A real-time multiple tunneling parameter prediction method of TBM steady phase based on dual recurrent neural networks-Reference-Cited by-同舟云学术

A real-time multiple tunneling parameter prediction method of TBM steady phase based on dual recurrent neural networks

Published:2024-05-20 Issue:26 Volume:36 Page:15981-16000
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Yu Shuangfei,Xu Jinchang,Hu Jiacheng,Li Jian,Liu Jiabin,Chen Haowen,Guan Yisheng,Xu Kun,Zhang Tao

Funder

National Natural Science Foundation of China

Natural Science Foundation of Guangdong Province

Young Elite Scientists Sponsorship Program by CAST

Young Talent Support Project of Guangzhou Association for Science and Technology

Natural Science Foundation of Chongqing

Social Development Science and Technology Collaborative Innovation System Construction Project of Shaoguan City

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00521-024-09912-7.pdf

Reference59 articles.

1. Tao J, Yu H, Qin C, Sun H, Liu C (2022) A gene expression programming-based method for real-time wear estimation of disc cutter on TBM Cutterhead. Neural Comput Appl 34(22):20231–20247

2. Wei M, Song Y, Wang X, Peng J (2021) Safety diagnosis of TBM for tunnel excavation and its effect on engineering. Neural Comput Appl 33:997–1005

3. Chen H, Xiao C, Yao Z, Jiang H, Zhang T, Guan Y (2019) Prediction of tbm tunneling parameters through an lstm neural network. In: 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 702–707. IEEE

4. Hou S, Liu Y (2022) Early warning of tunnel collapse based on adam-optimised long short-term memory network and TBM operation parameters. Eng Appl Artif Intell 112:104842

5. Yu S, Zhang Z, Wang S, Huang X, Lei Q (2024) A performance-based hybrid deep learning model for predicting TBM advance rate using attention-resnet-LSTM. J Rock Mech Geotech Eng 16(1):65–80