Optimization of random forest through the use of MVO, GWO and MFO in evaluating the stability of underground entry-type excavations

•We concentrated on the stability prediction of underground entry-type excavations.•Three RF-based hybrid models are present to predict entry-type excavation stability.•MVO, GWO and MFO can assist the hyper-parameters tuning of RF.•The accuracy of the proposed models is better than the models in pre...

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Veröffentlicht in:	Tunnelling and underground space technology Jg. 124; S. 104494
Hauptverfasser:	Zhou, Jian, Huang, Shuai, Qiu, Yingui
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Elsevier Ltd 01.06.2022 Elsevier BV
Schlagworte:	Algorithms Classification Construction accidents & safety Critical span graph Decision trees Entry-type excavations Excavation Hybrid model Kernel functions Machine learning Mathematical models Mining engineering Optimization Parameters Performance enhancement Performance evaluation Performance indices Random forest Rock masses Stability Stability analysis Support vector machines Systems stability Underground construction Working conditions Random forest Hybrid model Stability Critical span graph Entry-type excavations Machine learning
ISSN:	0886-7798, 1878-4364
Online-Zugang:	Volltext
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