Optimized machine learning models for predicting crown convergence of plateau mountain tunnels DOI
Xuefeng An, Fei Zheng, Yu‐Yong Jiao

et al.

Transportation Geotechnics, Journal Year: 2024, Volume and Issue: 46, P. 101254 - 101254

Published: April 18, 2024

Language: Английский

Deep learning technologies for shield tunneling: Challenges and opportunities DOI
Cheng Zhou,

Yuyue Gao,

Elton J. Chen

et al.

Automation in Construction, Journal Year: 2023, Volume and Issue: 154, P. 104982 - 104982

Published: June 27, 2023

Language: Английский

Citations

63
Digital twin enabled real-time advanced control of TBM operation using deep learning methods DOI
Limao Zhang,

Guo Jing,

Xianlei Fu

et al.

Automation in Construction, Journal Year: 2023, Volume and Issue: 158, P. 105240 - 105240

Published: Dec. 21, 2023

Language: Английский

Citations

45
Advanced informatic technologies for intelligent construction: A review DOI
Limao Zhang, Yongsheng Li, Yue Pan

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 137, P. 109104 - 109104

Published: Aug. 29, 2024

Language: Английский

Citations

32
Smart virtual sensing for deep excavations using real-time ensemble graph neural networks DOI
Chen Yang, Chen Wang,

Feng Zhao

et al.

Automation in Construction, Journal Year: 2025, Volume and Issue: 172, P. 106040 - 106040

Published: Feb. 3, 2025

Language: Английский

Citations

2
Dynamic prediction for attitude and position of shield machine in tunneling: A hybrid deep learning method considering dual attention DOI

Zeyu Dai,

Peinan Li, Mengqi Zhu

et al.

Advanced Engineering Informatics, Journal Year: 2023, Volume and Issue: 57, P. 102032 - 102032

Published: June 8, 2023

Language: Английский

Citations

31
Dynamic and explainable deep learning-based risk prediction on adjacent building induced by deep excavation DOI
Xuyang Li, Yue Pan, Limao Zhang

et al.

Tunnelling and Underground Space Technology, Journal Year: 2023, Volume and Issue: 140, P. 105243 - 105243

Published: June 8, 2023

Language: Английский

Citations

31
Unfavorable geology recognition in front of shallow tunnel face using machine learning DOI
Chenyang Zhao, Elham Mahmoudi,

Maomao Luo

et al.

Computers and Geotechnics, Journal Year: 2023, Volume and Issue: 157, P. 105313 - 105313

Published: Feb. 22, 2023

Language: Английский

Citations

27
A performance-based hybrid deep learning model for predicting TBM advance rate using Attention-ResNet-LSTM DOI Creative Commons
Sihao Yu, Zixin Zhang, Shuaifeng Wang

et al.

Journal of Rock Mechanics and Geotechnical Engineering, Journal Year: 2023, Volume and Issue: 16(1), P. 65 - 80

Published: Aug. 2, 2023

The technology of tunnel boring machine (TBM) has been widely applied for underground construction worldwide; however, how to ensure the TBM tunneling process safe and efficient remains a major concern. Advance rate is key parameter operation reflects TBM-ground interaction, which reliable prediction helps optimize performance. Here, we develop hybrid neural network model, called Attention-ResNet-LSTM, accurate advance rate. A database including geological properties operational parameters from Yangtze River Natural Gas Pipeline Project used train test this deep learning model. evolutionary polynomial regression method adopted aid selection input parameters. results numerical experiments show that our Attention-ResNet-LSTM model outperforms other commonly-used intelligent models with lower root mean square error absolute percentage error. Further, parametric analyses are conducted explore effects sequence length historical data architecture on accuracy. correlation analysis between output also implemented provide guidance adjusting relevant performance demonstrated in case study through complex ground variable strata. Finally, collected Baimang Tunnel Shenzhen China further generalization indicate that, compared conventional ResNet-LSTM better predictive capability scenarios unknown datasets due its self-adaptive characteristic.

Language: Английский

Citations

24
Physics-guided deep learning for driving force estimation in synchronous tunnel boring machines under missing cylinders DOI
Yongsheng Li, Yue Pan, Limao Zhang

et al.

Automation in Construction, Journal Year: 2024, Volume and Issue: 161, P. 105339 - 105339

Published: Feb. 21, 2024

Language: Английский

Citations

11
Spatio-temporal prediction of deep excavation-induced ground settlement: A hybrid graphical network approach considering causality DOI
Xiaojing Zhou, Yue Pan,

Jianjun Qin

et al.

Tunnelling and Underground Space Technology, Journal Year: 2024, Volume and Issue: 146, P. 105605 - 105605

Published: Feb. 21, 2024

Language: Английский

Citations

9