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

TL;DR

This paper introduces a gene expression programming model to accurately predict the compression index of fine-grained soils using easily obtainable soil parameters, reducing reliance on costly and time-consuming traditional tests.

Contribution

A novel GEP-based model and closed-form equation for estimating the compression index of fine-grained soils from simple soil parameters.

Findings

The GEP model outperformed other models in accuracy.

The model provides a quick and cost-effective estimation method.

It achieves high R2 and low error metrics.

Abstract

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.