Advanced identification method for adverse geological conditions in TBM tunnels based on stacking ensemble algorithm and Bayesian theory

Accurately identifying adverse geology ahead of the tunnel face in real-time is essential for guaranteeing the safe and efficient excavation of tunnel boring machines (TBMs). This study presents a novel real-time identification method for adverse geological conditions ahead of TBM tunnel face based...

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Veröffentlicht in:	Tunnelling and underground space technology Jg. 163; S. 106741
Hauptverfasser:	Wu, Zhijun, Huo, Dongbo, Chu, Zhaofei, Liu, Xiqi, Weng, Lei, Xu, Xiangyu, Li, Zheng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.09.2025
Schlagworte:	Adverse geology prediction Bayesian theory Ensemble learning Sliding double-windows method TBM big data TBM big data Sliding double-windows method Bayesian theory Adverse geology prediction Ensemble learning
ISSN:	0886-7798
Online-Zugang:	Volltext
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