Prediction of Compression Index of Soil - A Perspective Review

International Journal of Civil Engineering

© 2025 by SSRG - IJCE Journal

Volume 12 Issue 4

Year of Publication : 2025

Authors : Bishwajoty Paul, Dipika Devi, Santosh Kumar Tamang

10.14445/23488352/IJCE-V12I4P105

How to Cite?

Bishwajoty Paul, Dipika Devi, Santosh Kumar Tamang, "Prediction of Compression Index of Soil - A Perspective Review," SSRG International Journal of Civil Engineering, vol. 12,  no. 4, pp. 48-66, 2025. Crossref, https://doi.org/10.14445/23488352/IJCE-V12I4P105

Abstract:

The settlement analysis of structures built over soil masses is integral to design, ensuring both stability and long-term performance. A critical parameter influencing settlement behaviour is the soil’s Compression Index (Cc), which provides insights into soil compressibility and potential risk factors essential for informed structural design. Traditionally, the estimation of Cc relies on standardized laboratory procedures (e.g., Bureau of Indian Standards), which, while accurate, are often costly, labour-intensive, and time-consuming. To address these limitations, researchers have explored correlations between Cc and easily measurable index properties of soil, such as Atterberg limits and others. Through exploring these index properties, predictive models based on Linear Regression and Computer-aided Learning algorithms have emerged as efficient alternatives for Cc estimation. This review provides a comprehensive perspective on current methodologies for Cc prediction, highlighting that liquid limit, in-situ void ratio (eo), and natural moisture content exhibit a significant correlation with Cc estimates across both linear and machine learning models. The findings from this study underscore the potential for data-driven approaches to streamline soil compressibility assessments, offering reliable and time-efficient predictions essential for geotechnical design practices. This paper also shows that predictive models and simple correlations can be developed by using an extended range of index properties obtained from bibliographies, self-generated experimental data or other project sources.

Keywords:

Compression Index, Computer aided Learning, Index properties, Linear Regression, Machine Learning.

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