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 (
Cc) is a key parameter in modeling the settlement of fine-grained soil
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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 (
Cc) 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
Cc through other soil parameters, i.e., the liquid limit (
LL), plastic limit (
PL), and initial void ratio (
e0). Using these parameters is more convenient and requires substantially less time and cost compared to the conventional tests to estimate
Cc. This study presents a novel prediction model for the
Cc 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
Cc based on
LL,
PL, and
e0. The performance of the developed GEP-based model was evaluated through the coefficient of determination (
R2), the root mean squared error (
RMSE), and the mean average error (
MAE). The proposed model performed better in terms of
R2,
RMSE, and
MAE compared to the other models.
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