Settlement prediction of Nanjing Metro Line 10 with HOA-VMD-LSTM DOI Creative Commons
Xiangfeng Duan

Measurement, Journal Year: 2024, Volume and Issue: unknown, P. 116477 - 116477

Published: Dec. 1, 2024

Language: Английский

Prediction of shield machine attitude parameters based on decomposition and multi-head attention mechanism DOI
Qiushi Wang, Wenqi Ding, Kourosh Khoshelham

et al.

Automation in Construction, Journal Year: 2025, Volume and Issue: 171, P. 105973 - 105973

Published: Jan. 18, 2025

Language: Английский

Citations

1
Physics-Informed Neural Network (PINN) model for predicting subgrade settlement induced by shield tunnelling beneath an existing railway subgrade DOI
Guankai Wang, Shan Yao, Bettina Detmann

et al.

Transportation Geotechnics, Journal Year: 2024, Volume and Issue: unknown, P. 101409 - 101409

Published: Oct. 1, 2024

Language: Английский

Citations

4
Prediction and risk assessment of lateral collapse in deep foundation pits using machine learning DOI
Hongyun Fan, Liping Li,

Shen Zhou

et al.

Automation in Construction, Journal Year: 2025, Volume and Issue: 171, P. 106011 - 106011

Published: Jan. 28, 2025

Language: Английский

Citations

0
Long-term forecasting of shield tunnel position and attitude deviation using the 1DCNN-informer method DOI Creative Commons
Jiajie Zhen, Ming Huang, Shuang Li

et al.

Engineering Science and Technology an International Journal, Journal Year: 2025, Volume and Issue: 63, P. 101957 - 101957

Published: Jan. 30, 2025

Language: Английский

Citations

0
Data-Driven Deep-Learning Model for Predicting Jacking Force of Rectangular Pipe Jacking Tunnel DOI
Yongsuo Li,

Xuran Weng,

Da Hu

et al.

Journal of Computing in Civil Engineering, Journal Year: 2025, Volume and Issue: 39(3)

Published: Feb. 3, 2025

Language: Английский

Citations

0
A deep graph neural network-based link prediction model for proactive anomaly detection in discrete manufacturing workshop DOI
Shengbo Wang, Yu Guo, Shaohua Huang

et al.

Journal of Manufacturing Systems, Journal Year: 2025, Volume and Issue: 79, P. 301 - 317

Published: Feb. 4, 2025

Language: Английский

Citations

0
Analytical solution of the evolution of railway subgrade settlement induced by shield tunnelling beneath considering soil stress release DOI
Yao Shan, Guankai Wang, Weifan Lin

et al.

Tunnelling and Underground Space Technology, Journal Year: 2025, Volume and Issue: 162, P. 106607 - 106607

Published: April 14, 2025

Language: Английский

Citations

0
Reliability Evaluation of a Shield Tunnel Waterproofing System in the Life Cycle Stage Based on a Combination Weighting Method and Cloud Model DOI
Jiguo Liu, Jin Li, Desai Guo

et al.

Journal of Construction Engineering and Management, Journal Year: 2025, Volume and Issue: 151(6)

Published: April 15, 2025

Language: Английский

Citations

0
Parametric deep learning model for predicting bearing capacity of strip foundation via neural operator DOI Creative Commons
Tong Niu,

Maosong Huang,

Jian Yu

et al.

AI in Civil Engineering, Journal Year: 2025, Volume and Issue: 4(1)

Published: May 1, 2025

Language: Английский

Citations

0
Prediction of shield tunneling attitude: A hybrid deep learning approach considering feature temporal attention DOI

Liang Zeng,

Jia Ao Chen,

Chenning Zhang

et al.

Measurement Science and Technology, Journal Year: 2024, Volume and Issue: 35(8), P. 086211 - 086211

Published: May 21, 2024

Abstract Accurate prediction of shield attitude deviation is essential to ensure safe and efficient tunneling. However, previous studies have predominantly emphasized temporal correlation, which has limitations in engineering guidance accuracy. This research proposes a hybrid deep learning approach considering feature attention (FTA-N-GRU) for prediction. To elucidate the contributions each parameter, Integrated Gradients algorithm leveraged parameter sensitivity analysis. The results from Bangladesh Karnaphuli River Tunnel Project indicate that: proposed model outperforms other models integration can adaptively allocate weights input parameters, facilitating inexperienced operators discerning crucial variations decision-making. By incorporating attention, effectively explores connection among different output time steps, improving overall accuracy reliability. Consequently, are empowered with timely information proactively adjust operations before deviations occur, underscoring significance this promoting tunneling practices.

Language: Английский

Citations

1