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A single-valued neutrosophic Gaussian process regression approach for stability prediction of open-pit mine slopes

A single-valued neutrosophic Gaussian process regression approach for stability prediction of... The accurate prediction of the stability of open-pit mine slopes is usually one of the difficult problems faced by mining engineers and researchers, which is caused by the indeterminacy and uncertainty contained in the influence factors of stability of open-pit mine slopes. The single-valued neutrosophic number (SVNN) is very suitable for representing the information of truth, falsity, and indeterminacy in inconsistent and indeterminate situations. This paper proposes a method combining SVNN with Gaussian process regression (SVNN-GPR) to evaluate the open-pit mine slopes stability in uncertain environment. At first, the proposed method expresses the uncertain influence factors/indices (dip of potential failure plane, cohesion, friction angle, etc.) and the stability state of open-pit mine slopes as SVNNs. Then, the SVNN-GPR is established to obtain the latent complicated relationship between SVNN influence factors and slope stability. Finally, we use the score values of the SVNN output of the proposed method to predict the stability of each slope. To test the performance of the proposed method, we collected 286 slope cases from Zhejiang Province, China. The practical application results reflect that the SVNN-GPR method based on the exponential covariance function and isotropic distance measure has best training and testing results, and the training accuracy reaches 98.0% and the testing accuracy reaches 96.5%. This shows the proposed SVNN-GPR method provides a new effective way for predicting open-pit mine slopes stability in inconsistent and indeterminate situations. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Intelligence Springer Journals

A single-valued neutrosophic Gaussian process regression approach for stability prediction of open-pit mine slopes

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References (63)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
0924-669X
eISSN
1573-7497
DOI
10.1007/s10489-022-04089-9
Publisher site
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Abstract

The accurate prediction of the stability of open-pit mine slopes is usually one of the difficult problems faced by mining engineers and researchers, which is caused by the indeterminacy and uncertainty contained in the influence factors of stability of open-pit mine slopes. The single-valued neutrosophic number (SVNN) is very suitable for representing the information of truth, falsity, and indeterminacy in inconsistent and indeterminate situations. This paper proposes a method combining SVNN with Gaussian process regression (SVNN-GPR) to evaluate the open-pit mine slopes stability in uncertain environment. At first, the proposed method expresses the uncertain influence factors/indices (dip of potential failure plane, cohesion, friction angle, etc.) and the stability state of open-pit mine slopes as SVNNs. Then, the SVNN-GPR is established to obtain the latent complicated relationship between SVNN influence factors and slope stability. Finally, we use the score values of the SVNN output of the proposed method to predict the stability of each slope. To test the performance of the proposed method, we collected 286 slope cases from Zhejiang Province, China. The practical application results reflect that the SVNN-GPR method based on the exponential covariance function and isotropic distance measure has best training and testing results, and the training accuracy reaches 98.0% and the testing accuracy reaches 96.5%. This shows the proposed SVNN-GPR method provides a new effective way for predicting open-pit mine slopes stability in inconsistent and indeterminate situations.

Journal

Applied Intelligence – Springer Journals

Published: Jun 1, 2023

Keywords: Single-valued neutrosophic number; Gaussian process regression; Machine learning (ML); Slope stability; Classification

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