Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Hao-Han Xiao"'
Autor:
Jian-Bin Li, Zu-Yu Chen, Xu Li, Liu-Jie Jing, Yun-Pei Zhang, Hao-Han Xiao, Shuang-Jing Wang, Wen-Kun Yang, Lei-Jie Wu, Peng-Yu Li, Hai-Bo Li, Min Yao, Li-Tao Fan
Publikováno v:
Underground Space, Vol 11, Iss , Pp 26-45 (2023)
This review discusses the application scenarios of the machine learning-supported performance prediction and the optimization efficiency of tunnel boring machines (TBMs). The rock mass quality ratings, which are based on the Chinese code for geologic
Externí odkaz:
https://doaj.org/article/6ee45313cc174ddcb3143c04e25d6357
Autor:
Jian-Bin Li, Zu-Yu Chen, Xu Li, Liu-Jie Jing, Yun-Pei Zhangf, Hao-Han Xiao, Shuang-Jing Wang, Wen-Kun Yang, Lei-Jie Wu, Peng-Yu Li, Hai-Bo Li, Min Yao, Li-Tao Fan
Publikováno v:
Underground Space, Vol 11, Iss , Pp 1-25 (2023)
This review summarizes the research outcomes and findings documented in 45 journal papers using a shared tunnel boring machine (TBM) dataset for performance prediction and boring efficiency optimization using machine learning methods. The big dataset
Externí odkaz:
https://doaj.org/article/ba08dd1d5dc64e86a606eaa5f449942c
Publikováno v:
Underground Space, Vol 7, Iss 4, Pp 680-701 (2022)
This paper addresses the significance of preprocessing big data collected during a tunnel boring machine (TBM) excavation before it is used for machine learning on various TBM performance predictions. The research work is based on two water diversion
Externí odkaz:
https://doaj.org/article/ebcb53ec75c344359da4ac1ea9150123