Search Results (68)

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

Historical buildings are exposed to an increased risk of fire. The direct influence comes from the buildings’ structural design and the fire protection level. The fundamental principle for reducing the loss of heritage value in historical buildings due to fire is fire protection, as part of crisis management. This article focuses on selected castle buildings from Slovakia. Three castle buildings were selected based on their location in the country. All of them are currently used for museum purposes. Using an analytical form, we assessed fire hazards and fire safety measures in two parts, calculated the fire risk index, and proposed solutions. Qualitative research, which is more suitable for the issue at hand, was used to evaluate the selected objects. The main methods used in the research focused on visual assessment of the current condition of the objects and analysis of fire documentation and its comparison with currently valid legal regulations. Based on the results, we can conclude that Kežmarok Castle (part of the historical city center) has a small fire risk (fire risk index = 13 points). Trenčín Castle (situated on a rock above the city) and Stará Ľubovňa Castle (situated on a limestone hill outside the city, surrounded by forest) have an increased risk of fire (fire risk index = 50–63). Significant risk sources identified included surrounding forest areas, technical failures related to outdated electrical installations, open flames during cultural events, the concentration of highly flammable materials, and complex evacuation routes for both people and museum collections. 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

In recent years, the frequency and severity of large-scale forest fires have increased globally, threatening forest ecosystems, human lives, and property while potentially triggering cascading ecological and social crises. Despite significant advancements in remote sensing-based forest fire monitoring, early warning systems, and fire risk zoning, post-fire thermal damage assessment remains insufficiently addressed. This study introduces an innovative approach combining multispectral imaging with a dual annealing constrained optimization algorithm to enable dynamic monitoring of fire temperature distribution. Based on this method, we develop a dynamic thermal damage assessment model to quantify thermal impacts during forest fires. The proposed model provides valuable insights for defining thermal damage zones, optimizing evacuation strategies, and supporting firefighting operations, ultimately enhancing emergency response and forest fire management efficiency. Full article

This paper offers an in-depth analysis of the role of the Internet of Things (IoT) in fire safety systems, with a particular emphasis on fire detection, localization, and evacuation. Through a comprehensive bibliometric analysis, we identify pivotal research trends and advancements in IoT-based sensors, devices, and network architectures that facilitate real-time fire management. In addition, we examine the integration of emerging technologies—such as artificial intelligence, machine learning, and quantum computing—that enhance system performance and operational efficiency. Our study further highlights critical challenges and research gaps, including issues related to dynamic system adaptability, cross-domain synergies, bio-inspired fire safety mechanisms, post-fire analysis capabilities, linguistic and cultural barriers in research, and data security and privacy concerns. Finally, we outline prospective directions for future inquiry, underscoring the need for interdisciplinary collaboration and robust cybersecurity strategies to fully harness the potential of IoT in transforming fire safety. Full article

Portugal’s increasing wildfire frequency has led to home destruction, large areas burned, ecological damage, and economic loss, emphasizing the need for effective fire exposure assessments. This study builds on a Canadian approach to wildfire exposure and evaluates wildfire exposure in the Portuguese municipality of Mafra, using artificial territories (AT) as a proxy for the wildland–urban interface (WUI) and integrates land use land cover (LULC) data with a neighborhood analysis to map exposure at the municipal scale. Fire exposure was assessed for three fire transmission distances: radiant heat (RH, <30 m), short-range spotting (SRS, <100 m), and longer-range spotting (LRS, 100–500 m) using fine resolution (5 m) LULC data. Results revealed that while AT generally exhibited lower exposure (<16% “very high” exposure), adjacent hazardous LULC subtypes significantly increase wildfire hazard, with up to 51% of LULC subtypes classified as “very high exposure”. Field validation confirmed the accuracy of exposure maps, supporting their use in wildfire risk reduction strategies. This cost-effective, scalable approach offers actionable insights for forest and land managers, civil protection agencies, and policymakers, aiding in fuel management prioritization, community preparedness, and the design of evacuation planning. The methodology is adaptable to other fire-prone regions, particularly mediterranean landscapes. Full article

Ensuring rapid and efficient evacuation in high-density environments, such as stadiums, is critical for public safety during fire emergencies. Traditional fire alarm systems rely on reactive detection mechanisms, often resulting in delayed response times, increased panic, and overcrowding. This study introduces an AI-driven predictive fire alarm and evacuation model that leverages machine learning algorithms and real-time environmental sensor data to anticipate fire hazards before ignition, improving emergency response efficiency. To detect early fire risk indicators, the system processes data from 62,630 sensor measurements across 15 ecological parameters, including temperature, humidity, total volatile organic compounds (TVOC), CO₂ levels, and particulate matter. A comparative analysis of six machine learning models—Logistic Regression, Support Vector Machines (SVM), Random Forest, and proposed EvacuNet—demonstrates that EvacuNet outperforms all other models, achieving an accuracy of 99.99%, precision of 1.00, recall of 1.00, and an AUC-ROC score close to 1.00. The predictive alarm system significantly reduces false alarm rates and enhances fire detection speed, allowing emergency responders to take preemptive action. Moreover, integrating AI-driven evacuation optimization minimizes bottlenecks and congestion, reduces evacuation times, and improves structured crowd movement. These findings underscore the necessity of intelligent fire detection systems in high-occupancy venues, demonstrating that AI-based predictive modeling can drastically improve fire response and evacuation efficiency. Future research should focus on integrating IoT-enabled emergency navigation, reinforcement learning algorithms, and real-time crowd management systems to further enhance predictive accuracy and minimize casualties. By adopting such advanced technologies, large-scale venues can significantly improve emergency preparedness, reduce evacuation delays, and enhance public safety. 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

This research presents a pioneering framework to augment fire safety management within edifices by amalgamating real-time surveillance and adaptive evacuation methodologies. The proposed framework markedly enhances the efficacy of fire detection and the efficiency of evacuation processes. In an empirical investigation conducted on a 12-story residential structure in Wuhan, China, the implemented system achieved a 30% reduction in fire detection intervals and a 25% decrease in evacuation timeframes. The principal innovation of this framework resides in formulating an Improved Risk Index (ERI), which synthesizes real-time information garnered from environmental sensors, including temperature, smoke, and carbon monoxide concentrations, with architectural configurations and fire behavior to evaluate evacuation hazards. This system realized a detection accuracy rate of 95.2% and a 40% reduction in the necessity for manual inspections, surpassing the performance of conventional fire safety systems. The synthesis of real-time data with dynamic evacuation pathways enhanced emergency response times by equipping facility managers and emergency responders with instantaneous access to critical building intelligence. The framework complies with international and local fire safety regulations, ensuring its functional applicability across diverse types of buildings. This scholarly work offers a novel, scalable approach for improving fire safety management, potentially mitigating fire-induced damage and bolstering occupant safety within contemporary structures. Full article

To address the significant problems of high fire risk and low evacuation efficiency in large and complex medical buildings, this study uses Ezhou Hospital as the empirical object to construct a multi-dimensional threat and risk assessment and fire evacuation dynamic coupling model and proposes a systematic optimization scheme to improve personnel evacuation safety. This study proposes an innovative full-chain analysis framework of “threat and risk assessment-dynamic coupling-multi-strategy optimization”. The specific methods employed include the following: (1) Using the probabilistic threat and risk assessment (PRA) method and the risk index (RII) method to identify the most unfavorable scenarios where the fire source is located in the outpatient hall (risk value C2 = 9.86). (2) Combining PyroSim and Pathfinder to construct a dynamic coupling model of fire smoke diffusion and personnel evacuation. Multiple groups, such as patients with mobility problems and rescue personnel, are added to address the limitations of traditional single-factor simulations. (3) Considering the failure of fire shutters, a two-stage optimization strategy is proposed for when the number of personnel is at its peak: the evacuation time is shortened by 23% by using internal intelligent guidance to shunt the congestion node crowd, and the addition of external fire ladders forms a multi-channel coordinated evacuation that further reduces the total evacuation time from 1780 s to 1266 s and improves the efficiency by 29%. The results show that the coupled multi-path coordination strategy and three-dimensional rescue facilities can significantly reduce the bottleneck associated with a single channel. This study provides a multi-dimensional dynamic evaluation framework and comprehensive optimization paradigm for the design of the evacuation of high-rise medical buildings and has important theoretical and technical reference values for improving the fire safety performance of public buildings and the intelligence of emergency management. Full article

Media platforms provide an effective way to gauge public perceptions, especially during mass disruption events. This research explores public responses to the 2023 Ohio train derailment event through Twitter, currently known as X, and Google Trends. It aims to unveil public sentiments and attitudes by employing sentiment analysis using the Valence Aware Dictionary and Sentiment Reasoner (VADER) and topic modeling using Latent Dirichlet Allocation (LDA) on geotagged tweets across three phases of the event: impact and immediate response, investigation, and recovery. Additionally, the Self-Organizing Map (SOM) model is employed to conduct time-series clustering analysis of Google search patterns, offering a deeper understanding into the event’s spatial and temporal impact on society. The results reveal that public perceptions related to pollution in communities exhibited an inverted U-shaped curve during the initial two phases on both the Twitter and Google Search platforms. However, in the third phase, the trends diverged. While public awareness declined on Google Search, it experienced an uptick on Twitter, a shift that can be attributed to governmental responses. Furthermore, the topics of Twitter discussions underwent a transition across three phases, changing from a focus on the causes of fires and evacuation strategies in Phase 1, to river pollution and trusteeship issues in Phase 2, and finally converging on government actions and community safety in Phase 3. Overall, this study advances a multi-platform and multi-method framework to uncover the spatiotemporal dynamics of public perception during disasters, offering actionable insights for real-time, region-specific crisis management. Full article

Evacuation routing in wildland areas is an important aspect during various emergencies, including fire incidents. A review of the literature found a lack of research on vector routing systems for evacuations from wildland areas. This article aims to address the issue of determining evacuation routes using vector object database technology with various optimization methods. To this end, the author developed a novel algorithm for network creation and optimization through heuristic data aggregation. Case studies were conducted in a wooded area of the Bieszczady Mountains, where the potential of determining evacuation routes in the proprietary geodatabase (SQLite SpatiaLite) was examined, and the results were compared with traditional methods based on raster least-cost path analyses. The analyses confirmed the feasibility of creating a network of connections in the database within an area of 3.74 km² with undefined roads. Through the implementation of optimizations, the determination of evacuation routes in wildland areas was reduced to less than 1 s. Additionally, the possibility of the system operating for areas covering 40 km² was presented. The use of optimized vector data and database technology enabled the development of a comprehensive forest area management system, encompassing points of rescue units situated at significant distances from the area. This facilitated the establishment of flexible evacuation routes or rescue missions, particularly allowing for the establishment of multimodal routes using different means of transportation to reach the destination. Full article

This study systematically reviews the application of virtual reality (VR) in building evacuation scenarios in disaster contexts, highlighting its transformative potential to enhance preparedness, evacuation strategies, and safety training. Disasters such as fires, earthquakes, and multi-hazard emergencies pose significant challenges in densely populated urban environments, requiring innovative solutions beyond traditional methods. Analyzing 48 peer-reviewed studies (2014–2024) following PRISMA guidelines, this review focuses on VR applications in public buildings, transportation hubs, and high-risk workplaces, with VR simulations emerging as the predominant methodology. Key findings demonstrate VR’s ability to simulate realistic scenarios, improve spatial navigation, and optimize crowd dynamics and mobility accessibility. VR enhances evacuation efficiency and safety compliance by enabling adaptive training for diverse populations, including students, professionals, and vulnerable groups. In public and high-risk environments, VR addresses challenges such as visibility limitations, structural complexity, and the need for customized evacuation protocols. However, gaps remain in exploring multi-hazard environments and mixed-use spaces and ensuring scalability. Future research should integrate VR with artificial intelligence and machine learning for predictive and adaptive evacuation models. Expanding VR applications to underrepresented groups, including individuals with disabilities and the elderly, and collaborating with policymakers and urban planners are vital for translating research into practice. Overall, VR provides a scalable, adaptable, and inclusive solution for building evacuation preparedness, offering actionable insights to enhance resilience and safety in diverse architectural and disaster contexts. Its ability to transform evacuation strategies positions VR as a pivotal tool in advancing disaster management. Full article

With the frequent occurrence of natural disasters and the acceleration of urbanization, it is necessary to carry out efficient evacuation, especially when earthquakes, fires, terrorist attacks, and other serious threats occur. However, due to factors such as small targets, complex posture, occlusion, and dense distribution, the current mainstream algorithms still have problems such as low precision and poor real-time performance in crowd person detection. Therefore, this paper proposes EAAnet, a crowd person detection algorithm. It is based on YOLOv5, with CBAM (Convolutional Block Attention Module) introduced into the backbone, BiFPN (Bidirectional Feature Pyramid Network) introduced into the neck, and combined with a loss function of CIoU_Loss to better predict the person number. The experimental results show that compared with other mainstream detection algorithms, EAAnet has achieved significant improvement in precision and real-time performance. The precision value of all categories was 78.6%, which was increased by 1.8. Among these, the categories of riders and partially visible person were increased by 4.6 and 0.8, respectively. At the same time, the parameter number of EAAnet is only 7.1M, with a calculation amount of 16.0G FLOPs. Therefore, it is proved that EAAnet has the ability of the efficient real-time detection of the crowd person and is feasible in the field of emergency management. Full article

International research attention on evacuation issues has increased significantly following the human and natural disasters at the turn of the century, such as 9/11, Hurricane Katrina, Cyclones Idai and Kenneth, the Black Saturday forest fires and tsunamis in Japan. The main problem concerning when a disaster can occur involves studying the risk reduction. Risk, following all the theoretical and experimental studies, is determined by the product of three components: occurrence, vulnerability and exposure. Vulnerability can be improved over time through major infrastructure actions, but absolute security cannot be achieved. When the event will occur with certainty, only exposure remains to reduce the risk to people before the effect hits them. Exposure can be improved, under fixed conditions of occurrence and vulnerability, by improving evacuation. The main problem in terms of evacuating the population from an area is the available transport system, which must be used to its fullest. So, if the system is well managed, the evacuation improves (shorter times), meaning the exposure is reduced, and therefore, the risk is reduced. A key factor in the analysis of transport systems under emergency conditions is the behavior of the user, and therefore, the study of demand. This work identifies the main research lines that are useful for studying demand under exposure-related risk conditions. The classification of demand models that simulate evacuation conditions in relation to the effect on the transportation system is summarized. The contribution proposes a model for updating choice in relation to emergency conditions and utility. The contribution of emerging ICTs to actualization is formally introduced into the models. Intelligent technologies make it possible to improve user decisions, reducing exposure and therefore risk. The proposed model moves within the two approaches of the literature: it is an inter-period dynamic model with the probability expressed within the discrete choice theory; furthermore, it is a sequential dynamic model with the probability dependent on the previous choices. The contribution presents an example of application of the model, developing a transition matrix considering the case of choice updating under two extreme conditions. Full article