Backpropagation Neural Network-Based Machine Learning Model for Prediction of Soil Friction Angle

被引:23
作者
Thuy-Anh Nguyen [1 ]
Hai-Bang Ly [1 ]
Binh Thai Pham [1 ]
机构
[1] Univ Transport Technol, Hanoi 100000, Vietnam
关键词
SHEAR-STRENGTH PARAMETERS; LEVENBERG-MARQUARDT; PERFORMANCE; SOLUBILITY; ALGORITHMS;
D O I
10.1155/2020/8845768
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In the design process of foundations, pavements, retaining walls, and other geotechnical matters, estimation of soil strength-related parameters is crucial. In particular, the friction angle is a critical shear strength factor in assessing the stability and deformation of geotechnical structures. Practically, laboratory or field tests have been conducted to determine the friction angle of soil. However, these jobs are often time-consuming and quite expensive. Therefore, the prediction of geo-mechanical properties of soils using machine learning techniques has been widely applied in recent times. In this study, the Bayesian regularization backpropagation algorithm is built to predict the internal friction angle of the soil based on 145 data collected from experiments. The performance of the model is evaluated by three specific statistical criteria, such as the Pearson correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE). The results show that the proposed algorithm performed well for the prediction of the friction angle of soil (R = 0.8885, RMSE = 0.0442, and MAE = 0.0328). Therefore, it can be concluded that the backpropagation neural network-based machine learning model is a reasonably accurate and useful prediction tool for engineers in the predesign phase.
引用
收藏
页数:11
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