Application of artificial neural network in unsupported conical slope in anisotropic and inhomogeneous clays
Vol. 1 No. 15 (2023), Các bài báo
Vol. 1 No. 15 (2023)

Application of artificial neural network in unsupported conical slope in anisotropic and inhomogeneous clays

Các bài báo
https://doi.org/10.61591/jslhu.15.329
Published 2023-12-21
Nguyen Dang Khoa
Lac Hong University, No. 10 Huynh Van Nghe St., Buu Long ward, Bien Hoa, Dong Nai, Vietnam
Nguyen Dinh Du
Lac Hong University, No. 10 Huynh Van Nghe St., Buu Long ward, Bien Hoa, Dong Nai, Vietnam
Tran Hoang Duy Tung
Lac Hong University, No. 10 Huynh Van Nghe St., Buu Long ward, Bien Hoa, Dong Nai, Vietnam
Ngo Cong Danh
Lac Hong University, No. 10 Huynh Van Nghe St., Buu Long ward, Bien Hoa, Dong Nai, Vietnam
Nguyen Dinh Binh
Lac Hong University, No. 10 Huynh Van Nghe St., Buu Long ward, Bien Hoa, Dong Nai, Vietnam
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Keywords

Unsupported Conical Slopes
Anisotropic Clays
ANN model
Artifiial Intelligent
Machine learning

How to Cite

Nguyen Dang Khoa, Nguyen Dinh Du, Tran Hoang Duy Tung, Ngo Cong Danh, & Nguyen Dinh Binh. (2023). Application of artificial neural network in unsupported conical slope in anisotropic and inhomogeneous clays. Journal of Science Lac Hong University, 1(15), 84–90. https://doi.org/10.61591/jslhu.15.329
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Abstract

The paper proposed a correlation equation to determine the stability number of unsupported conical slopes on anisotropic and non-homogenous clays using Machine Learning applications based on the Artificial Neural Network model - ANN. The stability number (N) of unsupported conical slopes on anisotropic and non-homogenous clays was investigated by considering the effects of raising undrained shear strength (m), anisotropic strength ratio (re), the dip angle of slopes (β), and the ratio of slope height and radius at the bottom (H/B). The numerical method based on the finite element techniques was applied to investigate N variation due to changes of re, m, H/B and β. The results of numerical models are used as the data for the Artificial Neural Network model to propose the correlation equation between N and input parameters of re, m, H/B and β. The results show that the proposed correlation equation is well in agreement with the results of N from numerical models.

https://doi.org/10.61591/jslhu.15.329
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