RESEARCH ARTICLE


Prediction of Shear Strength of Soil Using Direct Shear Test and Support Vector Machine Model



Hai-Bang Ly1, Binh Thai Pham1, *
1 Department of Geotechnical Engineering, University of Transport Technology, Hanoi 100000, Vietnam


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Creative Commons License
© 2020 Ly and 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 Geotechnical Engineering, University of Transport Technology, Hanoi, Vietnam E-mail: binhpt@utt.edu.vn


Abstract

Background:

Shear strength of soil, the magnitude of shear stress that a soil can maintain, is an important factor in geotechnical engineering.

Objective:

The main objective of this study is dedicated to the development of a machine learning algorithm, namely Support Vector Machine (SVM) to predict the shear strength of soil based on 6 input variables such as clay content, moisture content, specific gravity, void ratio, liquid limit and plastic limit.

Methods:

An important number of experimental measurements, including more than 500 samples was gathered from the Long Phu 1 power plant project’s technical reports. The accuracy of the proposed SVM was evaluated using statistical indicators such as the coefficient of correlation (R), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) over a number of 200 simulations taking into account the random sampling effect. Finally, the most accurate SVM model was used to interpret the prediction results due to Partial Dependence Plots (PDP).

Results:

Validation results showed that SVM model performed well for prediction of soil shear strength (R = 0.9 to 0.95), and the moisture content, liquid limit and plastic limit were found as the three most affecting features to the prediction of soil shear strength.

Conclusion:

This study might help in quick and accurate prediction of soil shear strength for practical purposes in civil engineering.

Keywords: Machine learning, Partial Dependence Plot (PDP), Root Mean Squared Error (RMSE), Support Vector Machine (SVM), Soil shear strength, Vietnam.