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 coecient 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 dierent 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 coecient 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. Keywords: soil compression index; fine-grained soils; gene expression programming (GEP); prediction; big data; machine learning; construction; infrastructures; deep learning; data mining; soil engineering; civil engineering

soil compression index; fine-grained soils; gene expression programming (GEP); prediction; big data; machine learning; construction; infrastructures; deep learning; data mining; soil engineering; civil engineering