Extension of an Autopilot Model of Shield Tunneling Machines to Curved Section using Machine Learning

2021 ◽  
Author(s):  
Yasuyuki Kubota ◽  
Nobuyoshi Yabuki ◽  
Tomohiro Fukuda
Keyword(s):  
Machine Learning ◽  
Shield Tunneling ◽  
Curved Section

Prediction of shield tunneling-induced ground settlement using machine learning techniques

2019 ◽  
Vol 13 (6) ◽  
pp. 1363-1378 ◽  
Author(s):  
Renpeng Chen ◽  
Pin Zhang ◽  
Huaina Wu ◽  
Zhiteng Wang ◽  
Zhiquan Zhong
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Ground Settlement ◽  
Shield Tunneling ◽  
Learning Techniques

scholarly journals Analysis of Asymmetrical Deformation of Surface and Oblique Pipeline Caused by Shield Tunneling along Curved Section

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2396
Author(s):  
Huangshi Deng ◽  
Helin Fu ◽  
Yue Shi ◽  
Zhen Huang ◽  
Qibing Huang
Keyword(s):  
Theoretical Prediction ◽  
Straight Line Segment ◽  
Prediction Formula ◽  
Difference Model ◽  
Shield Tunneling ◽  
Maximum Deformation ◽  
Straight Line ◽  
Curved Section ◽  
Shield Machine ◽  
Asymmetrically Distributed

The deformation of existing pipelines caused by the tunneling of a shield machine along curved sections has not been sufficiently researched, and a corresponding theoretical prediction formula is lacking. This paper derives a prediction formula for the deformation of an existing pipeline caused by shield machine tunneling along a curved section. Further, a finite difference model (FDM) corresponding to an actual project is built. Finally, the deformation of the surface and existing pipelines caused by shield machine tunneling along the curved section is analyzed. The research results show that the results of theoretical prediction, FDM calculation, and field monitoring data are consistent. In addition, the deformation of the surface and the existing pipeline are asymmetrically distributed when the shield machine tunnels along the curve section instead of symmetrically distributed (for straight line segment). When the pipeline is perpendicular to the tunnel axis, the maximum deformation position of the existing pipeline deviates from the tunnel axis by about 0.5 times the tunnel radius. In addition, as the angle β between the pipeline axis and the tunnel axis increases, the maximum deformation position of the pipeline gradually approaches the tunnel axis.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Machine Learning for Speaker Recognition

2020 ◽  
Author(s):  
Man-Wai Mak ◽  
Jen-Tzung Chien
Keyword(s):  
Machine Learning ◽  
Speaker Recognition

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning

Phase Transitions in Machine Learning

2009 ◽  
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Machine Learning ◽  
Phase Transitions

Understanding Machine Learning

2009 ◽  
Author(s):  
Shai Shalev-Shwartz ◽  
Shai Ben-David
Keyword(s):  
Machine Learning
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