s)} and w_{opt(m)} was 27% and 31.2% in the regions of CL and CH soils respectively. The w_{opt(m)} value was 22.8% to 31.2% less than w_{opt(s)} value with the increment in the value of I_{P} from 2.3% to 41%.
It can be inferred from the above analysis that plasticity of the soils has a significant impact on the compaction characteristics. Plasticity index increases with the increment of clay mineral which causes the enhancement of specific surface area and interaction between soil grains. To break this interaction and move soil grains relative to each other, more water is required, which tends to cause increase in the w_{opt} of soil. Clay minerals form a gel due to the increase in water content of the soil that is called double diffused layer, which causes the enlargement of the size of voids between the soil particles. This phenomenon tends to decrease the dry density of soils.
Development of correlations between MCT and SCT parameters
Correlations of modified compaction parameters (w_{opt(m)} and γ_{dmax(m)}) with standard compaction parameters (w_{opt(s)} and γ_{dmax(s)})
FGS type | Model | R^{2} | Model | R^{2} |
---|---|---|---|---|
ML | w_{opt(m)} = 0.4447w_{opt(s) }+ 4.49 | 0.78 | γ_{dmax(m)} = 0.5951γ_{dmax(s) }+ 8.56 | 0.78 |
CL-ML | w_{opt(m)} = 0.4972w_{opt(s) }+ 3.67 | 0.94 | γ_{dmax(m)} = 0.7363γ_{dmax(s)} + 6.04 | 0.98 |
CL | w_{opt(m)} = 0.488w_{opt(s) }+ 3.95 | 0.91 | γ_{dmax(m)} = 0.7335γ_{dmax(s) }+ 6.124 | 0.91 |
CH | w_{opt(m)} = 0.4724w_{opt(s) }+ 4.28 | 0.93 | γ_{dmax(m)} = 0.7233_{γdmax(s) }+ 6.25 | 0.99 |
Combined model of FGS | w_{opt(m)} = 0.4901w_{opt(s) }+ 3.87 | 0.94 | γ_{dmax(m)} = 0.716γ_{dmax(s) }+ 6.36 | 0.94 |
A good and reliable correlation must have a high value of correlation coefficient (R^{2}). The correlation coefficient (R^{2}) is an index of the goodness of fit between the predictive correlation and sample data used to develop that correlation. It provides a quantitative index of association between measured and predicted values and indicate the accuracy for the future predictions [9, 27]. For γ_{dmax(m)} models, the value of R^{2} varied from 0.78 to 0.99 and the R^{2} value was 0.78 to 0.94 for w_{opt(m)} models as shown in Figs. 8 and 9.
Validation of correlations
Percentage error in Humdani [20] model is about ± 0.6% for γ_{dmax(m)} and 6 out of 30 points fell outside the standardized envelope as shown in Fig. 12. The error in the developed combined model of γ_{dmax(m)} was ± 0.4% which is less than the Humdani [20] model and only one data point is out of the ± 0.4% envelope. For w_{opt(m)}, the percentage error in Humdani [20] model is around ± 4.9% and out of 30 data points, 22 points are failed to remain in the standardized envelope of ± 2.6%. On the other hand, the percentage error in the combined model of w_{opt(m)} is ± 2.7%, which is almost equal to percentage error of standardized envelope and only one point fell out of the error envelope (Fig. 13). Humdani [20] model is found to be less reliable for the locally available fine-grained soils especially for w_{opt(m)} as compared with the developed models of the present study. Such validity rendered that model reliability can be enhanced by widening the range of sample characteristics.
Conclusions
- 1
Based on the results of the compaction tests using standard and modified efforts, the γ_{dmax(m)} is almost 7% to 11.5% more than γ_{dmax(s)} and the w_{opt(s)} is 22.8% to 31.8% more than w_{opt(m)}, for the fine-grained soil.
- 2
Quantitatively, with an increase in the plasticity index (I_{P}) of fine-grained soils from 2.3% to 41%, the γ_{dmax(m)} and γ_{dmax(s)} are almost decreased from 20.4 kN/m^{3} to 16 kN/m^{3} and 19.5 kN/m^{3} to 13.5 kN/m^{3} respectively. Similarly, w_{opt(m)} and w_{opt(s)} are almost increased from 8.4% to 16.6% and 10% to 25%, respectively.
- 3
A correlation is established to predict the modified maximum dry unit weight (γ_{dmax(m)}) by using standard maximum dry unit weight (γ_{dmax(s)}) as the independent variable for the fine-grained soil: γ_{dmax(m) }= 0.716γ_{dmax(s) }+ 6.36. This correlation has a reliable value of correlation coefficient (R^{2}) and has percentage error of around ± 0.4%, which indicate the good accuracy of the model.
- 4
A correlation is also developed to predict the modified optimum water content (w_{opt(m)}) by using standard optimum water content (w_{opt(s)}) as the independent variable for fine-grained soils: w_{opt(m) }= 0.4901w_{opt(s) }+ 3.87. This correlation has a reliable value of R^{2} and has only ± 2.7% error in the predictive values.
- 5
The proposed correlation models are valid for fine-grained soils, having γ_{dmax(s)} and w_{opt(s)} up to 19.5 kN/m^{3} and 25% respectively.
Notes
Authors’ contributions
UK designed and executed the laboratory test plan, developed the empirical models, and drafted the manuscript. ZR analysed the test results, checked the validity of empirical models and presented the discussion along with illustrative examples in the manuscript. Both authors read and approved the final manuscript.
Acknowledgements
Authors are grateful to the Civil Engineering Department, University of Engineering and Technology, Lahore, Pakistan, for providing the technical support in Pakistan for this research. Authors want to extend their approbation to Prof. Khalid Farooq and Dr. H.M. Shehzad for their continuous support and encouragement.
Competing interests
The authors declare that they have no competing interests.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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