岩石力学通报（英文）2025，Vol.4Issue(4)：27-40,14.DOI:10.1016/j.rockmb.2025.100219
Deep learning-powered rock mass classification:Predicting RMR from Q-system parameters with high accuracy
Deep learning-powered rock mass classification:Predicting RMR from Q-system parameters with high accuracy
Tawanda Zvarivadza 1Abiodun Ismail Lawal 2Moshood Onifade 3Francois Mulenga 2Sangki Kwon 4Manoj Khandelwal3
作者信息
- 1. Department of Civil,Environmental and Natural Resources Engineering,Luleå University of Technology,Luleå,Sweden
- 2. Department of Mining Engineering,University of South Africa,Florida Campus Private Bag X6,Johannesburg,1710,South Africa
- 3. Institute of Innovation,Science and Sustainability,Federation University Australia,Ballarat,Victoria 3350,Australia
- 4. Department of Energy Resources Engineering,Inha University,Yong-Hyun Dong,Nam Ku,Incheon,South Korea
- 折叠
摘要
关键词
Deep learning/Rock mass rating(RMR)/Q parameters/Neural network/ANNKey words
Deep learning/Rock mass rating(RMR)/Q parameters/Neural network/ANN引用本文复制引用
Tawanda Zvarivadza,Abiodun Ismail Lawal,Moshood Onifade,Francois Mulenga,Sangki Kwon,Manoj Khandelwal..Deep learning-powered rock mass classification:Predicting RMR from Q-system parameters with high accuracy[J].岩石力学通报（英文）,2025,4(4):27-40,14.
