Monitoring-based analysis of the responses of upper structure and tunnel lining during shield tunneling with pile cutting

Tunnelling and Underground Space Technology, Год журнала: 2025, Номер 158, С. 106427 - 106427

Опубликована: Фев. 8, 2025

Язык: Английский

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Stratigraphic response and control measures induced by excavation of shallow underpass tunnels

Engineering Failure Analysis, Год журнала: 2025, Номер unknown, С. 109286 - 109286

Опубликована: Янв. 1, 2025

Язык: Английский

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Intelligent prediction and optimization of ground settlement induced by shield tunneling construction

Tunnelling and Underground Space Technology, Год журнала: 2025, Номер 160, С. 106486 - 106486

Опубликована: Март 3, 2025

Язык: Английский

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Shield tunneling efficiency and stability enhancement based on interpretable machine learning and multi-objective optimization

Underground Space, Год журнала: 2025, Номер unknown

Опубликована: Март 1, 2025

Язык: Английский

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

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 152, С. 110803 - 110803

Опубликована: Апрель 11, 2025

Язык: Английский

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Performance Comparison of Various Tunnel Lighting Scenarios on Driver Lane-Changing Behaviours in a Driving Simulator

Applied Sciences, Год журнала: 2024, Номер 14(23), С. 11319 - 11319

Опубликована: Дек. 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.

Язык: Английский

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

Frontiers in Earth Science, Год журнала: 2024, Номер 12

Опубликована: Дек. 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.

Язык: Английский

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Control of existing tunnel deformation caused by shield adjacent undercrossing construction using interpretable machine learning and multiobjective optimization

Automation in Construction, Год журнала: 2024, Номер 170, С. 105943 - 105943

Опубликована: Дек. 24, 2024

Язык: Английский

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