Predicting the Friction Angle of Bangkok Sand Using State Parameter and Neural Network
Journal article
Authors/Editors
Strategic Research Themes
Publication Details
Author list: Youwai S., Wongsala K.
Publisher: Springer
Publication year: 2024
Volume number: 42
Start page: 5947
End page: 5965
Number of pages: 19
ISSN: 0960-3182
eISSN: 1573-1529
Languages: English-Great Britain (EN-GB)
Abstract
Accurate determination of the friction angle of sand is crucial for foundation design. Existing research lacks a comprehensive method to ascertain the friction angle specifically for Bangkok Sand based on the Standard Penetration Test (SPT). This study introduces an innovative technique leveraging the concept of the state parameter, a dimensionless metric characterizing the sand’s state relative to its critical condition. The method establishes a correlation between the friction angle and data obtained from triaxial tests and the SPT. The relationship between the friction angle and state parameters of Bangkok Sand is derived from triaxial testing. Additionally, a calibration chamber study involving 17 tests explores the connection between SPT values and the state parameter. To predict the state parameter, a fully connected neural network was developed, incorporating the SPT-N value and the prevailing confining stresses. This parameter is subsequently used to estimate the friction angle from triaxial test data. The validity of this approach is confirmed by a high coefficient of determination (R2 = 0.98), demonstrating its precision. Furthermore, the friction angle deduced through this novel approach is compared with those obtained using traditional methods. This study contributes to the field by providing an effective and accurate method for determining the friction angle of Bangkok Sand, essential for geotechnical engineering applications. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.
Keywords
Standard penetration test, State parameters
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