Intelligent prediction of slope stability based on visual exploratory data analysis of 77 in situ cases
文献类型：期刊论文
| 作者 | Wang, Guangjin1,2,5; Zhao, Bing1,2; Wu, Bisheng5; Zhang, Chao4; Liu, Wenlian3 |
| 刊名 | INTERNATIONAL JOURNAL OF MINING SCIENCE AND TECHNOLOGY
 |
| 出版日期 | 2023 |
| 卷号 | 33期号:1页码:47 |
| ISSN号 | 2095-2686 |
| 关键词 | Slope stability prediction Machine learning algorithm Dimensionality reduction visualization Random cross validation Coefficient of variation |
| 英文摘要 | Slope stability prediction research is a complex non-linear system problem. In carrying out slope stability prediction work, it often encounters low accuracy of prediction models and blind data preprocessing. Based on 77 field cases, 5 quantitative indicators are selected to improve the accuracy of prediction models for slope stability. These indicators include slope angle, slope height, internal friction angle, cohesion and unit weight of rock and soil. Potential data aggregation in the prediction of slope stability is analyzed and visualized based on Six-dimension reduction methods, namely principal components analysis (PCA), Kernel PCA, factor analysis (FA), independent component analysis (ICA), non-negative matrix factorization (NMF) and t-SNE (stochastic neighbor embedding). Combined with classic machine learning methods, 7 prediction models for slope stability are established and their reliabilities are examined by random cross validation. Besides, the significance of each indicator in the prediction of slope stability is discussed using the coefficient of variation method. The research results show that dimension reduction is unnecessary for the data processing of prediction models established in this paper of slope stability. Random forest (RF), support vector machine (SVM) and k-nearest neighbour (KNN) achieve the best prediction accuracy, which is higher than 90%. The decision tree (DT) has better accuracy which is 86%. The most important factor influencing slope stability is slope height, while unit weight of rock and soil is the least significant. RF and SVM models have the best accuracy and superiority in slope stability prediction. The results provide a new approach toward slope stability prediction in geotechnical engineering. (c) 2022 Published by Elsevier B.V. on behalf of China University of Mining & Technology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
| 学科主题 | Mining & Mineral Processing |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:000925434300001 |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/35407]  |
| 专题 | 中科院武汉岩土力学所 |
| 作者单位 | 1.Yunnan International Technology Transfer Center for Mineral Resources Development and Solid Waste Resource Utilization, Kunming 650093, China 2.Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China 3.China Nonferrous Metals Industry Kunming Survey and Design Research Institute Co. Ltd, Kunming 650051, China 4.State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China 5.State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China |
| 推荐引用方式 GB/T 7714 | Wang, Guangjin,Zhao, Bing,Wu, Bisheng,et al. Intelligent prediction of slope stability based on visual exploratory data analysis of 77 in situ cases[J]. INTERNATIONAL JOURNAL OF MINING SCIENCE AND TECHNOLOGY,2023,33(1):47. |
| APA | Wang, Guangjin,Zhao, Bing,Wu, Bisheng,Zhang, Chao,&Liu, Wenlian.(2023).Intelligent prediction of slope stability based on visual exploratory data analysis of 77 in situ cases.INTERNATIONAL JOURNAL OF MINING SCIENCE AND TECHNOLOGY,33(1),47. |
| MLA | Wang, Guangjin,et al."Intelligent prediction of slope stability based on visual exploratory data analysis of 77 in situ cases".INTERNATIONAL JOURNAL OF MINING SCIENCE AND TECHNOLOGY 33.1(2023):47. |
入库方式： OAI收割
来源：武汉岩土力学研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。