Empirical Models for the Determination of the Compression Index from the Atterberg Limits: Case of the Soils of the Issaba Depression in Benin

The good realization of any infrastructure in civil engineering requires the implementation of geotechnical investigations. During these investigations, the physical properties of the soil, especially the mechanical ones, must be determined with care and precision because of their capital importance...

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Published inCurrent Journal of Applied Science and Technology Vol. 41; no. 27; pp. 11 - 20
Main Authors Agbelele, Koffi Judicaël, F. Kpomahou, Y. J., C. Houehanou, Ernesto, Kenoukon, Gbenihon Céleste-Amour, Houngan, A. C.
Format Journal Article
LanguageEnglish
Published Current Journal of Applied Science and Technology 29.07.2022
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Summary:The good realization of any infrastructure in civil engineering requires the implementation of geotechnical investigations. During these investigations, the physical properties of the soil, especially the mechanical ones, must be determined with care and precision because of their capital importance in the preliminary studies before the realization of the work. The soil compression index is one of these mechanical parameters, determined by laboratory tests. It allows the calculation of settlements and is therefore essential, especially for fine clay or silty soils which are often subject to swelling phenomena. However, the realization of the test to find the compression index in the laboratory takes time, and the test itself proves delicate. Several authors have therefore proposed the    determination of this index from the limits of Atterberg, which can be obtained more quickly and easily. Through this study, empirical models have been proposed to easily calculate the compression index. Regression analyses in Matlab with 2D and 3D graph systems were performed for this purpose. The model was established for the soils of the Issaba depression in the Republic of Benin, where clayey and silty soils are very swelling. The models obtained show very good correlations with R2 coefficients higher than 0.80 and the RMSE error minimized to less than 0.5.
ISSN:2457-1024
2457-1024
DOI:10.9734/cjast/2022/v41i2731783