Advance Rate Predictions of Tunnel Boring Machines Using Bayesian-Optimized CNN-LSTM

AbstractDuring the tunnelling process of a tunnel boring machine (TBM), accurately predicting the advance rate (AR) is highly desirable for enhancing construction efficiency and safety. Inaccurate AR estimates may lead to extended construction periods and, thus, increased project costs. This study i...

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Veröffentlicht in:	ASCE-ASME journal of risk and uncertainty in engineering systems. Part A, Civil Engineering Civil Engineering, 2025-03, Vol.11 (1)
Hauptverfasser:	Men, Xiaoxiong, Li, Yuanfei, Guo, Baohe, Wang, Lai, Ye, Xinyu, Pan, Qiujing
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Bayesian analysis Boring machines Machine learning Optimization Predictions Technical Papers Tunnel construction
Online-Zugang:	Volltext
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