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Prediction of creep index of soft clays using gene expression programming

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Abstract

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 wL, plasticity index Ip, 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.

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Data will be made available on reasonable request.

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Authors and Affiliations

  1. State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu, 610065, People’s Republic of China

    Xinhua Xue & Chubing Deng

Authors
  1. Xinhua Xue
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  2. Chubing Deng
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Contributions

XX: Methodology, Data acquisition, Writing—original draft. CD: Software, Numerical analysis.

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Correspondence to Xinhua Xue.

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Xue, X., Deng, C. Prediction of creep index of soft clays using gene expression programming. Soft Comput 27, 16265–16278 (2023). https://doi.org/10.1007/s00500-023-08053-8

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