Dynamic Prediction on Driving Attitude of Tunnel Boring Machine (TBM): An Automated Deep Learning Approach
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-1257-1_24
Reference8 articles.
1. Zhang Z, Ma L (2018) Attitude correction system and cooperative control of tunnel boring machine. Int J Pattern Recogn Artif Intell 32(11):1859018.1–1859018.14
2. Zhou C et al (2019) Dynamic prediction for attitude and position in shield tunneling: a deep learning method. Autom Constr 105:102840.1–102840.16
3. Festa D, Broere W, Bosch JW (2015) Kinematic behaviour of a Tunnel Boring Machine in soft soil: theory and observations. Tunnell & Undergr Space Technol Inc Trenchless Technol Res 49:208–217
4. Nakano Y, Sugimoto S, Arai H (2010) A method to measure position and attitude of a tunneling machine. Electron Commun Jpn (Part II: Electron) 71(3):1–9
5. Sakai K, Hoshiya M (1987) Prediction and control of behaviors on driving shields using Kalman filter theory. Proc Jpn Soc Civ Eng 385:69–78
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