Strength of ensemble learning in multiclass classification of rockburst intensity-Reference-Cited by-同舟云学术

Strength of ensemble learning in multiclass classification of rockburst intensity

Published:2020-06-29 Issue:13 Volume:44 Page:1833-1853
ISSN:0363-9061
Container-title:International Journal for Numerical and Analytical Methods in Geomechanics
language:en
Short-container-title:Int J Numer Anal Methods Geomech

Author:

Zhang Junfei¹^ORCID,Wang Yuhang²,Sun Yuantian³^ORCID,Li Guichen³

Affiliation:

1. Department of Civil, Environmental and Mining EngineeringThe University of Western Australia Perth 6009 Australia

2. Institute of Information EngineeringChinese Academy of Sciences Beijing 10093 China

3. School of Mines, Key Laboratory of Deep Coal Resource Mining, Ministry of Education of ChinaChina University of Mining and Technology Xuzhou 221116 China

Funder

China Scholarship Council

Fundamental Research Funds for the Central Universities

Publisher

Wiley

Subject

Mechanics of Materials,Geotechnical Engineering and Engineering Geology,General Materials Science,Computational Mechanics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/nag.3111

Reference75 articles.

1. Rockburst mechanism in soft coal seam within deep coal mines

2. Case Histories of Four Extremely Intense Rockbursts in Deep Tunnels