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

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. Pr...

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Bibliographic Details
Published in:Mathematical problems in engineering Vol. 2020; no. 2020; pp. 1 - 11
Main Authors: Nguyen, Thuy-Anh, Pham, Binh Thai, Ly, Hai-Bang
Format: Journal Article
Language:English
Published: Cairo, Egypt Hindawi Publishing Corporation 2020
Hindawi
John Wiley & Sons, Inc
Subjects:
Algorithms
Back propagation
Back propagation networks
Correlation coefficients
Datasets
Field tests
Friction
Internal friction
Laboratories
Machine learning
Mechanical properties
Moisture content
Neural networks
Parameter estimation
Regularization
Retaining walls
Root-mean-square errors
Shear strength
Shear tests
Soil mechanics
Soil properties
Soil strength
Stability analysis
Standard deviation
Statistical analysis
Variables
ISSN:1024-123X, 1563-5147
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Summary: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.
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ISSN:1024-123X
1563-5147
DOI:10.1155/2020/8845768