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Utilizing multivariable mathematical models to predict maximum dry density and optimum moisture content from physical soil properties

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Abstract

Compacting soil is crucial in almost every earthwork project to achieve the densest possible state. The compaction characteristics of soil, such as Optimum Moisture Content (OMC) and Maximum Dry Density (MDD), determine its suitability for earthworks. However, it is time-consuming to determine these characteristics in a laboratory for a large volume specified soil from different borrowed sources. Therefore, it is important to determine the compaction characteristics from physical soil properties for the initial assessment. This study developed four multivariable mathematical models using Multiple Linear Regression (MLR), Pure Quadratic (PQ), Interaction (IA), and Full Quadratic (FQ) approaches to predict the Maximum Dry Density and Optimum Moisture Content of soil. The models combined particle-size and plasticity properties of soil and used seven input parameters of gravel, sand, silt, and clay contents, plastic limit, liquid limit, and plasticity index. 1038 datasets were used to develop the models, and various statistical analyses, including coefficient of determination (R2), scatter index (SI), root mean squared error (RMSE), mean absolute error (MAE), a20—index, and variance accounted for (VAF), were employed to assess their effectiveness. The results indicated that the Interaction Model (IA) was the most effective in predicting both OMC and MDD. Sensitivity analyses showed that the plasticity limit had a greater influence on OMC, while both liquid and plasticity index played important roles in predicting MDD.

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  1. Department of Civil Engineering, University of Halabja, Halabja, Iraq

    Hunar Farid Hama Ali

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Hama Ali, H.F. Utilizing multivariable mathematical models to predict maximum dry density and optimum moisture content from physical soil properties. Multiscale and Multidiscip. Model. Exp. and Des. 6, 603–627 (2023). https://doi.org/10.1007/s41939-023-00165-w

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