Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model

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 (C_c) 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 C_c 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 C_c. This study presents a novel prediction model for the C_c 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 C_c 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. View Full-Text