| Summary: | In construction projects, estimation of the settlement of fine-grained soils is of critical importance, and yet is a challenging task. The coefficient of consolidation for the compression index (<i>C<sub>c</sub></i>) is a key parameter in modeling the settlement of fine-grained soil layers. However, the estimation of this parameter is costly, time-consuming, and requires skilled technicians. To overcome these drawbacks, we aimed to predict <i>C<sub>c</sub></i> through other soil parameters, i.e., the liquid limit (<i>LL</i>), plastic limit (<i>PL</i>), and initial void ratio (<i>e</i><sub>0</sub>). Using these parameters is more convenient and requires substantially less time and cost compared to the conventional tests to estimate <i>C<sub>c</sub></i>. This study presents a novel prediction model for the <i>C<sub>c</sub></i> of fine-grained soils using gene expression programming (GEP). A database consisting of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate <i>C<sub>c</sub></i> based on <i>LL</i>, <i>PL</i>, and <i>e</i><sub>0</sub>. The performance of the developed GEP-based model was evaluated through the coefficient of determination (<i>R</i><sup>2</sup>), the root mean squared error (<i>RMSE</i>), and the mean average error (<i>MAE</i>). The proposed model performed better in terms of <i>R</i><sup>2</sup>, <i>RMSE</i>, and <i>MAE</i> compared to the other models.
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