“Travelling fires” discriminate a fire plume at the near-field and a hot smoke layer preheating the ceiling at the far-field, with the intent of ensuring the robustness of structural design for large compartments under realistic fires. Once the fire is “travelling”, the near-field has a leading edge representing the fire spread, and a trailing edge representing the burnout of the fuel. Despite the recognised effects of travelling fires, the mainstream of efforts into their effect on structural response has been limited to 2D models using the finite element method (FEM). This paper aims to identify the importance of slab inclusion with a 3D FEM structural model for steel-composite structures under travelling fires, assessed against the corresponding simplified 2D structural frame models (i.e., with and without effective slab in the 2D steel frame model). The first step is a comparative structural analysis of a prototype composite structure under various design fire scenarios, including standard fire, parametric fires and travelling fires. The role of the fire protection scheme for the simplified 2D models against the 3D model for the numerical predictions is also explored. It is found that the structural load path, and the potential structural failure mechanisms, could be fundamentally different between the 3D model and the simplified 2D models. Although the 2D frame model tends to predict larger deflections (i.e., more conservative) than the 3D model, it could also significantly underestimate the large internal forces from the beams, so that the connections' failure under travelling fires might be overlooked. Further, due to the simplification of the 2D models in omitting the significant stiffness contribution from the slab and the adjacent structural components, the effect of the fire protection is likely to be amplified. This may give misleading information on the performance-based structural fire design under different travelling fire scenarios. Hence, the 3D model can be considered as feasible but also necessary for structural fire analysis for travelling fires as a complement to the simplified 2D model approach.

Cross-Laminated Timber (CLT) walls are crucial components of modern buildings, consisting of multiple layers of timber bonded together. However, as combustible construction materials, their potential fire risk remains a significant concern. The behaviour of CLT components during a fire is complex and requires careful consideration of both (a) the temperature-dependent behaviour of the timber layers and (b) potential chemical reactions (e.g., pyrolysis) within the wood. For (a), the Eurocode EN 1995-1-2 provides guidelines for assessing the fire resistance of timber structures. For (b), this paper applies a pyrolysis model within a Heat Transfer (HT) analysis framework to predict the CLT structural response under fire conditions. This paper introduces and demonstrates a One-Way Coupled (OWC) fire-structure simulation, which combines Computational Fluid Dynamics (CFD) and Finite Element Method (FEM) domains. Inspired by the standard fire test for CLT walls (Osborne et al. 2012), here, CFD is used to reproduce the ISO-834 standard fire as a preliminary demonstration case. Using the thermal data obtained from the CFD model as the boundary condition, a subsequent heat transfer analysis using Abaqus is able to predict pyrolysis and heat transfer behaviour, but it fails to represent the initial temperature distribution (which because of water evaporation is not considered) and capture post-failure behaviour. Additionally, a Structure Response (SR) analysis of the CLT wall under various mechanical loads indicated failures at different times during the fire. However, due to the lack specific information about experimental set-up in the literature, such as mechanical loads and material properties, future studies are planned to verify the model against experimental data.

Human pose estimation (HPE) is a crucial task in computer vision with extensive applications in healthcare, surveillance, and human–computer interaction. Traditional HPE research primarily utilizes RGB cameras, which may suffer from poor performance under varying lighting conditions and raise privacy concerns. Recently, millimeter-wave (mmWave) radar technology has emerged as a promising alternative, providing a non-invasive and privacy-preserving solution for HPE. However, the progress in mmWave-based HPE is hindered by the limited availability of high-quality datasets that encompass a diverse range of poses and provide accurate data annotations. Current mmWave-based datasets for HPE often feature only basic poses or rely on imprecise annotations, typically derived from pre-trained image-based HPE models using synchronized RGB images, which can limit the potential of derived models. This study introduces a pioneering approach to HPE by synergizing wearable motion capture sensors with mmWave radar technology to create a comprehensive and precise dataset tailored for enhancing HPE with mmWave radar. Leveraging this dataset, we develop an innovative deep learning framework specifically designed to explore the unique properties of radar signals for HPE. The performance of our proposed model is evaluated and compared with several well-known deep learning models. Extensive experimental results affirm the robustness of the dataset, establishing it as a rigorous benchmark for mmWave radar-based HPE. The proposed methodology demonstrates exceptional accuracy in estimating human poses from radar data, setting the stage for its application in environments where privacy and complexity are critical concerns.

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.

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.

The diagrid structural system has seen significant uptake in medium to high rise buildings because of the architectural and resource advantages that it provides. These arise mainly as a result of flexibility in the topology that this particular structural solution provides. However, as a result of the way in which diagrids carry both horizontal and vertical loading, the diagrid structure itself may be susceptible to fire in ways which are not immediately obvious on the basis of our understanding of more traditional rectilinear construction forms. This study addresses this to improve our understanding of diagrid structures' response to fire. A comprehensive structural analysis on 45 fire load cases is conducted, considering different fire locations and sizes, using parametric design tools and finite element analysis software. The results provide valuable insights into the load redistribution and collapse mechanisms of diagrid structures in fire conditions.

This study analyses the structural response of an aluminium reticulated roof structure that is constructed at Sichuan Fire Research Institute (Sichuan, China), and to be tested in fire. The structural fire behaviour under 960 localised fire scenarios is considered first, and then used to construct a database for training a modular artificial intelligence (AI) system for real-time forecasting. The system consists of several AI models, each of which predicts the displacement at a specific monitoring point. These individual predictions are then combined to generate a comprehensive forecast of the global structural-fire behaviour. The individual AI model utilized is a Long Short-Term Memory Recurrent Neural Network (LSTM RNN). The modular design allows different models to be modified or added as needed, making the system flexible and adaptable, and improving the accuracy and reliability of the predictions. The results demonstrate the effectiveness of the modular AI approach in accurately forecasting fire-induced structural collapses as indicated by the sensitivity the local models can have. The key objective of this research is to help to make informed decisions and prioritize efforts to minimize the risk of structural collapse in fire.

Large open-plan compartment fires in modern buildings may exhibit a local burning region travelling across the floor plan as a ‘travelling fire’. This phenomenon has been found in the forensic investigations of fire accidents and in the large compartment fire tests. The fire impact in a large compartment is spatially non-uniform and time-variant, which can cause severe local damage to structural components. Advanced from the previous models assuming constant travelling, the natural fire model established in this paper comprises time-variant and test-based travelling behaviour models and localised fire models of various modes. It is demonstrated with the fast-spread Veselí fire test and the slow-spread Malveira fire test. A generic structural model is set up within OpenSees for fire to examine the thermal impact on structural members under various travelling fire scenarios of different travelling parameters, fire travelling directions, and beam sizes. Locally much higher thermal responses are represented after introducing behaviour models while adopting the same design fire load. Based on the work in this paper, a library of design fire models can be potentially enabled to examine the fire safety performance of structures regarding the realistic fire load and fire impact aiming for discovering unknown worse fire scenarios.

In performance-based structural fire engineering, “travelling fires” is being gradually accepted as an important fire boundary condition. However, its application is still limited by uncertainties in the selection of different design travelling fire parameters, resulting from the lack of relevant experimental data and corresponding validated structural finite element models which can be used with advanced travelling fire methodologies, e.g. the Extended Travelling Fire Methodology (ETFM) framework. This paper aims to fill this gap through modelling a prototype steel-composite floor structure (representing a “slice” of a large open-plan office), to investigate its true structural response under a wide range of travelling fire scenarios, with an emphasis on considering the effect of concrete slab in a 3D finite element model, using LS-DYNA. To ensure the credibility of this numerical study, the model was first validated against the experimental data of the structural response from the Veselí Travelling Fire Test. In the parametric studies, 32 cases were examined to investigate the thermal and structural response, related to the selection of key design parameters for travelling fires (i.e. fire spread rates, fuel load densities and inverse opening factors (IOF)); fire protection (i.e. different fire protection schemes and required fire resistance rating (FRR)), and the effect of slab specification (i.e. thicknesses and steel reinforcements). It was found that solely satisfying the critical temperature and deflection criteria for the structural members might not guarantee a sufficient structural design for travelling fire scenarios, and it is suggested that the steel stress utilisation should also be examined. Compared with the IOF, it appears that the selection of fire spread rates and fuel load densities are likely to be more critical in identifying the worst travelling fire scenario for the structural response with fire protection. Moreover, the global structural response under travelling fire is also affected by the combination of fire protection (i.e. equivalent FRR in this paper) and fire spread rate. Under a “slow” travelling fire (e.g. 0.5 mm/s) with increasing FRR, the failure of structural elements during the cooling phase was prevented effectively; however, under a relatively “fast” travelling fire (e.g. 2.5 mm/s, 12.5 mm/s), increasing FRR may not always improve the fire performance of the structure. This work also indicates that steel reinforcement ratio has a greater influence on structural response than slab thickness under travelling fires. Furthermore, the 3D finite element model is very important for structural fire analysis, not only due to the more conservative internal force captured by the 3D model (i.e. reduced by over 80 % on the 2D model in our case) thereby reproducing the collapse triggered by the failure of the connection under fire in general, but also the 3D model was able to better represent the deflection and the “internal force reversal” caused by travelling fires.