scholarly journals A Hybrid Data Mining Method for Tunnel Engineering Based on Real-Time Monitoring Data From Tunnel Boring Machines

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 90430-90449 ◽  
Author(s):  
Shuo Leng ◽  
Jia-Rui Lin ◽  
Zhen-Zhong Hu ◽  
Xuesong Shen
Keyword(s):  
Data Mining ◽  
Real Time ◽  
Mining Method ◽  
Tunnel Engineering ◽  
Real Time Monitoring ◽  
Tunnel Boring Machines ◽  
Tunnel Boring ◽  
Data Mining Method ◽  
Hybrid Data ◽  
Boring Machines

scholarly journals Real-time Optimal Load Dispatching of Power Plant Based on Data Mining Method

2009 ◽  
Vol 42 (13) ◽  
pp. 677-682
Author(s):  
Zeng Deliang ◽  
Yang Tingting ◽  
Niu Yuguang ◽  
Cheng Xiao ◽  
Liu Jizhen
Keyword(s):  
Data Mining ◽  
Power Plant ◽  
Real Time ◽  
Mining Method ◽  
Data Mining Method ◽  
Optimal Load ◽  
Time Optimal ◽  
Load Dispatching

scholarly journals Hybrid Data Mining Method of Telecom Customer Based on Improved Kmeans and XGBoost

2021 ◽  
Vol 2010 (1) ◽  
pp. 012060
Author(s):  
Yongge Shi ◽  
Shaoyun Yan ◽  
Meibin He ◽  
Xiangjun Li
Keyword(s):  
Data Mining ◽  
Mining Method ◽  
Data Mining Method ◽  
Hybrid Data

Advance prediction method for rock mass stability of tunnel boring based on deep neural network of time series

2022 ◽  
pp. 095440622110616
Author(s):  
Huo Junzhou ◽  
Jia Guopeng ◽  
Liu Bin ◽  
Nie Shiwu ◽  
Liang Junbo ◽  
...  
Keyword(s):  
Rock Mass ◽  
Prediction Model ◽  
Real Time ◽  
Prediction Method ◽  
Tunnel Boring Machines ◽  
Tunnel Boring ◽  
Error Accumulation ◽  
Boring Machines ◽  
Advance Time ◽  
And Control

Geological layers excavated using tunnel boring machines are buried deeply and sampled difficultly, and the geological behavior exhibits high diversity and complexity. Excavating in uncertain geology conditions bears the risks of excessive damage to the equipment and facing geologic hazards. Many scholars have used various signals to predict the advance geology conditions, but accurate prediction of these conditions in real-time and without effecting operations has not been realized yet. In this article, based on a large amount of corresponding data, an advance prediction model of the rock mass category (RMC) is formulated. First, the problem is divided into two parts, which are modeled separately to reduce the complexity of design and training. Then, the two models are combined in a pre-trained model, which is retrained to as the final prediction model to avoid the problem of error accumulation. The final model can predict the advance RMC in real-time and without affecting operations. The accuracy of the prediction model reaches 99% at an advance time of 60 min. The advance RMC can be used to guide the selection of support modes and control parameters without additional detection equipment and excavation down-time.

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