Published February 23, 2022 | Version v1
Conference paper Open

Prediction of Soil Compaction Parameters using Gene Expression Programming

Description

The determination of soil compaction parameters, maximum dry density (MDD) and optimum
moisture content (OMC), in the laboratory requires considerable time and energy. Various efforts
have been made to counter the issue by developing models to predict compaction parameters of
soil, but they are either applicable to specific soil types or specific compaction energies. A machine
learning technique, Gene Expression Programming (GEP), is used in this study to predict the
compaction parameters of soil using the index properties of soils collected from literature. In terms
of accuracy, the GEP models presented in this study outperformed the previously developed
prediction models. The accuracy (R-squared) for training and validation of MDD was found to be
0.8945 and 0.9032, respectively, and that for training and validation of OMC was 0.9165 and
0.9251, respectively.

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References

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