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http://dx.doi.org/10.12989/gae.2022.30.5.437

Development of new models to predict the compressibility parameters of alluvial soils  

Alzabeebee, Saif (Department of Roads and Transport Engineering, University of Al-Qadisiyah)
Al-Taie, Abbas (Department of Civil Engineering, Al-Nahrain University)
Publication Information
Geomechanics and Engineering / v.30, no.5, 2022 , pp. 437-448 More about this Journal
Abstract
Alluvial soil is challenging to work with due to its high compressibility. Thus, consolidation settlement of this type of soil should be accurately estimated. Accurate estimation of the consolidation settlement of alluvial soil requires accurate prediction of compressibility parameters. Geotechnical engineers usually use empirical correlations to estimate these compressibility parameters. However, no attempts have been made to develop correlations to estimate compressibility parameters of alluvial soil. Thus, this paper aims to develop new models to predict the compression and recompression indices (Cc and Cr) of alluvial soils. As part of the study, geotechnical laboratory tests have been conducted on large number of undisturbed samples of local alluvial soil. The obtained results from these tests in addition to available results from the literature from different parts in the world have been compiled to form the database of this study. This database is then employed to examine the accuracy of the available empirical correlations of the compressibility parameters and to develop the new models to estimate the compressibility parameters using the nonlinear regression analysis. The accuracy of the new models has been accessed using mean absolute error, root mean square error, mean, percentage of predictions with error range of ±20%, percentage of predictions with error range of ±30%, and coefficient of determination. It was found that the new models outperform the available correlations. Thus, these models can be used by geotechnical engineers with more confidence to predict Cc and Cr.
Keywords
alluvial soil; compression index; mean absolute error; nonlinear regression; recompression index; root mean square error;
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