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 ( 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 ( e 0). 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 e 0. The performance of the developed GEP-based model was evaluated through the coefficient of determination ( R 2), the root mean squared error ( RMSE), and the mean average error ( MAE). The proposed model performed better in terms of R 2, RMSE, and MAE compared to the other models. View Full-Tex