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Prediction of stability coefficient of open-pit mine slope based on artificial intelligence deep learning algorithm

The mining of open pit mines is widespread in China, and there are many cases of landslide accidents. Therefore, the problem of slope stability is highlighted. The stability of the slope is a factor that directly affects the mining efficiency and the safety of the entire mining process. According to...

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Autores principales: Wang, Shuai, Zhang, Zongbao, Wang, Chao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10368623/
https://www.ncbi.nlm.nih.gov/pubmed/37491388
http://dx.doi.org/10.1038/s41598-023-38896-y
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author Wang, Shuai
Zhang, Zongbao
Wang, Chao
author_facet Wang, Shuai
Zhang, Zongbao
Wang, Chao
author_sort Wang, Shuai
collection PubMed
description The mining of open pit mines is widespread in China, and there are many cases of landslide accidents. Therefore, the problem of slope stability is highlighted. The stability of the slope is a factor that directly affects the mining efficiency and the safety of the entire mining process. According to the statistics, there is a 15 percent chance of finding landslide risk in China’s large-scale mines. And due to the expansion of the mining scale of the enterprise, the problem of slope stability has become increasingly obvious, which has become a major subject in the study of open-pit mine engineering. In order to better predict the slope stability coefficient, this study takes a mine in China as a case to deeply discuss the accuracy of different algorithms in the stability calculation, and then uses a deep learning algorithm to study the stability under rainfall conditions. The change of the coefficient and the change of the stability coefficient before and after the slope treatment are experimentally studied with the displacement of the monitoring point. The result shows that the safety coefficient calculated by the algorithm in this paper is about 7% lower than that of the traditional algorithm. In the slope stability analysis before treatment, the safety factor calculated by the algorithm in this paper is 1.086, and the algorithm in this paper is closer to reality. In the stability analysis of the slope after treatment, the safety factor calculated by the algorithm in this paper is 1.227, and the stability factor meets the requirements of the specification. It also shows that the deep learning algorithm effectively improves the efficiency of the slope stability factor prediction and improves security during project development.
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spelling pubmed-103686232023-07-27 Prediction of stability coefficient of open-pit mine slope based on artificial intelligence deep learning algorithm Wang, Shuai Zhang, Zongbao Wang, Chao Sci Rep Article The mining of open pit mines is widespread in China, and there are many cases of landslide accidents. Therefore, the problem of slope stability is highlighted. The stability of the slope is a factor that directly affects the mining efficiency and the safety of the entire mining process. According to the statistics, there is a 15 percent chance of finding landslide risk in China’s large-scale mines. And due to the expansion of the mining scale of the enterprise, the problem of slope stability has become increasingly obvious, which has become a major subject in the study of open-pit mine engineering. In order to better predict the slope stability coefficient, this study takes a mine in China as a case to deeply discuss the accuracy of different algorithms in the stability calculation, and then uses a deep learning algorithm to study the stability under rainfall conditions. The change of the coefficient and the change of the stability coefficient before and after the slope treatment are experimentally studied with the displacement of the monitoring point. The result shows that the safety coefficient calculated by the algorithm in this paper is about 7% lower than that of the traditional algorithm. In the slope stability analysis before treatment, the safety factor calculated by the algorithm in this paper is 1.086, and the algorithm in this paper is closer to reality. In the stability analysis of the slope after treatment, the safety factor calculated by the algorithm in this paper is 1.227, and the stability factor meets the requirements of the specification. It also shows that the deep learning algorithm effectively improves the efficiency of the slope stability factor prediction and improves security during project development. Nature Publishing Group UK 2023-07-25 /pmc/articles/PMC10368623/ /pubmed/37491388 http://dx.doi.org/10.1038/s41598-023-38896-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Wang, Shuai
Zhang, Zongbao
Wang, Chao
Prediction of stability coefficient of open-pit mine slope based on artificial intelligence deep learning algorithm
title Prediction of stability coefficient of open-pit mine slope based on artificial intelligence deep learning algorithm
title_full Prediction of stability coefficient of open-pit mine slope based on artificial intelligence deep learning algorithm
title_fullStr Prediction of stability coefficient of open-pit mine slope based on artificial intelligence deep learning algorithm
title_full_unstemmed Prediction of stability coefficient of open-pit mine slope based on artificial intelligence deep learning algorithm
title_short Prediction of stability coefficient of open-pit mine slope based on artificial intelligence deep learning algorithm
title_sort prediction of stability coefficient of open-pit mine slope based on artificial intelligence deep learning algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10368623/
https://www.ncbi.nlm.nih.gov/pubmed/37491388
http://dx.doi.org/10.1038/s41598-023-38896-y
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