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

Hai-Bang Ly,Binh Thai Pham

doi:10.2174/1874836802014010268

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.

Highlights

Research in the field of soil mechanics has been the subject of miscellaneous studies over decades [1]
The performance of Support Vector Machine (SVM) is evaluated in performing 200 simulations taking into the random sampling strategy to construct the training and testing datasets. As it is well-known that the data appear in the training dataset greatly affects the performance of machine learning models, the random indexing process of samples aimed to fully evaluate the performance and robustness of SVM under the presence of variability in the input space
It is worth noticing that 70% of the experimental data was randomly taken to construct the SVM model, the corresponding R, Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) values were different for each simulation

Summary

Introduction

Research in the field of soil mechanics has been the subject of miscellaneous studies over decades [1]. The shear strength of the soil is a very important parameter in geotechnical engineering for assessment of the stability of retaining walls, embankments and determination of the bearing capacity of highway construction foundations. The determination of this parameter is often carried out in the laboratory by different kinds of tests, such as triaxial shear test, direct shear test and unconfined compression test. Conducting these tests usually takes time and is often costly. The magnitude of shear stress that a soil can maintain, is an important factor in geotechnical engineering

Methods

Results

Conclusion