Hybrid stacking ensemble algorithm and simulated annealing optimization for stability evaluation of underground entry-type excavations

The stability of underground entry-type excavations (UETEs) is of paramount importance for ensuring the safety of mining operations. As more engineering cases are accumulated, machine learning (ML) has demonstrated great potential for the stability evaluation of UETEs. In this study, a hybrid stacki...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Underground space (Beijing) Jg. 17; S. 25 - 44
Hauptverfasser:	Liu, Leilei, Zhao, Guoyan, Liang, Weizhang, Jian, Zheng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Shanghai Elsevier B.V 01.08.2024 KeAi Publishing Communications Ltd KeAi Communications Co., Ltd
Schlagworte:	Accuracy Algorithms Base and meta learners Coal mining Critical span graph Decision trees Deformation Engineering Graphical representations Hybrid stacking ensemble Indicators Machine learning Methods Mines Monitoring systems Multilayer perceptrons Neural networks Optimization Performance evaluation Recall Risk analysis Risk management Rock mass rating Simulated annealing Simulation Stability analysis Support vector machines Underground entry-type excavations (UETEs) Base and meta learners Critical span graph Hybrid stacking ensemble Underground entry-type excavations (UETEs) Machine learning Simulated annealing
ISSN:	2467-9674, 2096-2754, 2467-9674
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!