Control of existing tunnel deformation caused by shield adjacent undercrossing construction using interpretable machine learning and multiobjective optimization DOI
Hongyu Chen, Jun Liu, Qiping Shen

et al.

Automation in Construction, Journal Year: 2024, Volume and Issue: 170, P. 105943 - 105943

Published: Dec. 24, 2024

Language: Английский

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
Stratigraphic response and control measures induced by excavation of shallow underpass tunnels DOI
Yunting Pu, Chuang Sun, Yunhe Ao

et al.

Engineering Failure Analysis, Journal Year: 2025, Volume and Issue: unknown, P. 109286 - 109286

Published: Jan. 1, 2025

Language: Английский

Citations

0
Intelligent prediction and optimization of ground settlement induced by shield tunneling construction DOI
Dejun Liu, Wenpeng Zhang, Kang Duan

et al.

Tunnelling and Underground Space Technology, Journal Year: 2025, Volume and Issue: 160, P. 106486 - 106486

Published: March 3, 2025

Language: Английский

Citations

0
Shield tunneling efficiency and stability enhancement based on interpretable machine learning and multi-objective optimization DOI Creative Commons

Wenli Liu,

Yang Chen, Tianxiang Liu

et al.

Underground Space, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

Citations

0
Multi-dimensional real-time data-driven identification for progressive fracturing characteristics of sandstone under freeze-thaw cycles and seepage-stress coupling DOI

Sen Zhang,

Yongjun Song, Xiao Wang

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 152, P. 110803 - 110803

Published: April 11, 2025

Language: Английский

Citations

0
Performance Comparison of Various Tunnel Lighting Scenarios on Driver Lane-Changing Behaviours in a Driving Simulator DOI Creative Commons
Omer Faruk Ozturk, Yusuf MAZLUM, Metin Mutlu Aydın

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(23), P. 11319 - 11319

Published: Dec. 4, 2024

Recent advances in tunnel infrastructure have emphasized safety, operational efficiency and low operating costs. Modern tunnels are equipped with systems to improve both safety performance. This study investigates the effect of lighting vehicle breakdown scenarios on driver lane changing behaviour (LCB) using a driving simulator modelled third longest twin-tube tunnel. Data were collected from 125 drivers considering various characteristics different conditions presence stopped lane. The results show that tend slow down change lanes more safely response red flashing lights. In contrast, blue sky lights, which designed reduce stress compare other dangerous scenarios, had no significant LCB. addition, demographic factors such as gender previous experience played role influencing LCB tendencies. Female those familiar simulators showed cautious behaviour. findings valuable insights into how can safety. Results highlighted potential for dynamic targeted training programs All these may contribute ongoing efforts traffic management accidents environments.

Language: Английский

Citations

0
Classification and prediction of rock mass drillability for a tunnel boring machine based on operational data mining DOI Creative Commons

Mingshe Sun,

Song Chen,

Huafei He

et al.

Frontiers in Earth Science, Journal Year: 2024, Volume and Issue: 12

Published: Dec. 23, 2024

Currently, the accurate prediction of tunnel boring machine (TBM) performance remains a considerable challenge due to complex interactions between TBM and rock mass. In this study, research work is based on part metro project that covers 2,083.94 m. The Gaussian mixture model (GMM) K-nearest neighbor algorithm (KNN) are used classify predict mass drillability in excavation process. Drillability indexes introduced cluster mass, including penetration (P), field index (FPI), torque (TPI), specific energy (SE). Statistical characteristics were analyzed, it was found their distributions did not conform normal distribution, with large variation coefficients. Clustering analysis then conducted TPI FPI within training group using , six categories classified. Subsequently, mapping relationship cutterhead speed, advance total force, established KNN classification model. It revealed when K-value set 4, has high macro - F 1 P R . Validated by testing data, method been proven be feasible effective. results indicate can effectively tunneling surrounding shield construction, particularly at face uniform homogeneous. This provides theoretical basis technical support for safe efficient tunneling.

Language: Английский

Citations

0
Control of existing tunnel deformation caused by shield adjacent undercrossing construction using interpretable machine learning and multiobjective optimization DOI
Hongyu Chen, Jun Liu, Qiping Shen

et al.

Automation in Construction, Journal Year: 2024, Volume and Issue: 170, P. 105943 - 105943

Published: Dec. 24, 2024

Language: Английский

Citations

0