Prediction of creep index of soft clays using gene expression programming

The creep index plays an important role in calculating the long-term settlement of natural soft clays, so it is vital to determine the creep index quickly and accurately. However, the prediction accuracy of the existing creep index models is low. This study presents seven gene expression programming (GEP) models by using different combinations of the liquid limit w(L), plasticity index I-p, void ratio e and clay content CI as input variables for the prediction of creep index. A total of 151 datasets were collected from the available literature for building and testing the GEP models. The proposed GEP models were compared with two machine learning (ML) models (i.e., back propagation neural network and random forest) and five conventional empirical models in terms of three statistical indicators. The research results showed that the prediction performances of the two proposed GEP models (i.e., with combinations CI-w(L)-e and CI-I-p-w(L)-e as input, respectively) surpass those of the five conventional empirical models and two ML-based models, recommended for predicting the creep index of natural soft clays in engineering practice.

Automated summary

This study presents seven gene expression programming (GEP) models for predicting the creep index of natural soft clays using different combinations of input variables. The proposed GEP models outperform conventional empirical models and machine learning models in terms of prediction performance, making them recommended for engineering practice.