Predicting Compaction Characteristics of Fine-Grained Soils in Terms of Atterberg Limits View Full Text


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Article Info

DATE

2017-06

AUTHORS

Ankurjyoti Saikia, Debankur Baruah, Kaushik Das, Hirak Jyoti Rabha, Anirjit Dutta, Anupjyoti Saharia

ABSTRACT

This study presents a set of regression models for predicting maximum dry unit weight (γdmax) and optimum moisture content (OMC) of fine-grained soils in terms of their consistency limits. The empirical models were developed by performing experimental investigation on forty (40) natural fine-grained soils, encompassing a wide range of liquid limit (LL) and plastic limit (PL). The compaction characteristics were determined by conducting IS light compaction test (standard Proctor equivalent in Indian standards). Observation shows that γdmax linearly decreases and in contrast, OMC increases in the same fashion with increase in LL or PL. However, in terms of regression coefficient, LL exhibits a superior correlation with γdmax and OMC than PL does. The observed variation trend of compaction characteristics with LL and PL is affirmed by a few previous studies in the domain. A set of two independent models are finally developed for predicting γdmax and OMC of soils taking both LL and PL into account. Reasonably good regression coefficients are obtained in case of both the models (R2 = 0.90 in case of γdmax and R2 = 0.86 in case of OMC model). The models are validated by predicting γdmax and OMC and comparing with actually measured values in a published study as well as present study. The root-mean-square error (RMSE) in case of γdmax prediction is 2.1% against the measured values of present study and 7.4–7.5% against measured values in literature. The RMSE involved in case of OMC prediction model is 7% against present values and 17.5–28.2% against measured values in literature. More... »

PAGES

18

References to SciGraph publications

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URI

http://scigraph.springernature.com/pub.10.1007/s40891-017-0096-4

DOI

http://dx.doi.org/10.1007/s40891-017-0096-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1085759684


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