Geomechanics and Engineering

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Estimating the unconfined compression strength of low plastic clayey soils using gene-expression programming

  • Muhammad Naqeeb Nawaz (Department of Civil Engineering, Sunchon National University) ;
  • Song-Hun Chong (Department of Civil Engineering, Sunchon National University) ;
  • Muhammad Muneeb Nawaz (NUST Institute of Civil Engineering, National University of Sciences and Technology) ;
  • Safeer Haider (Department of Civil, Environmental and Architectural Engineering, University of Padua) ;
  • Waqas Hassan (NUST Institute of Civil Engineering, National University of Sciences and Technology) ;
  • Jin-Seop Kim (Radioactive Waste Disposal Research Division, Korea Atomic Energy Research Institute)
  • Received : 2022.11.28
  • Accepted : 2023.01.26
  • Published : 2023.04.25
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Abstract

The unconfined compression strength (UCS) of soils is commonly used either before or during the construction of geo-structures. In the pre-design stage, UCS as a mechanical property is obtained through a laboratory test that requires cumbersome procedures and high costs from in-situ sampling and sample preparation. As an alternative way, the empirical model established from limited testing cases is used to economically estimate the UCS. However, many parameters affecting the 1D soil compression response hinder employing the traditional statistical analysis. In this study, gene expression programming (GEP) is adopted to develop a prediction model of UCS with common affecting soil properties. A total of 79 undisturbed soil samples are collected, of which 54 samples are utilized for the generation of a predictive model and 25 samples are used to validate the proposed model. Experimental studies are conducted to measure the unconfined compression strength and basic soil index properties. A performance assessment of the prediction model is carried out using statistical checks including the correlation coefficient (R), the root mean square error (RMSE), the mean absolute error (MAE), the relatively squared error (RSE), and external criteria checks. The prediction model has achieved excellent accuracy with values of R, RMSE, MAE, and RSE of 0.98, 10.01, 7.94, and 0.03, respectively for the training data and 0.92, 19.82, 14.56, and 0.15, respectively for the testing data. From the sensitivity analysis and parametric study, the liquid limit and fine content are found to be the most sensitive parameters whereas the sand content is the least critical parameter.

Keywords

Acknowledgement

This work was mainly supported by National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT), No. 2021R1C1C1006003. One of the co-author "Jin-Seop Kim" was partially supported by the Nuclear Research and Development Program of the National Research Foundation of Korea (NRF-2021M2E1A1085193) funded by the Ministry of Science and ICT.

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