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[Objective] There are many characteristics of the box culvert tunnel, such as large section, slow construction progress and instability excavation face.[Method] Based on the project of shallow buried tunnel section of Luzhou Avenue in Binhu Science City of Hefei City, this study used limit equilibrium theory, Mohr-Coulomb strength criterion and full overburden weight theory to carry out theoretical derivation, and combined with large-scale three-dimensional fine numerical model to study the limit support pressure of excavation face. [Result] The results show that : it proposed a simple calculation method for the limit support pressure of the excavation face of large-section box culvert tunnel under the action of pipe curtain. Based on this, according to the calculated value of Bishop slice method, the error was very small. Compared with the calculated value of the limit support pressure proposed in this paper, which proves the accuracy of the method. Through the establishment of three-dimensional numerical model of pipe curtain box culvert construction, it is found that the surface s ment and the displacement of the longitudinal axis of the excavation face gradually decrease with the increase of the support pressure and tend to be stable. When the support pressure exceeds the static earth pressure, the upper soil is subjected to strong extrusion, and the excavation face state is easy to be inverted S shape. [Conclusion] The above results can be used as a reference for related projects. [ABSTRACT FROM AUTHOR]

This paper presents a specific 3D computational fluid dynamics model to quantify the effects of the combustion of asphalt road pavement on user safety in the event of a fire in a bi-directional road tunnel. Since the consequences on tunnel users and/or rescue teams might be affected not only by the tunnel geometry but also by the type of ventilation and traffic flow, the environmental conditions caused by the fire in the tunnel under natural or longitudinal mechanical ventilation, as well as congested traffic conditions, were more especially investigated. The simulation results showed that the combustion of the asphalt pavement in the event of a 100 MW fire, compared to the case of a non-combustible road pavement, caused (i) an increase in smoke concentrations; (ii) a greater number of users exposed to the risk of incapacity to escape from the tunnel; (iii) a more difficult situation for the firefighters entering the tunnel upstream of the fire source in the case of natural ventilation; (iv) a higher probability of the domino effect for vehicles queued downstream of the fire when the tunnel is mechanically ventilated. [ABSTRACT FROM AUTHOR]

Given the role of road tunnels in a road network, it is relevant to quantitatively assess their resilience due to disruptive events. In this respect, the travel speed of traffic flow, as a metric of resilience, in the event of traffic accidents in road tunnels has been scarcely used. This represents a gap of knowledge that this paper intends to fill. For the purpose, the research method applied involves the development of a traffic macro-simulation model, and the analysis of results using the average travel speed and its spatial profile as a resilience metric. Particularly, we have evaluated the resilience of a twin-tube motorway tunnel when a traffic accident occurs in a tube. The findings showed how the best functionality level of the system, expressed in terms of average vehicles' speed, with its related greatest resilience index, is associated with the partial closure of the disrupted tube rather than the complete one. Further benefits might be obtained by activating Variable Message Signs (VMSs) that alert only the Heavy Goods Vehicles (HGVs) to exit the motorway before entering the tunnel and to use an alternative itinerary identified in the nearby transportation network. In this respect, we found that by means of the activation of VMSs as a traffic control strategy, the resilience index increased by about 7–17% when the traffic accident caused the partial closure of the disrupted tube for 1–3 h. Improvements might also be achieved by rapidly opening the two traffic by-passes at the tunnel portals that allow for the use of the adjacent undisrupted tube for two-way traffic. The unavailability of an alternative itinerary in the nearby transportation network along which to re-route the HGVs is also examined, finding a reduction in the functionality conditions of the system. The results obtained might serve as a support tool in the choice of functional recovery strategies in the case of the temporary partial or complete block of a tunnel tube due to a traffic accident. [ABSTRACT FROM AUTHOR]

This paper presents a specific 3D computational fluid dynamics model to quantify the effects of the combustion of asphalt road pavement on user safety in the event of a fire in a bi-directional road tunnel. Since the consequences on tunnel users and/or rescue teams might be affected not only by the tunnel geometry but also by the type of ventilation and traffic flow, the environmental conditions caused by the fire in the tunnel under natural or longitudinal mechanical ventilation, as well as congested traffic conditions, were more especially investigated. The simulation results showed that the combustion of the asphalt pavement in the event of a 100 MW fire, compared to the case of a non-combustible road pavement, caused (i) an increase in smoke concentrations; (ii) a greater number of users exposed to the risk of incapacity to escape from the tunnel; (iii) a more difficult situation for the firefighters entering the tunnel upstream of the fire source in the case of natural ventilation; (iv) a higher probability of the domino effect for vehicles queued downstream of the fire when the tunnel is mechanically ventilated.

With the proliferation of tunnels in mountainous and intricate landscapes, the design of tunnel lighting has emerged as a crucial factor in curbing tunnel-related accidents and bolstering driving safety. This study employed scaled experiments and integrated human eye luminance adaptation curves to refine tunnel lighting configurations. The investigation revealed that the incorporation of high-light-transmittance sunshades within tunnels effectively diminished external luminance, thereby the alleviating the 'black hole effect'. From an economic perspective, adopting a tunnel lighting model with sunshades can achieve up to a 30% reduction in annual electricity costs. This study presents innovative strategies and methodologies for optimizing tunnel lighting design, offering valuable insights for future tunnel construction and lighting design endeavours. [ABSTRACT FROM AUTHOR]

Road tunnels might be exposed to Boiling Liquid Expansion Vapor Explosion (BLEVE) due to the transportation of liquified gas tankers passing through road tunnels. An efficient and accurate prediction of the response of road tunnels under internal BLEVEs can facilitate the reliable BLEVE-resistant design and risk assessment of road tunnels. This study introduces an advanced deep-learning model that employs a Transformer-based architecture with a modified self-attention mechanism, termed as Self-Attention Modified Transformer (SAMT), to predict BLEVE-induced support rotation of tunnel structure, which is a common criterion in assessing reinforced concrete structure damage to blast loads. Unlike the Transformer with the traditional self-attention mechanism, the proposed SAMT effectively aggregates global information across all variables while mitigating undue dependencies among uncorrelated variables. Consequently, the proposed SAMT is better suited for processing tabular data with uncorrelated variables. The feasibility and advantages of the proposed SAMT are verified by extensive data generated using calibrated numerical models of box-shaped road tunnels subjected to internal BLEVEs. By comparing the performance of the proposed SAMT with the non-modified Transformer network (FT-Transformer) as well as two other typical deep learning networks, i.e., Multi-layer Perceptron (MLP) and Residual Network (ResNet), it is found that the SAMT offers higher prediction accuracy and robustness than the other three models in predicting BLEVE-induced support rotations of box-shaped road tunnels. The study demonstrates that the proposed SAMT is an effective tool for the prediction of BLEVE-induced support rotations of road tunnels. • An improved Transformer-based deep learning model (SAMT) is proposed. • The SAMT features a modified self-attention mechanism. • BLEVE-induced response of road tunnel is predicted by using SAMT. • Advantages of the SAMT model are demonstrated. [ABSTRACT FROM AUTHOR]