Mechanism-Driven Intelligent Settlement Prediction for Shield Tunneling Through Areas Without Ground Monitoring DOI Creative Commons
Min Hu,

Pengpeng Zhao,

Jing Lu

et al.

Smart Cities, Journal Year: 2024, Volume and Issue: 8(1), P. 6 - 6

Published: Dec. 27, 2024

Ground settlement is a crucial indicator for assessing the safety of shield tunneling and its impact on surrounding environment. However, most existing prediction methods are based historical data, which can only be applied with effective monitoring conditions. To overcome this limitation, paper proposes mechanism-driven intelligent method (MISPM), considers mechanisms attitude movements during construction to design new features that indirectly reflect settlement. Simulation experiments were used compare different candidate algorithms performance, verifying validity accuracy model. The efficacy MISPM in predicting changes advance was substantiated by practical engineering applications. Results showed could accurately predict even without ground monitoring, thereby corroborating reliability applicability supporting safe complex geological environments. In urban infrastructure, has potential enhance efficiency tunnel ensure environmental safety, great significance development smart cities.

Language: Английский

Study on the flame radiative heat transfer and near-field radiation heat flux predictive model of vehicle fires in a tunnel DOI
Xinyang Fan, Fei Tang,

Nannan Zhu

et al.

International Journal of Heat and Mass Transfer, Journal Year: 2024, Volume and Issue: 228, P. 125666 - 125666

Published: May 10, 2024

Language: Английский

Citations

12
Monitoring-based analysis of the responses of upper structure and tunnel lining during shield tunneling with pile cutting DOI
Zhixiong Liu, Xiao‐Wei Ye, Ke Song

et al.

Tunnelling and Underground Space Technology, Journal Year: 2025, Volume and Issue: 158, P. 106427 - 106427

Published: Feb. 8, 2025

Language: Английский

Citations

1
Data-driven deformation prediction and control for existing tunnels below shield tunneling DOI
Zongbao Feng, Jingyi Wang, Wenzhao Liu

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 138, P. 109379 - 109379

Published: Sept. 26, 2024

Language: Английский

Citations

8
Integrated early warning and reinforcement support system for soft rock tunnels: A novel approach utilizing catastrophe theory and energy transfer laws DOI
Li Gan, Zhanyou Luo,

Chuangzhou Wu

et al.

Tunnelling and Underground Space Technology, Journal Year: 2024, Volume and Issue: 150, P. 105869 - 105869

Published: June 1, 2024

Language: Английский

Citations

4
Risk assessment of mountain tunnel entrance collapse based on PSO-LSTM surface settlement prediction DOI

Yazhen Sun,

Kun Lin, Jinchang Wang

et al.

Engineering Construction & Architectural Management, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 23, 2025

Purpose Predicting surface settlement at mountain tunnel entrances during construction is increasingly crucial for risk analysis, as the accuracy of these predictions directly impacts collapse assessments and personnel safety. Design/methodology/approach This study introduces a novel approach using particle swarm optimization (PSO)-optimized long short-term memory (LSTM) neural network prediction. The PSO algorithm optimizes key hyperparameters LSTM model, including number hidden layer neurons, learning rate L2 regularization, while Adam optimizer refines iterations. Dropout used in combination with adaptive regularization parameters to avoid overfitting situations, sensitivity analysis remaining variables ensures identification optimal solution. Findings based on monitoring data from Aketepu No. 1 Tunnel’s left tunnel, establishes evaluation criteria incorporating error margins root mean square (RMSE). By examining range maximum (minimum) rates cumulative values, determined that section exposed an average slow deformation, which consistent actual observations. Originality/value suggests can proceed normally, appropriate mitigate collapse. PSO-LSTM forecast model presents promising predicting risks entrances.

Language: Английский

Citations

0
A Case Study of Visualization Prediction of Deformation of a Typical Rock Tunnel Using Variable Modal Decomposition Technique, Memory Networks, and BIM Technique DOI Creative Commons

Ruibing He,

Cheng Yao, Danhong Wu

et al.

Buildings, Journal Year: 2025, Volume and Issue: 15(4), P. 615 - 615

Published: Feb. 17, 2025

A visual deformation prediction method was proposed to improve the accuracy and visualization of surrounding rock in tunnel construction, combining Variational Modal Decomposition (VMD) Bidirectional Long- Short-Term Memory (BiLSTM) network. Based on VMD decompose measured data deformation, BiLSTM model used predict final value. The results were also embedded into tunnel’s Building Information Modeling (BIM) as plug-ins, visualized through graphs color warnings. Taking arch settlement Loushan an example, showed that more consistent with situation, expression could effectively warn risk vault construction stage. This study realized combined use BIM technology, which be a reference for similar projects.

Language: Английский

Citations

0
Deformation of existing underpasses due to pile cutting and shield tunneling: Observations from field monitoring and explanations by analytical model DOI Creative Commons
Xiao‐Wei Ye, Zhixiong Liu, Yanbo Chen

et al.

Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: unknown, P. e03836 - e03836

Published: Oct. 1, 2024

Language: Английский

Citations

1
Big Data-Driven Evaluation of Shield Tunneling Performance: Methodology and Application to a Pile-Cutting Engineering Project DOI
Zhixiong Liu, Xiao‐Wei Ye, Ke Song

et al.

Published: Jan. 1, 2024

Language: Английский

Citations

0
Optimized deep learning modelling for predicting the diffusion range and state change of filling projects DOI
Ziyao Xu, Ailan Che, Hanxu Zhou

et al.

Tunnelling and Underground Space Technology, Journal Year: 2024, Volume and Issue: 154, P. 106073 - 106073

Published: Sept. 11, 2024

Language: Английский

Citations

0
Mechanism-Driven Intelligent Settlement Prediction for Shield Tunneling Through Areas Without Ground Monitoring DOI Creative Commons
Min Hu,

Pengpeng Zhao,

Jing Lu

et al.

Smart Cities, Journal Year: 2024, Volume and Issue: 8(1), P. 6 - 6

Published: Dec. 27, 2024

Ground settlement is a crucial indicator for assessing the safety of shield tunneling and its impact on surrounding environment. However, most existing prediction methods are based historical data, which can only be applied with effective monitoring conditions. To overcome this limitation, paper proposes mechanism-driven intelligent method (MISPM), considers mechanisms attitude movements during construction to design new features that indirectly reflect settlement. Simulation experiments were used compare different candidate algorithms performance, verifying validity accuracy model. The efficacy MISPM in predicting changes advance was substantiated by practical engineering applications. Results showed could accurately predict even without ground monitoring, thereby corroborating reliability applicability supporting safe complex geological environments. In urban infrastructure, has potential enhance efficiency tunnel ensure environmental safety, great significance development smart cities.

Language: Английский

Citations

0