Tunnelling performance prediction of cantilever boring machine in sedimentary hard-rock tunnel using deep belief network
School of Civil Engineering, Xi’an University of Architecture and Technology, Xi’an, 710055, China
Zhan-ping Song & Ze-kun Zhang
School of Civil Engineering, Yancheng Institute of Technology, Yancheng, 224051, China
Yun Cheng
Shaanxi Key Laboratory of Geotechnical and Underground Space Engineering, Xi’an, 710055, China
Yun Cheng
School of Highway, Chang’an University, Xi’an, 710064, China
Ze-kun Zhang
China Railway Construction Bridge Engineering Bureau Group Co., Ltd., Tianjin, 300300, China
Teng-tian Yang
Journal of Mountain Science Published: 26 July 2023
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https://link.springer.com/article/10.1007/s11629-023-7931-y
Tunnelling performance prediction of cantilever boring machine in sedimentary hard-rock tunnel using deep belief network
link.springer.comJournal of Mountain Science - Evaluating the adaptability of cantilever boring machine (CBM) through in-depth excavation and analysis of tunnel excavation data and rock mass parameters is the...