Many survivors of traumatic injuries (serious nervous, visceral, bone, and vascular damage due to motor vehicle collisions, falls, sports injuries, electrocution, and fires) also experience mental health issues such as emotional distress, anxiety, and depression. It has been widely accepted that the integration of peers into psychosocial interventions for traumatic injury survivors would provide similar outcomes as cognitive behavioral therapy, while having the benefit of peer lived experience. However, a peer support intervention for trauma survivors has yet to be trialed for feasibility and implemented across the care continuum. Thus, we collaborated with trauma survivors, caregivers, and healthcare professionals (HCPs) to co-design a cross-continuum peer support program (PSP) that we are ready to pilot. The purpose of the study is to (1) assess the acceptability, feasibility, and satisfactoriness of the PSP; (2) evaluate the preliminary impact of the PSP on trauma patients' health and social outcomes; and (3) explore the impact of the PSP on clinical practice, administrative procedures, and patient care. Feasibility will be determined based on study recruitment, adherence, and retention. Participants will complete outcome surveys at baseline, 1-month post-intervention, and 3-month follow-up. A Repeated Measures ANOVA will be exploratorily used to assess outcome scores at each time point. Qualitative interviews will be completed after the study to explore the acceptability, satisfactoriness, and feasibility of the program. HCPs and TPSs will participate in focus groups to provide insight into the administrative, clinical, and patient care impacts of the PSP. We plan to recruit n = 8-10 Trained Peer Supporters; n = 30 trauma survivors (n = 10-25 to participate in post-intervention qualitative interviews); and n = 15 Health Care Providers (HCPs). Based on our existing data and supporting literature, there is strong evidence for the need for a PSP that spans the care continuum and is led by individuals with injury experience. Similar studies conducted with other rehab populations have seen great success with peer support initiatives including the amelioration of psychosocial health and well-being outcomes. Our feasibility trial has the potential to demonstrate that a cross-continuum PSP for trauma survivors is not only feasible, but also successful in reducing the potential adverse psychosocial health outcomes related to traumatic injuries. This trial was registered with ClinicalTrials.gov, registration number NCT07275892, on December 10th, 2025.

Air curtain systems have been proposed as a supplementary smoke control strategy for vehicle tunnels, particularly where structural constraints limit the installation or upgrading of conventional ventilation systems. However, most previous studies rely on numerical simulations or fixed experimental facilities, while flexible experimental platforms and the influence of vehicle obstruction on smoke behavior remain less explored. This study experimentally investigates the smoke confinement performance of an air curtain using a 1:18 modular detachable scaled vehicle tunnel model. The modular configuration enables flexible assembly and adjustment of the experimental setup for different test conditions. A series of laboratory experiments was conducted using a liquefied petroleum gas (LPG) burner to simulate a vehicle fire. Temperature measurements and smoke visualization were performed under different air curtain jet velocities and vehicle obstruction conditions to analyze the interaction between the air curtain jet and buoyancy-driven smoke flow. The results show that the air curtain significantly restricts the upstream propagation of hot smoke and modifies the thermal field inside the tunnel. When the jet velocity reached approximately 5 m/s, the temperature in the protected region decreased by about 25–35% compared with the case without an air curtain. In addition, the presence of vehicle models altered the airflow structure and increased heat accumulation in the middle region of the tunnel cross-section. These results demonstrate that the proposed modular tunnel model provides a reliable experimental platform for tunnel fire research and highlights the importance of considering vehicle obstruction effects in tunnel smoke control studies.

Battery pack jet fire modeling and vehicle fire spread simulation in Ro-Ro ships

YOLOFlame: Benchmarking vehicle fire and smoke detection in urban environments

Electric vehicle (EV) adoption is rapidly increasing in Australia as well as around the world, as it is supporting national decarbonisation goals. Despite this, the battery used in EVs poses a severe fire risk when subjected to physical damage. The garage of the timber-framed house is highly prone to suffering from this unique and severe fire. As the garage’s walls were designed based on the deemed-to-satisfy criteria specified by the fire resistance level (FRL), which follows the standard fire curve. However, an EV fire is non-linear and initiated by a violent thermal runaway, making it unpredictable, and these conditions were not accounted for in the traditional fire safety design approaches for timber-framed walls. This study assesses the FRL of timber-framed walls subjected to EV fires through the validated fire dynamic simulation. A timber-framed wall panel and the walls attached to the representative house garage were subjected to twelve different EV fires. Results show that the FRL of the timber-framed wall attached to the garage declines abruptly within 15 min in both structural adequacy and integrity, leading to early structural compromise and increased fire spread, ultimately resulting in both structural and thermal insulation failure under EV fires. These findings indicate that the fire resilience of the current timber-framed house design is inadequate in the event of EV fires, highlighting the need for improvements in design and construction regulations.

Study on the Smoke and Toxic Gas from Vehicle Fires on the Lower Lane of a Double Deck Steel Bridge

In electric vehicle (EV) charging systems, DC series arc faults, due to their high concealment and severe hazard, have become one of the important causes of electric vehicle fire accidents. An improved hybrid arc fault model of a charging system was established in Simulink for preliminary study. The results show that the high-frequency noise generated by arc faults affects the output voltage quality of the charger, and this noise is conducted to the battery voltage. Arc faults in a real electric vehicle charging experimental platform were further investigated, where it was found that, during arc fault events, the charging system provides no alarm indication, and the current signals exhibit significant large-amplitude random disturbances and nonlinear fluctuations. Moreover, under normal conditions during vehicle charging startup and the pre-charge stage, the current waveforms also present high-pulse spike characteristics similar to arc faults. Finally, a carefully designed deep neural network-based arc fault detection algorithm, Arc_TCNsformer, is proposed. The current signal samples are directly input into the network model without manual feature selection or extraction, enabling end-to-end fault recognition. By integrating a temporal convolutional network for multi-scale local feature extraction with a sparse Transformer for contextual information aggregation, the proposed method achieves strong robustness under complex charging noise environments. Experimental results demonstrate that the algorithm not only provides high detection accuracy but also maintains reliable real-time performance when deployed on embedded edge computing platforms.

With the rapid expansion of electric vehicles (EVs), the probability of fires in underground parking lots and the risk of complex damage have increased, necessitating the establishment of systematic and priority-based response strategies. Previous studies utilizing full-scale experiments simulating EV fires in underground parking lots confirmed that overhead sprinkler systems alone can effectively block the spread of fires to adjacent vehicles. This study analyzes the relative importance of EV fire response strategies derived from focus group interviews (FGIs) with four experts. Survey results from 38 relevant experts showed a consistency ratio (CR) of 0.027, indicating high reliability. Among upper-level factors, life safety and evacuation strategies were evaluated as the most critical with a weight of 0.410. In the global weight analysis of the sub-factors, emergency alarm system (EAS) recorded the highest priority (0.171). Additionally, the evacuation route design (ERD) and fire detection speed (FDS) were emphasized as key elements. These results align with previous findings, suggesting that response systems centered on life safety should be prioritized. Furthermore, this study demonstrates that overhead sprinkler systems can serve as critical facilities for the initial response to EV fires. The findings of this study provide foundational evidence for the development of policies and technical strategies related to EV fire response.

The article is devoted to the analysis of certain aspects of legal support for the safety and sustainability of underground parking lots as dual-purpose structures (parking vehicles in peacetime and sheltering the population during emergencies) in Ukraine against the backdrop of ongoing armed aggression. The current state of civil protection shelters, key risks (vehicle fires, limited ventilation, insufficient shock wave resistance, and evacuation problems), and the regulatory framework of Ukraine (DBN V.2.2-5:2023, DBN V.2.3-15:2007) are analyzed. The article presents the results of a comparative analysis with European standards (Directive 2004/54/ EC for road tunnels, EN 12845 for sprinkler systems, EN 12101 for smoke extraction). The research methodology includes a comparative analysis of standards, CFD risk modeling, site surveys, and expert surveys. An analysis of the content of the DBN safety standards made it possible to identify key requirements for dual-use facilities that are relevant to dual use in general – ventilation, airtightness, evacuation, design, and autonomy. Hybrid models for adapting EU standards are proposed, including structural reinforcement of buildings, zoning of space, integration of fire extinguishing systems, and automated sealing. The conclusions confirm the economic and technical feasibility of adaptation to improve resilience without significant costs, with recommendations for updating building codes and international cooperation.

The application of condensed aerosol (CA) as a fire suppression system for motor vehicles has increasingly been promoted as an alternative to conventional streaming agents. However, such equivalence raises fundamental conceptual problems. Streaming agents suppress fire through high-momentum, directional discharge enabling rapid localized fire attack, whereas condensed aerosol operates through volumetric dispersion of submicron particles and is highly dependent on spatial confinement and the achievement of homogeneous agent concentration. This study aims to scientifically analyze the inherent inability of condensed aerosol to function as a streaming agent within vehicle engine compartments, which are semi-open and characterized by high ventilation rates. The research adopts an analytical–qualitative approach integrating particle fluid mechanics, thermodynamics of pyrotechnic aerosol systems, combustion reaction kinetics, and a systematic review of international standards including NFPA 2010, ISO 15779, and LPS regulations. The analysis is reinforced by a synthesis of Scopus-indexed experimental studies, Computational Fluid Dynamics (CFD) simulations, and reviews of real-world fire incident cases. The results demonstrate that condensed aerosol intrinsically lacks flow momentum, throw range, and penetration capability required for effective vehicle fire suppression. Its performance is deterministically degraded by ventilation effects, making suppression failure scientifically predictable. Conversely, condensed aerosol exhibits effective performance only in small, well-confined, and static compartments such as electrical panels and control cabinets, where a total flooding mechanism based on volumetric concentration can be consistently achieved. These findings are fully consistent with international regulatory classifications that restrict condensed aerosol to fixed total flooding systems for small enclosed spaces. This study concludes that the application of condensed aerosol in motor vehicles represents a categorical design error in fire suppression systems.

Electric vehicles (EVs) are widely recognized as a key strategy for improving global sustainability; however, their implications for building safety, particularly under fire conditions, require further investigation. This study examines the structural response of reinforced concrete (RC) beams exposed to EV fire scenarios, which are characterized by more severe thermal demands than the ISO 834 standard fire curve adopted in structural fire design, including EN 1992-1-2. A coupled thermal–mechanical finite element analysis (FEA) was performed on nine RC beams, considering variations in reinforcement layout, rebar diameter, and concrete cover thickness. When compared with fire resistance times predicted by standardized design procedures, the numerical results indicate that EV fires accelerate building damage by up to 27% within the first 60 min of exposure. Increasing the concrete cover to at least 30 mm and adopting multiple reinforcement layers were shown to enhance fire performance by reducing heat transfer to the steel reinforcement and lowering stress levels within the cross section. The findings demonstrate that current fire design provisions may underestimate the structural demands imposed by EV fire scenarios. Consequently, this study highlights the need to revise fire resistance criteria and reinforcement detailing rules to ensure adequate safety and resilience of RC structures in sustainable built environments subjected to emerging EV fire hazards.

Thermal runaway (TR) of lithium-ion batteries caused by electrical, thermal, and mechanical abuse is a primary contributor to electric vehicle (EV) fires. To address the unclear propagation mechanisms and hazard characteristics of thermal runaway fires in full-scale EV battery packs, a comprehensive thermal runaway fire test on battery pack was conducted in this study. The experimental results revealed that TR propagated from individual cells to adjacent cells, ultimately engulfing the entire pack. The combustion dynamics of battery packs differed significantly from those of individual cells, exhibiting rapid fire escalation and complex combustion-explosion behaviors. A thermal radiation prediction model was established based on experimental data, according to the thermal radiation prediction model, the thermal radiation intensity of the battery pack at different distances can be obtained, so that the safe distance between various types of equipment and the electric vehicle can be determined, providing critical insights for designing safety distances and suppressing TR propagation in battery systems of EV.

Effect of fine water mist system layout on electric vehicle fire suppression in underground garages

ABSTRACT Vehicle fires in underground parking structures can cause rapid temperature rises, posing significant threats to both structural integrity and occupant safety. This study develops predictive models for quantitatively estimating fire temperatures in single‐vehicle fire scenarios within underground parking structures. A total of 25 single‐vehicle fire test datasets were collected, and the maximum heat release rate, time to reach maximum heat release rate, and duration of maximum heat release rate were analyzed for each vehicle type. Based on the analysis results, various fire scenarios were constructed, and fire simulations were performed to derive time–temperature curves for each fire scenario. From the simulated curves, regression‐based and artificial neural network (ANN)‐based prediction models were developed to estimate the peak temperature and the temperature at the end of combustion for each fire scenario, using heat release rate as input variables. The predictive performance of both models was validated against the fire simulation results, with coefficients of variation below 0.2 and coefficients of determination above 0.84, indicating high accuracy. Furthermore, to examine the applicability of the proposed time–temperature curves to structural fire analysis, nonlinear finite element analysis of reinforced concrete beams was performed for fire resistance assessment. The results showed that the structural fire behavior predicted using the regression and ANN‐based curves closely matched that obtained using fire simulation‐based curves. The proposed temperature prediction models are expected to be effective tools for estimating thermal loads and evaluating the fire safety of structures subjected to single‐vehicle fires in underground parking facilities.

Over time, the burning behaviour of passenger vehicles has been influenced by continuous advancements in vehicle design, including changes in materials, manufacturing processes, and the integration of modern lithium-ion battery powertrains. Fire protection engineers, first responders, and other safety professionals currently lack sufficient data to determine whether existing fire protection system designs and firefighting practices effectively mitigate the burning behaviour of modern vehicle fires or to adapt these approaches to any measurable changes in fire dynamics. Eighteen full-scale vehicle fire experiments were conducted in a laboratory environment to obtain a novel data set describing the fire size, thermal hazards, and evolved smoke and particulate species resulting from vehicle fires. Vehicle mass, gas temperature, heat flux, sorbent tube, Fourier-transform infrared spectrometer, and suppression waterflow data are provided in tabular format. Eight vehicle models across six manufacturers were selected based on the most popular battery electric and gasoline powered compact and mid-sized recently sold in North America at the time of writing. Experiments consisted of nine free-burn experiments wherein the gasoline vehicles were ignited in the engine compartment and the electric vehicle battery packs were induced into thermal runaway; these fires progressed unabated until the occurrence of natural flame extinction. Another four experiments simulated electric vehicle fire suppression using ordinary water and traditional vehicle fire suppression techniques. In one additional experiment, an encapsulator firefighting agent was added to the water. The final four experiments consisted of deploying fire blankets over the burning vehicles a single strategy or in combination with water simultaneously applied from beneath. There are many potential uses of this data, but primary uses are expected to include revision of design criteria and guidance within the fire protection engineering community, strategic and tactical decision aids for vehicle fire incident operations, validation of existing (and development of new) fire behavior models, and guidance to vehicle manufacturers for improved fire safety design.

Experimental and numerical study on heat flux distribution and temperature response of cable surface in suspension bridges under vehicle fires

How are major electric vehicle fires associated with purchase Intention? The moderating role of EV ownership experience in South Korea

Study on the temperature variation characteristics of subway vehicles interior fire based on realistic scenarios

This study evaluated the performance of YOLOv11 models trained to detect electric vehicle (EV) fires and smoke emission using bounding-box and instance segmentation annotations. Comprehensive experiments were conducted across all YOLOv11 variants (n, s, m, l, x) to analyze the trade-offs between speed and detection accuracy. The models were trained on a dataset consisting of 3,000 images depicting EV fires and smoke, which were annotated using Roboflow’s polygon tool under identical training conditions with consistent hardware and hyperparameters. The evaluation was based on precision, recall, and mean average precision at an intersection over union threshold of 0.50 (mAP50), as well as mAP50-95, F1-score, and inference speed in frames per second. The experimental results showed distinct advantages for each annotation method. Bounding-box models demonstrated superior inference speed, with the YOLOv11n model achieving the highest speed at 44.62 FPS. This makes these methods optimal for real-time EV fire monitoring applications. By contrast, segmentation models showed significantly higher accuracy in detecting objects with irregular boundaries, particularly smoke plumes. The YOLOv11m model with segmentation achieved optimal performance with a mAP50 of 0.7952 and an F1-score of 0.7510. The segmentation approach proved particularly effective for smoke detection, where bounding-box annotation frequently failed to accurately capture the irregular boundaries of smoke in an image, which resulted in detection failures for these critical fire indicators. These findings demonstrate that segmentation labeling is more effective in achieving accurate early detection and precise localization of EV fires and smoke emissions, despite the computational trade-off. The superior boundary definition capability of the segmentation approach is crucial for reliable smoke detection, which provides an important early warning of thermal runaway events in EV batteries. Future studies will focus on expanding the diversity of the dataset used to train the models by applying generative AI techniques such as GAN and diffusion models to further enhance detection robustness across varied fire scenarios.

This study examined the applicability of Raman LiDAR as a non-contact and remote technique for the detection of hydrogen gas released during lithium-ion battery fires in electric vehicles (EVs). A thermal runaway phenomenon was induced in the battery cells, and the hydrogen off-gas was continuously monitored. The results showed that approximately 1,400 ppm of hydrogen was detected within 2-3 min of ignition, and the concentration increased to a maximum of 4,850 ppm as combustion progressed. Distinct Raman signals for hydrogen were observed at approximately 390 nm, while Rayleigh scattering at 371 nm provided complementary insights into combustion stages and gas release behavior. These findings demonstrate that EV battery fires are not merely thermal events but also involve the release of large amounts of hydrogen, posing additional hazards. The proposed Raman LiDAR technique effectively overcomes the limitations of conventional contact-based sensors in open environments and enables the real-time monitoring of hydrogen under high-risk conditions. This study provides fundamental insights into strategies for enhancing EV fire safety and contributes to future efforts in safety management and standardization of hydrogen-based infrastructures.