Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories
作者: Jian ZhouEnming LiShan YangMingzheng WangXiuzhi ShiShu YaoHani S. Mitri
作者单位: 1School of Resources and Safety Engineering, Central South University, Changsha 410083, China
2MIRARCO – Mining Innovation, Laurentian University, Sudbury P3E 2C6, Canada
3Department of Mining and Materials Engineering, McGill University, Montreal, QC H3A 0E8, Canada
4Shenzhen Zhongjin Lingnan Nonfemet Company Limited, Shenzhen 518042, China
刊名: Safety Science, 2019, Vol.118 , pp.505-518
来源数据库: Elsevier Journal
DOI: 10.1016/j.ssci.2019.05.046
关键词: Slope stabilityCircular failureGradient boosting machine (GBM)Predictive modelingMine safety
原始语种摘要: Abstract(#br)Prediction of slope stability is one of the most crucial tasks in mining and geotechnical engineering projects. The accuracy of the prediction is very important for mitigating the risk of slope instability and enhancing mine safety in preliminary design. However, existing methods such as traditional statistical learning models are unable to provide accurate results for slope instability due to the complexity and uncertainties of multiple related factors with small unbalanced data samples thus requiring complex data processing algorithms. To address this limitation, this paper presents a novel prediction method that utilizes the gradient boosting machine (GBM) method to analyze slope stability. The GBM-based model is developed by the freely available R Environment software,...
全文获取路径: Elsevier  (合作)
影响因子:1.359 (2012)

  • boosting 局部通风
  • machine 机器
  • database 资料库
  • gradient 倾斜度
  • stability 稳定性
  • circular 循环的
  • modeling 制祝型
  • updated 修改
  • prediction 预报
  • based 基于