High traffic flow in a confined tunnel makes fire safety a critical issue. This paper proposed a digital twin framework for tunnel fire safety management in real-time, driven by dynamic sensor data and AIoT technologies. A deep learning model trained by the Transformer network and simulation dataset is used to predict real-time fire location and size. Then, the AI model is integrated into a 3D digital twin platform developed by the game engine Unity 3D. The performance of the proposed digital twin framework is demonstrated using numerical experiments and large-scale tunnel fire tests. Results show that the established AI model achieved promising accuracy in predicting fire location and power for both numerical and experimental data. The digital twin platform can also visualize the 3D fire scene that supports evacuation, firefighting, and emergency rescue. This research demonstrates the feasibility of using a 3D environment and digital twin in real-time fire safety management.

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Fire accidents in buildings are occurring and claiming thousands of lives each year. Due to various architectural designs, fire hazards would be unique to each building layout. This paper discusses how fire hazard varies with the arrangement of the fuel inside buildings. To comprehensively present the effect of fuel distribution on fire behaviour, results from large-scale experiments, bench-scale experiments, empirical correlations, and numerical studies are provided. In large-scale fire tests, two different cases of wood cribs were tested to demonstrate the effects of porosity on heat generation and fire spread behaviour. Due to the limitations of experimental conditions, the variation in heat release rate attributable to differences in fuel porosity and surface area has been also qualitatively investigated using a cone calorimeter test. To bring the gap between experimental observations and real-word scenarios, a numerical study is also performed. This study further explores the effects of fuel distribution (considering porosity and surface area of fuel throughout the compartment) and ventilation on fire spread beyond the fire compartment. The computational fluid dynamics (CFD) simulations show how the distribution of fuel in different ways can lead fire to spread beyond its origin, as observed in many fire accidents. The paper suggests that designers should consider such critical fire scenarios in performance-based design.@en

The ‘travelling fire’ models have been used to describe the localised and travelling burning of uniform fuel bed in large open-plan building space. However, fuel is typically distributed non-uniformly in the built environment, leading to complex fire spread behaviours. This paper investigates the effect of non-uniform fuel load distribution on fire development in a sufficiently-ventilated space. A series of fire tests up to 3.5 MW with different wood crib layouts are categorised into two types, i.e., non-uniform and continuous, and non-uniform and discontinuous. The leading and trailing edges of the flame, height of flame, and fire spread rates are estimated using visual evidence. The non-uniform fuel load distribution fundamentally changes the spreading behaviour of fire. On a continuous wood crib, the fire spread rate and fire size are generally proportional to the fuel load density when the arrangement of the wood crib is similar. However, when wood cribs are discontinuous, the fire dynamics depend more on the localised burning size and gaps between fuels. Furthermore, very distinct fire behaviours were observed for fuel loads with different porosity. This work reveals the possible under-estimation of fire hazards of assuming evenly distributed fuel load and suggests considering design fire scenarios of non-uniform fuel load distribution in the performance-based fire safety design.

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First respondents to fires in structures face severe risks as both the fire and structural behaviour are unpredictable. While structural collapse may manifest some warning signs, these signs are not always easily identified which has led to the death of many fire fighters over the years. Both fire and structural fire simulation have come a long way and are now capable of assessing the thermomechanical behaviour of structures to a good degree of accuracy. However, such simulations take hundreds or thousands of engineering and computation hours. This paper explores performing these analyses a priori and using the generated database to train a recurrent neural network for real time prediction of potential failure. The analysis is performed on an aluminium reticulated roof structure that is constructed in Sichuan Fire Research Institute (Sichuan, China) and is expected to be tested to failure in fire in 2023. One hundred localised fire scenarios were used to cover the potential fire that will be used to induce the failure of the test roof. Heat transfer analyses for each section were then performed in OpenSEES followed by thermomechanical analysis in the same software. The generated results database was then cleaned and the data at several key locations were extracted and used to train a long short term memory recurrent neural network. The results of the predictions show that the artificial intelligence model can infer results with increasing accuracy the closer the structure is to failure. The real test of the accuracy of the model, however, will be during the fire experiment on the real structure. This would be the first time an artificial intelligence model for rapid forecasting of structural response in fire is built a priori and tested against a real fire.@en