Search Results (141)

This article discusses the possibilities and methods for rescuing people from abandoned mine workings and the cooperation of the components of the Integrated Rescue System of the Slovak Republic when carrying out rescue work in underground spaces, specifically the Bankov mine. Additionally, the basic legislative restrictions on the level of rescue work that can be performed in underground spaces in Slovakia and abroad are characterized. In the study itself, exercises in a mining environment were designed and tested by rescuers from the fire and rescue corps of the Slovak Republic, while several methods for rescuing people from underground spaces using climbing equipment were tested. Since the research setting was an abandoned mine, the rescue methods were carried out with regard to the maximum achievable safety of the firefighters. With the demise of the Mine Rescue Service in the Slovak Republic in 2025, rescue activities passed into the hands of the fire and rescue corps, and it is therefore necessary to determine the best method for rescue from mining spaces that can be performed by firefighters when the priority is the rescue time: the most important factor for saving human life. Using the analysis of the data obtained in this study, the most effective method specifically for rescuing people from underground spaces was determined. Based on the information obtained, proposals and measures were established to make rescue work in underground spaces more efficient. The research met all standards set for firefighters, and all rescuers agreed to publish this research. Full article

Ventilation in subway and railway tunnels is a critical safety component, especially during fire emergencies, where effective smoke and heat management is essential for successful evacuation and firefighting efforts. The Subway Environmental Simulation (SES, Version 4.1) model is widely used for predicting airflow and thermal conditions during fire events, but its accuracy in real-world applications requires validation. This study compares SES predictions with experimental data from the Memorial Tunnel fire ventilation tests to evaluate its performance in simulating the effects of jet fans on longitudinal ventilation. The analysis focuses on SES’s ability to predict flow rate and temperature distributions. Results showed reasonable agreement between SES-predicted airflows and temperatures. However, SES tended to underpredict temperatures upstream and near the fire source, indicating a limitation in simulating thermal behavior close to the fire. These findings suggest that SES can be a reliable tool for tunnel ventilation design if certain safety margins, based on the error values identified in this study, are considered. Nonetheless, further improvements are necessary to enhance its accuracy, particularly in modeling heat transfer dynamics and the impact of fire-induced temperature changes. Future work should focus on conducting additional full-scale test validations and model refinements to improve SES’s predictive capabilities for fire safety planning. Full article

The movement towards low-emission and sustainable building practices has driven increased use of natural, carbon-based materials such as wood. While these materials offer significant environmental advantages, their inherent flammability introduces new challenges for timber building safety. Despite advancements in fire protection standards and building regulations, the risk of fire incidents—whether from technical failure, human error, or intentional acts—remains. The rapid detection of fire onset is crucial for safeguarding human life, animal welfare, and valuable assets. This study investigates the potential of monitoring fire precursor gases emitted inside building structures during pre-ignition and early combustion stages. The research also examines the sensitivity and effectiveness of commercial smoke detectors compared with custom sensor arrays in detecting these emissions. A representative structural sample was constructed and subjected to a controlled fire scenario in a laboratory setting, providing insights into the integration of gas sensing technologies for enhanced fire resilience in sustainable building systems. Full article

The growing popularity of Personal Light Electric Vehicles (PLEVs), such as e-scooters, has revolutionized urban mobility by offering compact, cost-effective, and environmentally friendly transportation solutions. However, safety concerns, including inadequate infrastructure, poor protective measures, and high accident rates, remain critical challenges. This paper presents the design and development of an innovative self-balancing microvehicle under the H2020 LEONARDO project, which aims to address these challenges through advanced engineering and user-centric design. The vehicle combines features of monowheels and e-scooters, integrating cutting-edge technologies to enhance safety, stability, and usability. The design adheres to European regulations, including Germany’s eKFV standards, and incorporates user preferences identified through representative online surveys of 1500 PLEV users. These preferences include improved handling on uneven surfaces, enhanced signaling capabilities, and reduced instability during maneuvers. The prototype features a lightweight composite structure reinforced with carbon fibers, a high-torque motorized front wheel, and multiple speed modes tailored to different conditions, such as travel in pedestrian areas, use by novice riders, and advanced users. Braking tests demonstrate deceleration values of up to 3.5 m/s², comparable to PLEV market standards and exceeding regulatory minimums, while smooth acceleration ramps ensure rider stability and safety. Additional features, such as identification plates and weight-dependent motor control, enhance compliance with local traffic rules and prevent misuse. The vehicle’s design also addresses common safety concerns, such as curb navigation and signaling, by incorporating large-diameter wheels, increased ground clearance, and electrically operated direction indicators. Future upgrades include the addition of a second rear wheel for enhanced stability, skateboard-like rear axle modifications for improved maneuverability, and hybrid supercapacitors to minimize fire risks and extend battery life. With its focus on safety, regulatory compliance, and rider-friendly innovations, this microvehicle represents a significant advancement in promoting safe and sustainable urban mobility. Full article

Mycelium-based composites (MBCs) are an emerging category of cost-effective and environmentally sustainable materials that are attracting significant research and commercial interest across various industries, including construction, manufacturing, agriculture, and biomedicine. These materials harness the natural growth of fungi as a low-energy bio-fabrication method, converting abundant agricultural by-products and waste into sustainable alternatives to energy-intensive synthetic construction materials. Their affordability and eco-friendly characteristics make them attractive for both research and commercialisation. Currently, mycelium-based foams and sandwich composites are being actively developed for applications in construction. These materials offer exceptional thermal insulation, excellent acoustic absorption, and superior fire safety compared to conventional building materials like synthetic foams and engineered wood. As a result, MBCs show great potential for applications in thermal and acoustic insulation. However, their foam-like mechanical properties, high water absorption, and limited documentation of material properties restrict their use to non- or semi-structural roles, such as insulation, panelling, and furniture. This paper presents a comprehensive review of the fabrication process and the factors affecting the production and performance properties of MBCs. It addresses key elements such as fungal species selection, substrate choice, optimal growth conditions, dehydration methods, post-processing techniques, mechanical and physical properties, termite resistance, cost comparison, and life cycle assessment. Full article

The composite materials industry is increasingly seeking sustainable alternatives to mitigate the environmental impact of end-of-life materials. As a result, many sectors are transitioning toward bio-based or partially bio-based matrices (e.g., epoxy resins) to preserve material properties while improving sustainability. The transportation sector, in particular, demands materials that meet stringent mechanical and fire resistance standards. In this study, various epoxy systems with bio-based and/or recyclable content were investigated, along with renewable additives designed to enhance fire resistance through their functional groups and chemical structure. The research focused on developing formulations compatible with Sheet Moulding Compound (SMC) technology, which is widely used in transportation applications. Through extensive testing, materials with high bio-based content were successfully developed, exhibiting competitive mechanical properties and compliance with key fire safety requirements of the railway sector, as per the EN 45545-2 standard. Full article

This study presents the design and validation of an integrated fire safety system that leverages edge AI, hybrid sensing, and precision suppression to overcome the latency and collateral limitations of conventional smoke detection and sprinkler systems. The proposed platform features a dual-mode sensor array for early fire recognition, motorized ventilation units for rapid smoke extraction, and a 360° directional nozzle for targeted agent discharge using a residue-free clean extinguishing agent. Experimental trials demonstrated an average fire detection time of 5.8 s and complete flame suppression within 13.2 s, with 90% smoke clearance achieved in under 95 s. No false positives were recorded during non-fire simulations, and the system remained fully functional under simulated cloud communication failure, confirming its edge-resilient architecture. A probabilistic risk analysis based on ISO 31000 and NFPA 551 frameworks showed risk reductions of 75.6% in life safety, 58.0% in property damage, and 67.1% in business disruption. The system achieved a composite risk reduction of approximately 73%, shifting the operational risk level into the ALARP region. These findings demonstrate the system’s capacity to provide proactive, energy-efficient, and spatially targeted fire response suitable for high-value infrastructure. The modular design and SmartThings Edge integration further support scalable deployment and real-time system intelligence, establishing a strong foundation for future adaptive fire protection frameworks. Full article

Assessing building fire safety risks during the early design phase is vital for developing practical solutions to minimize loss of life and property. This study aims to identify research trends and provide a guiding framework for researchers by systematically reviewing the literature on integrating machine learning-based predictive methods into building fire safety design using bibliometric methods. This study evaluates machine learning applications in fire safety using a comprehensive approach that combines bibliometric and content analysis methods. For this purpose, as a result of the scan without any year limitation from the Web of Science Core Collection-Citation database, 250 publications, the first of which was published in 2001, and the number has increased since 2019, were reached, and sample analysis was performed. In order to evaluate the contribution of qualified publications to science more accurately, citation counts were analyzed using normalized citation counts that balanced differences in publication fields and publication years. Multiple regression analysis was applied to support this metric’s theoretical basis and determine the impact levels of variables affecting the metric’s value (such as total citation count, publication year, and number of articles). Thus, the statistical impact of factors influencing the formation of the normalized citation count was measured, and the validity of the approach used was tested. The research categories included evacuation and emergency management, fire detection, and early warning systems, fire dynamics and spread prediction, fire load, and material risk analysis, intelligent systems and cyber security, fire prediction, and risk assessment. Convolutional neural networks, artificial neural networks, support vector machines, deep neural networks, you only look once, deep learning, and decision trees were prominent as machine learning categories. As a result, detailed literature was presented to define the academic publication profile of the research area, determine research fronts, detect emerging trends, and reveal sub-themes. Full article

Lithium-ion batteries (LIBs) are widely used as energy storage units in electric vehicles, mobile phones, and other electric devices due to their high voltage, large capacity, and long cycle life. Lithium-ion batteries are prone to thermal runway (TR), resulting in fires and explosions, which can seriously hinder the commercial development of LIBs. A series of safety methods has been studied to prevent TR of LIBs. The safety methods for suppressing TR in LIBs were reviewed, including safety equipment method, material modification method, thermal management method, and cooling method. The mechanism, advantages and disadvantages, and future applications of the TR suppression method are discussed. The effectiveness of the proposed safety method was evaluated through technical analysis and experimental testing, and the inhibitory effects of different safety methods on battery TR were summarized. The future trend of suppressing TR is discussed by summarizing and generalizing existing technologies for suppressing thermal runaway. This study provides a reference for exploring more effective methods to mitigate TR in the future. Full article

With the escalating frequency and intensity of global wildfires driven by climate change, fire retardants (FRs) have become essential tools in wildfire management. Despite their widespread use, the environmental safety of newer FR formulations—particularly in relation to aquatic ecosystems and developmental toxicity—remains insufficiently understood. In particular, their effects on fish embryos, which represent a sensitive and ecologically important life stage, are poorly characterized. This study investigated the acute toxicity of three commercially available FRs—N-Borate, N-Phosphate+, and N-Phosphate-—on early life stages of zebrafish (Danio rerio), based on an OECD 236 Fish Embryo Toxicity (FET) test. Notably, N-Phosphate- FR exhibited significant toxicity with a 96 h LC50 of 60 mg/L (0.0055%), while N-Borate (>432 mg/L, >0.032%) and N-Phosphate+ (>1181 mg/L, >0.08%) showed substantially lower toxicity. Sublethal effects, including reduced yolk sac absorption and yolk sac darkening, were observed across all FRs, highlighting potential developmental disruptions. The elevated toxicity of N-Phosphate- FR likely stems from its surfactant content. These findings reveal variations in the acute toxicity of different FR formulations, emphasizing the need for ecotoxicological assessments to guide the selection of safer FRs for wildfire management and to protect aquatic biodiversity. The results highlight the importance of incorporating developmental endpoints in FR risk assessments and provide foundational data for regulatory decisions regarding FR application near aquatic habitats. Further research is necessary to elucidate the mechanisms underlying observed effects and to evaluate cross-species toxicity at environmentally relevant concentrations. Full article

Fire is a combustion reaction where fuel reacts with oxygen in the presence of heat, releasing energy as light, heat, and flames. The main components of fire are fuel, oxygen, and heat. All three components must be present to cause a fire. Fire is a significant threat to residential and commercial buildings, often intensified by high fuel content in building materials such as wood and synthetics. This paper summarizes fire types and damages, loss of property and life, fuel content in building materials, and a method to reduce fire risk by minimizing the building material’s fuel content. This method uses minerals (coal combustion residual (CCR)), primarily inorganic oxides bonded with a small percentage of polyurethane binder, to manufacture a composite material moldable into multiple building products. The composite was tested as per the ASTM for mechanical, thermal, and fire safety performance. ASTM D635-based fire testing showed self-extinguishing behavior with significantly reduced burn rate and lengths (1–2 mm). A low calorific value of 6.6 MJ/kg was determined separately. The test results demonstrate that CCR-based mineral composites offer a fire-resistant, structurally sound, and eco-friendly alternative to wood products. This research supports recycling inorganic minerals into fire-resistant building products that enhance safety. Full article

In recent years, the increasingly significant impacts of climate change and human activities on the environment have led to more frequent occurrences of extreme events such as forest fires. The recurrent wildfires pose severe threats to ecological environments and human life safety. Consequently, forest fire prediction has become a current research hotspot, where accurate forecasting technologies are crucial for reducing ecological and economic losses, improving forest fire management efficiency, and ensuring personnel safety and property security. To enhance comprehensive understanding of wildfire prediction research, this paper systematically reviews studies since 2015, focusing on two key aspects: datasets with related tools and prediction algorithms. We categorized the literature into three categories: statistical analysis and physical models, traditional machine learning methods, and deep learning approaches. Additionally, this review summarizes the data types and open-source datasets used in the selected literature. The paper further outlines current challenges and future directions, including exploring wildfire risk data management and multimodal deep learning, investigating self-supervised learning models, improving model interpretability and developing explainable models, integrating physics-informed models with machine learning, and constructing digital twin technology for real-time wildfire simulation and fire scenario analysis. This study aims to provide valuable support for forest natural resource management and enhanced environmental protection through the application of remote sensing technologies and artificial intelligence algorithms. Full article

Fires in buildings result in the undesirable loss of life and property. Despite fire safety designs, the frequent occurrence of fires indicates a need for improvements in fire safety management. Conventional fire safety management is based on regulations managed separately by different parties at various stages of a building’s lifecycle. This study aims to present a conceptual framework for building information modeling (BIM)-based fire safety and risk management using the fire dynamics simulator (FDS) for a three-story building. A BIM model was developed for the building with fire safety compliance checks, and a simulation was conducted using FDS to integrate the results into the BIM model and test the model’s feasibility. The framework process consists of modeling, analysis, data integration, and user education. The BIM model was developed using Revit during the modeling stage and evaluated for fire safety compliance using Dynamo scripts. Concurrently, FDS simulations were performed for fire risk assessment in various scenarios, and evacuation route planning was established, considering the available evacuation time obtained from FDS results. Fire safety information, such as available evacuation time and optimal evacuation paths, was then integrated back into the BIM model for data integration using Dynamo scripts. In the model, fire safety compliance and simulation results were successfully integrated into the BIM model, serving as a platform for effective fire safety and risk management and providing fire safety information for building residents. Full article

The cognitive ability of the elderly significantly influences evacuation performance in urgent situations. Despite its importance, many fire evacuation studies overlook the impact of cognitive ability on elderly evacuation performance. To address this gap, this study employs multicriteria decision-making to identify nudging factors that enhance the cognitive abilities of the elderly during fire evacuations in long-term care facilities. Based on a literature review, key nudging factors include guidance lights, guide lines, handrails, and guidance equipment, with sub-criteria such as location, color, size, and intervals. Experts from academic and practical fields analyzed the nudging factors, followed by a hybrid analytic hierarchy process (AHP–TOPSIS) analysis. The findings emphasize the necessity of providing auditory information through guidance equipment (e.g., voice evacuation system) in high-level scenarios (practice experts AHP: 0.31) and visual information through the continuous installation of guide lines in strategic locations (academic experts AHP: 0.35) to facilitate efficient evacuation. As a result, this study confirms both the differing and concordant opinions among expert groups while recognizing the absolute necessity of elderly evacuation research and considering the unique challenges that prevent actual evacuation experiments with elderly individuals. By synthesizing these perspectives, the study derives the weights and ranks of nudging factors based on urgent-level circumstances, thereby conducting a quantitative assessment of factors that enhance cognitive ability during elderly evacuation. The findings of this study can serve as a basis for future evacuation policy formulation for elderly-related facilities and, as a derivative effect, contribute to ensuring the life safety of elderly individuals within the local community. Full article

Fire is one of the most serious threatening conditions that endanger the safety of human life and building property. Religious buildings, where activities such as ritual incense burning and parishioner worship are conducted year-round, suffer from high fire risks and incomplete coverage of fire protection facilities, which have led to the frequent occurrence of fire accidents in ancient religious buildings around the globe. This study focuses on fire reconstruction and flame-retardant research for double-roofed ancient Buddhist buildings, addressing a gap in fire protection research for ancient religious buildings, particularly those with unique double-roofed structures. A systematic fire simulation method integrating building information modeling (BIM) and computational fluid dynamics (CFD) is proposed. This approach not only accurately models the complex structures of ancient buildings but also simulates fire and smoke spread paths, providing a scientific basis for fire warnings and firefighting strategies. Firstly, the double-roofed ancient Buddhist building is modeled according to its size through building information modeling (BIM). Secondly, the building modeling is revised, and the fire hazard is modeled based on computational fluid dynamics (CFD). Thirdly, the smoke and temperature sensors for fire warning and sprinkler systems for flame retardant are set. Finally, the fire and smoke spread paths are simulated for determining the location for installing the warning sensor and providing valuable fire rescues strategy. Based on simulations, a fire warning system using smoke and temperature sensors, along with a sprinkler-based flame retardant system, is designed. This integrated design significantly enhances the fire prevention and control capabilities of ancient buildings, reducing the occurrence of fire accidents. By simulating fire and smoke spread paths, the optimal locations for sensor installation are determined, and valuable fire rescue strategies are provided. This simulation-based analytical method greatly improves the precision and effectiveness of fire prevention and control. Experiments validate the flame-retardant and fire warning capabilities of the proposed method, demonstrating its practical application value in protecting ancient buildings from fire. The method offers new insights and technical support for fire protection in religious ancient buildings. Full article