Identification of Surrounding Rock in TBM Excavation with Deep Neural Network

被引：0
|
作者：
Nie, ShiWu [1 ]
Xue, Lin [1 ]
Jia, GuoPeng [1 ]
Ma, Yue [1 ]
Chen, Jing [2 ]
Huo, JunZhou [1 ]
机构：
[1] Dalian Univ Technol, Sch Mech Engn, Dalian, Peoples R China
[2] Dalian Ocean Univ, Sch Nav & Shipbldg Engn, Dalian, Peoples R China
来源：
2019 2ND INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND BIG DATA (ICAIBD 2019) | 2019年
基金：
中国国家自然科学基金;
关键词：
component; deep neural network; TBM; identification of surrounding rock; GAME; GO;
D O I：
10.1109/icaibd.2019.8837034
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, based on the measured data of a water diversion project and combined with the existing research on the artificial neural network technology, a deep neural network model is trained to realize the real-time identification of surrounding rock in tunnel boring machine (TBM) excavation. The overall accuracy is above 85%. The result shows that deep learning technology can play a role in TBM geological prediction, and TBM operation can be guided by this method.
引用
收藏
页码：251 / 255
页数：5
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