RESEARCH ARTICLE


Soil Unconfined Compressive Strength Prediction Using Random Forest (RF) Machine Learning Model



Hai-Bang Ly1, Binh Thai Pham1, *
1 Department of Civil Engineering, University of Transport Technology, Hanoi100000, Vietnam


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Creative Commons License
© 2020 Ly and Thai Pham.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Department of Civil Engineering, University of Transport Technology, Hanoi100000, Vietnam;
E-mail: binhpt@utt.edu.vn


Abstract

Aims:

Understanding the mechanical performance and applicability of soils is crucial in geotechnical engineering applications. This study investigated the possibility of application of the Random Forest (RF) algorithm – a popular machine learning method to predict the soil unconfined compressive strength (UCS), which is one of the most important mechanical properties of soils.

Methods:

A total number of 118 samples collected and their tests derived from the laboratorial experiments carried out under the Long Phu 1 power plant project, Vietnam. Data used for modeling includes clay content, moisture content, specific gravity, void ratio, liquid limit and plastic limit as input variables, whereas the target is the UCS. Several assessment criteria were used for evaluating the RF model, namely the correlation coefficient (R), root mean squared error (RMSE) and mean absolute error (MAE).

Results:

The results showed that RF exhibited a strong capability to predict the UCS, with the R value of 0.914 and 0.848 for the training and testing datasets, respectively. Finally, a sensitivity analysis was conducted to reveal the importance of input parameters to the prediction of UCS using RF. The specific gravity was found as the most affecting variable, following by clay content, liquid limit, plastic limit, moisture content and void ratio.

Conclusion:

This study might help in the accurate and quick prediction of the UCS for practice purpose.

Keywords: Unconfined compressive strength (UCS), Unconfined compression test, Random forest, Machine learning, Root mean squared error (RMSE), Mean absolute error (MAE).