Search

The fire heat release rate (HRR) is a crucial parameter for describing the combustion process and its thermal effects. In recent years, some studies have employed fire scene images and deep learning algorithms to predict real-time fire HRR, which has led to the advancement of HRR prediction in terms of both lightweightness and real-time monitoring. Nevertheless, the development of an early-stage monitoring system for fires and the ability to predict future HRR based on current moment data represents a crucial foundation for evaluating the scale of indoor fires and enhancing the capacity to prevent and control such incidents. This paper proposes a deep learning model based on continuous fire scene images (containing both flame and smoke features) and their time-series information to predict the future transient fire HRR. The model (Att-BiLSTM) comprises three bi-directional long- and short-term memory (Bi-LSTM) layers and one attention layer. The model employs a bidirectional feature extraction approach, followed by the introduction of an attention mechanism to highlight the image features that have a critical impact on the prediction results. In this paper, a large-scale dataset is constructed by collecting 27,231 fire scene images with instantaneous HRR annotations from 40 different fire trials from the NIST database. The experimental results demonstrate that Att-BiLSTM is capable of effectively utilizing fire scene image features and temporal information to accurately predict future transient HRR, including those in high-brightness fire environments and complex fire source situations. The research presented in this paper offers novel insights and methodologies for fire monitoring and emergency response. [ABSTRACT FROM AUTHOR]

When a motor is accidentally started, the solid particles produced by fuel combustion have impact and erosion effects on the surrounding structure via gas ejection, and the structure of the bulkhead is damaged. Therefore, in this paper, the effect of solid particle phase motion on a bulkhead was investigated. A two-dimensional SST k-ω model was used for the analysis. The grid size of the core area of a supersonic jet was selected as RN/24 by the calculation accuracy, and the resources and time consumption of the calculation were comprehensively considered. Based on the simulation of supersonic impact jets, the influence of the phase motion of solid particles was introduced, and the impact of a two-phase jet field on a wall was investigated. The addition of a particle phase created a hysteresis effect on the airflow, changing the shock structure of the pure gas-phase flow field. The rebound of the particle phase at the wall caused the waves in front of the wall to move forward and the stagnation bubble structures to disappear in some cases. The particle aggregation degree and collision angle would affect the particle erosion rate of solid bulkheads. The increase in particle jet impingement distance would change the distribution of particle aggregation and would influence the distribution of wall particle erosion rate and deposition rate. This paper would provide theoretical and engineering guidance for the safety protection design of magazines, which is of great significance for the safety assurance of ship magazines. [ABSTRACT FROM AUTHOR]

In 2019, the Chinese government put forward the concept of social co-governance in the field of emergency management. This paper discusses whether the adjustment in the governance concept promotes public participation in emergency management practices and what the mechanisms are to achieve successful public participation in a wildfire emergency. Taking the Beibei wildfire in Chongqing in 2022 as a study case, this paper establishes the public participation mechanisms for wildfire rescue and management with a qualitative case study method. The result shows that adjustments to emergency management concepts and the pressure of wildfire governance make local governments and commanders seek help from the public; key persons and informal networks play an important role in public participation in a wildfire emergency; and constructing social memory in social networks, whether government-led or public-organized, is an important way to enhance public participation in wildfire governance. Our findings, grounded in the reality of the growing complexity of wildfires, provide useful lessons for future wildfire governance in other provinces in China, as well as valuable references for other countries or regions. [ABSTRACT FROM AUTHOR]

Wildfires have short- and long-term impacts on the geoenvironment, including the changes to biogeochemical and mechanical properties of soils, landfill stability, surface- and groundwater, air pollution, and vegetation. Climate change has increased the extent and severity of wildfires across the world. Simultaneously, anthropogenic activities—through the expansion of urban areas into wildlands, abandonment of rural practices, and accidental or intentional fire-inception activities—are also responsible for a majority of fires. This paper provides an overall review and critical appraisal of existing knowledge about processes induced by wildfires and their impact on the geoenvironment. Burning of vegetation leads to loss of root reinforcement and changes in soil hydromechanical properties. Also, depending on the fire temperature, soil can be rendered hydrophobic or hydrophilic and compromise soil nutrition levels, hinder revegetation, and, in turn, increase post-fire erosion and the debris flow susceptibility of hillslopes. In addition to direct hazards, wildfires pollute air and soil with smoke and fire suppression agents releasing toxic, persistent, and relatively mobile contaminants into the geoenvironment. Nevertheless, the mitigation of wildfires' geoenvironmental impacts does not fit within the scope of this paper. In the end, and in no exhaustive way, some of the areas requiring future research are highlighted. [ABSTRACT FROM AUTHOR]

The molecular structure of coal is complex, and the existing research methods are limited, so it is difficult to clarify its influence mechanism on the spontaneous-combustion characteristics of coal. In this paper, the previous extraction, FTIR, TPR, TG-DSC and other experimental results are combined to analyze the extraction weakening effect and the correlation analysis of the spontaneous-combustion characteristic parameters of raffinate coal. The results show that extraction can destroy the connection bond of coal molecules, change the content of dominant active groups in the coal spontaneous-combustion reaction, increase the lower limit of the key temperature nodes of coal spontaneous-combustion or extend the temperature range, resulting in an increase in the ignition-point temperature of coal and a decrease in coal quality. This paper will provide a theoretical basis for the study of the microscopic mechanism of coal spontaneous-combustion and then provide new ideas for the development of an active prevention and control technology for coal spontaneous-combustion. [ABSTRACT FROM AUTHOR]

As a paradigm of clean energy, hydrogen is gradually attracting global attention. However, its unique characteristics of leakage and autoignition pose significant challenges to the development of high-pressure hydrogen storage technologies. In recent years, numerous scholars have made significant progress in the field of high-pressure hydrogen leakage autoignition. This paper, based on diffusion ignition theory, thoroughly explores the mechanism of high-pressure hydrogen leakage autoignition. It reviews the effects of various factors such as gas properties, burst disc rupture conditions, tube geometric structure, obstacles, etc., on shock wave growth patterns and autoignition characteristics. Additionally, the development of internal flames and propagation characteristics of external flames after ignition kernels generation are summarized. Finally, to promote future development in the field of high-pressure hydrogen energy storage and transportation, this paper identifies deficiencies in the current research and proposes key directions for future research. [ABSTRACT FROM AUTHOR]

With the development of image processing technology and video analysis technology, forest fire monitoring technology based on video recognition is more and more important in the field of forest fire prevention and control. The objects currently applied to forest fire video image monitoring system monitoring are mainly flames and smoke. This paper proposes a forest fire risk monitoring and early warning algorithm, which integrates a deep learning model, infrared monitoring and early warning, and forest fire weather index. The algorithm first obtains the current visible image and infrared image of the same forest area, utilizing a smoke detection model based on deep learning to detect smoke in the visible image, and obtains the confidence level of the occurrence of fire in said visible image. Then, it determines whether the local temperature value of said infrared image exceeds a preset warning value, and obtains a judgment result based on the infrared image. It calculates again a current FWI based on environmental data, and determines a current fire danger level based on the current FWI. Finally, it determines whether or not to carry out a fire warning based on said fire danger level, said confidence level of the occurrence of fire in said visible image, and said judgment result based on the infrared image. The experimental results show that the accuracy of the algorithm in this paper reaches 94.12%, precision is 96.1%, recall is 93.67, and F1-score is 94.87. The algorithm in this paper can improve the accuracy of smoke identification at the early stage of forest fire danger occurrence, especially by excluding the interference caused by clouds, fog, dust, and so on, thus improving the fire danger warning accuracy. [ABSTRACT FROM AUTHOR]

Forest fire is a significant global natural disaster, and unmanned aerial vehicles (UAVs) have gained attention in wildfire prevention for their efficient and flexible monitoring capabilities. Proper UAV patrol path planning can enhance fire-monitoring accuracy and response speed. This paper proposes a probabilistic path planning (PPP) module that plans UAV patrol paths by combining real-time fire occurrence probabilities at different points. Initially, a forest fire risk logistic regression model is established to compute the fire probabilities at different patrol points. Subsequently, a patrol point filter is applied to remove points with low fire probabilities. Finally, combining fire probabilities with distances between patrol points, a dynamic programming (DP) algorithm is employed to generate an optimal UAV patrol route. Compared with conventional approaches, the experimental results demonstrate that the PPP module effectively improves the timeliness of fire monitoring and containment, and the introduction of DP, considering that the fire probabilities and the patrol point filter both contribute positively to the experimental outcomes. Different combinations of patrol point coordinates and their fire probabilities are further studied to summarize the applicability of this method, contributing to UAV applications in forest fire monitoring and prevention. [ABSTRACT FROM AUTHOR]

Indigenous Peoples have been stewarding lands with fire for ecosystem improvement since time immemorial. These stewardship practices are part and parcel of the ways in which Indigenous Peoples have long recorded and protected knowledge through our cultural transmission practices, such as oral histories. In short, our Peoples have always been data gatherers, and as this article presents, we are also fire data gatherers and stewards. Given the growing interest in fire research with Indigenous communities, there is an opportunity for guidance on data collection conducted equitably and responsibly with Indigenous Peoples. This Special Issue of Fire presents fire research approaches and data harvesting practices with Indigenous communities as we "Reimagine the Future of Living and Working with Fire". Specifically, the article provides future-thinking practices that can achieve equitable, sustainable, and just outcomes with and for stakeholders and rightholders (the preferred term Indigenous Peoples use in partnerships with academics, agencies, and NGOs). This research takes from the following key documents to propose an "Indigenous fire data sovereignty" (IFDS) framework: (1) Articles declared in the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP) as identified by the author and specified in Indigenous-led and allied Indigenous fire research in Australia, Canada, and the U.S.; (2) recommendations specific to cultural fire policy and calls for research in the 2023 Wildland Fire Mitigation and Management Commission report; (3) research and data barriers and opportunities produced in the 2024 Good Fire II report; and threads from (4) the Indigenous Fire Management conceptual model. This paper brings together recommendations on Indigenous data sovereignty, which are principles developed by Indigenous researchers for the protection, dissemination, and stewardship of data collected from Tribal/Nation/Aboriginal/First Nations Indigenous communities. The proposed IFDS framework also identifies potential challenges to Indigenous fire data sovereignty. By doing so, the framework serves as an apparatus to deploy fire research and data harvesting practices that are culturally informed, responsible, and ethically demonstrated. The article concludes with specific calls to action for academics and researchers, allies, fire managers, policymakers, and Indigenous Peoples to consider in exercising Indigenous fire data sovereignty and applying Indigenous data sovereignty principles to fire research. [ABSTRACT FROM AUTHOR]

This concept paper explores possible collective identities for a future wildland fire workforce. Taking inspiration from the work of futurists who foresee an end to the dominant fire exclusion/suppression paradigm, and assuming that an emerging fire restoration/resilience paradigm shift replaces it, this paper engages in speculative explorations of the process and product of this paradigm shift with respect to the future collective identities of a workforce conducting ecological fire management. Social constructionist assumptions from symbolic interactionist sociological theory, Gramscian political theory's concept of hegemony, and new social movement theory's concept of collective identity all provide the intellectual foundations for the discussion. This concept paper argues that in order to actualize a paradigm shift, more than advances in scientific research or reforms of government policies will be required—the wildland fire community will need to become (or join) a social movement engaged in collective actions. An imaginary social movement, the "Greenfire revolution," is invented to help illustrate how the selected theories and concepts might apply in the social construction of ecological fire management and the collective identities of its future workforce. [ABSTRACT FROM AUTHOR]

Optimizing Resource Allocation in Fire Departments (RAFD) is crucial for enhancing Fire Protection Services (FPS) and ultimately saving lives. Efficient RAFD ensures that fire departments have the necessary resources to respond effectively to emergencies. This paper presents a method for optimizing RAFD based on performance assessment results, examining its impact on Fire Department (FD) efficiency in Portugal. Evaluating data from 353 FDs, two RAFD optimization methods were assessed: one adhering to Portuguese regulations and constraints, such as budget allocation limitations, and another without such constraints. Integrating a slack-based data envelopment analysis model and mixed-integer linear programming, the study found that incorporating FD efficiency scores in RAFD improved overall efficiency at national, district, and FD levels. While adherence to Portuguese regulations led to balanced resource allocation and a 4% performance improvement at the national level, relaxing constraints yielded an 8% improvement, albeit with potential performance deterioration in some FDs. The detailed budget and efficiency metric analysis provided in this paper offers actionable insights for fire protection services enhancement. This underscores the importance of diverse optimization strategies to enhance FD efficiency, with implications for decision-makers at the Portuguese National Authority for Emergency and Civil Protection and similar organizations globally. [ABSTRACT FROM AUTHOR]

Fires are disastrous events with significant negative impacts on both people and the environment. Thus, timely and accurate fire detection and firefighting operations are crucial for social development and ecological protection. In order to segment the flame accurately, this paper proposes the GK-RGB unsupervised flame segmentation method. In this method, RGB segmentation is used as the central algorithm to extract flame features. Additionally, a Gaussian filtering method is applied to remove noise interference from the image. Moreover, K-means mean clustering is employed to address incomplete flame segmentation caused by flame colours falling outside the fixed threshold. The experimental results show that the proposed method achieves excellent results on four flame images with different backgrounds at different time periods: Accuracy: 97.71%, IOU: 81.34%, and F1-score: 89.61%. Compared with other methods, GK-RGB has higher segmentation accuracy and is more suitable for the detection of fire. Therefore, the method proposed in this paper helps the application of firefighting and provides a new reference value for the detection and identification of fires. [ABSTRACT FROM AUTHOR]

Semiclosed tunnels are very common in engineering construction. They are not connected, so they easily accumulate heat. Once a fire breaks out in a semiclosed tunnel, the route for rescue workers to enter is limited, so it is tough to get close to the fire source. In this paper, taking a mine excavation roadway with local pressure ventilation as an example, the temperature field distribution and water spray fire prevention characteristics of the excavation roadway face were studied using numerical simulation and theoretical analysis. This paper provides an explanation of a dynamics-based smoke management method for water spraying in a semiclosed tunnel as well as the equilibrium relationship between droplet drag force and smoke buoyancy. A method was first developed to calculate the quantity of smoke blockage based on the thickness of the smoke congestion. The local ventilation and smoke movement created a circulating flow in the excavation face, which was discovered by investigating the velocity and temperature fields of the excavation face. The size of the high-temperature area and the pattern of temperature stratification varied due to this circulating flow. When local ventilation and sprinkler systems were operating simultaneously, when the volume of smoke was small, the smoke avoided the majority of the water spray effect with the circulation flow; however, when the volume of smoke was large, the effect of the circulation flow decreased and the smoke gathered close to the sprinkler head. At this time, the blocking effect of the water spray was significant. The mean square error analysis revealed that activating the sprinkler had the most significant cooling impact on the wall on one side of the air duct. [ABSTRACT FROM AUTHOR]

Considering that the determination of fire hazard zones in warehouses is not sufficiently researched and studied, this paper aims to present a new methodological approach concerning the mentioned issue. Based on the COPRAS multi-criteria decision-making method, a new method was developed for the precise determination of potential zones where there is a risk of fire. The advantage of the described method is that it allows the quick and easy determination of all-orientation fire risk zones. The method requires fewer hardware resources compared to the existing ones and enables the display of the warehouse space in the form of a 3D model with calculated fire hazard zones. The mentioned procedure represents the first step when planning the layout and arrangement in the warehouse itself. The effectiveness of the proposed method was confirmed through a suitable numerical example. [ABSTRACT FROM AUTHOR]

In order to evaluate the risk of transmission line tripping due to wildfires, it is necessary to predict the breakdown voltage of the insulation gap under the flame. Firstly, this paper studies the breakdown prediction of rod–plane gaps under the full-flame bridging of wooden cribs; it then obtains the breakdown voltage and the leakage current values of full-flame bridging considering different sizes of wooden cribs and different gap distances. Then, a multi-physical field simulation is carried out to obtain the flame gap characteristic parameters, such as spatial temperature. The feature quantity is normalized and reduced in dimension, and a prediction model for gap breakdown voltage under flame conditions based on a support vector machine (SVM) is established. Finally, the DC withstand voltage values and corresponding characteristic quantities under different flame gap conditions are used as sample sets to test the prediction model. The results show that the prediction error for small gap breakdown voltage is less than 2.6%. The samples were tested under different flame intensities for training and prediction, and the error was less than 3.3%. The small gap data for 30~60 cm is used to predict the breakdown voltage of the long gap for 100~140 cm, and the error is less than 3.2%. Compared with the fitting correction formula method proposed in existing research, the error is reduced by 11.5% and 4.4%, respectively, which verifies the effectiveness of the SVM prediction model. [ABSTRACT FROM AUTHOR]

Extreme forest fires have historically been a significant concern in Canada, the Russian Federation, the USA, and now pose an increasing threat in boreal Europe. This paper deals with application of the wildFire cLimate impacts and Adaptation Model (FLAM) in boreal forests. FLAM operates on a daily time step and utilizes mechanistic algorithms to quantify the impact of climate, human activities, and fuel availability on wildfire probabilities, frequencies, and burned areas. In our paper, we calibrate the model using historical remote sensing data and explore future projections of burned areas under different climate change scenarios. The study consists of the following steps: (i) analysis of the historical burned areas over 2001–2020; (ii) analysis of temperature and precipitation changes in the future projections as compared to the historical period; (iii) analysis of the future burned areas projected by FLAM and driven by climate change scenarios until the year 2100; (iv) simulation of adaptation options under the worst-case scenario. The modeling results show an increase in burned areas under all Representative Concentration Pathway (RCP) scenarios. Maintaining current temperatures (RCP 2.6) will still result in an increase in burned area (total and forest), but in the worst-case scenario (RCP 8.5), projected burned forest area will more than triple by 2100. Based on FLAM calibration, we identify hotspots for wildland fires in the boreal forest and suggest adaptation options such as increasing suppression efficiency at the hotspots. We model two scenarios of improved reaction times—stopping a fire within 4 days and within 24 h—which could reduce average burned forest areas by 48.6% and 79.2%, respectively, compared to projected burned areas without adaptation from 2021–2099. [ABSTRACT FROM AUTHOR]

Forest is an important resource for human survival, and forest fires are a serious threat to forest protection. Therefore, the early detection of fire and smoke is particularly important. Based on the manually set feature extraction method, the detection accuracy of the machine learning forest fire detection method is limited, and it is unable to deal with complex scenes. Meanwhile, most deep learning methods are difficult to deploy due to high computational costs. To address these issues, this paper proposes a lightweight forest fire detection model based on YOLOv8 (FFYOLO). Firstly, in order to better extract the features of fire and smoke, a channel prior dilatation attention module (CPDA) is proposed. Secondly, the mixed-classification detection head (MCDH), a new detection head, is designed. Furthermore, MPDIoU is introduced to enhance the regression and classification accuracy of the model. Then, in the Neck section, a lightweight GSConv module is applied to reduce parameters while maintaining model accuracy. Finally, the knowledge distillation strategy is used during training stage to enhance the generalization ability of the model and reduce the false detection. Experimental outcomes demonstrate that, in comparison to the original model, FFYOLO realizes an mAP0.5 of 88.8% on a custom forest fire dataset, which is 3.4% better than the original model, with 25.3% lower parameters and 9.3% higher frames per second (FPS). [ABSTRACT FROM AUTHOR]

In a long and narrow corridor, the installation of roof smoke blocking structures is a measure to slow down the spread of fire smoke. When employing multiple smoke blocking structures, the spacing between these structures is a critical parameter that needs to be considered for optimal effectiveness. This paper analyzes the smoke blocking performance of double structures at different spacing and measures the smoke flow velocity both upstream and downstream of the double structures. According to the analysis of the smoke velocity vector obtained from numerical simulation, the smoke can be divided into three zones based on the flow state of the smoke after passing through the front smoke screen structure, namely the vortex zone, surge wave zone, and steady flow zone. When the rear smoke screen is located in the surge zone, the smoke blocking effect is optimal. Analysis of the morphology of the smoke layer indicates that the length of the vortex region is directly proportional to the upstream smoke flow velocity. The numerical and experimental results both indicate that an excessively large or small spacing between the structures fails to achieve optimal smoke control effectiveness. When the spacing is within an optimal range, the smoke velocity is the lowest. Finally, using a real architectural corridor as a case background, this paper presents a design example of roof smoke blocking structures. In order to arrange as many smoke blocking structures as possible, an appropriate spacing between the structures should be slightly larger than the vortex region. The smoke control effectiveness of multiple roof structures was validated through numerical simulation. As a result, the time required for smoke to pass through the corridor increases by 110 s. [ABSTRACT FROM AUTHOR]

In order to solve the problem of emergency decision-making with incomplete information and deal with the accident information in different time series at the scenes of major accidents, this paper proposes a method of sequential decision-making by utilizing the relevant knowledge of D-S evidence theory and game theory. Firstly, we took an oil tank fire accident as an example and sorted out historical cases and expert experiences to establish a logical relationship between key accident scenes and accident scene symptoms in the accident. Meanwhile, we applied the logistic regression analysis method to obtain the basic probability distribution of each key accident scene in the oil tank fire, and on this basis, we constructed an evidence set of the fire. Secondly, based on the D-S evidence theory, we effectively quantified the knowledge uncertainty and evidence uncertainty, with the incomplete and insufficient information taken as an evidence system of the development of key accident scenes to construct a situation prediction model of these accident scenes. Thirdly, based on the game theory, we viewed emergency decision-makers and major accidents as two sides of the game to compare and analyze accident states at different time points and solve the contradiction between loss costs of decision-making and information collection costs. Therefore, this paper has provided a solution for the optimization of accident schemes at different time stages, thus realizing the sequential decision-making at the scenes of major accidents. Furthermore, we combined the situation prediction model with sequential decision-making, with the basic steps described below: (1) We drew up an initial action plan in the case of an extreme lack of information; then, we (2) started to address the accident and constructed a framework of accident identification, (3) collected and dealt with the continuously added evidence information with the evolution of the accident, (4) calculated the confidence levels of key accident scenarios after evaluating different evidence and then predicted the accident state in the next stage, and (5) calculated the profit–loss ratio between the current decision-making scheme and the decision-making scheme of the next stage. Finally, we (6) repeated steps (3) to (5) until the accident completely vanished. We verified the feasibility of the proposed method with the explosion accident of the Zhangzhou P.X. project in Fujian on 6 April used as an example. Based on the D-S evidence theory, this method employs approximate reasoning on the incomplete and insufficient information obtained at the scenes of major accidents, thus realizing the situation prediction of key scenes of these accidents. Additionally, this method uses the game theory to solve the contradiction between decision-making loss costs and information collection costs, thus optimizing the decision-making schemes at different time stages of major accidents. [ABSTRACT FROM AUTHOR]

A workshop, as a crowded place, is quite easy to cause serious casualties and economic losses once there is a fire. In this paper, Pathfinder software was used to simulate fire emergency evacuation in a workshop of a large factory with building structural symmetry. According to the simulation results, several obstacles to the evacuation were discovered and further analyzed. The results showed that the main factors affecting the evacuation were the width of exits, the distribution of occupants and the effective evacuation width of stairs. Among them, only changing the width of exits had little influence on shortening evacuation time. While changing the effective evacuation width of stairs could greatly relieve the evacuation pressure, every increase of 0.5 m in the width of the staircase could shorten the evacuation time by 30.0 s. Meanwhile, the larger the number of people in high-rise buildings, the longer the evacuation time was. Therefore, the means of restricting people from entering the high-rise buildings in batches could be used to prevent personnel from being evacuated in time when a fire incident occurs. [ABSTRACT FROM AUTHOR]

Fire detection systems are critical for mitigating the damage caused by fires, which can result in significant annual property losses and fatalities. This paper presents a deep learning-based fire classification model for an intelligent multi-sensor system aimed at early and reliable fire detection. The model processes data from multiple sensors that detect various parameters, such as temperature, humidity, and gas concentrations. Several deep learning architectures were evaluated, including LSTM, GRU, Bi-LSTM, LSTM-FCN, InceptionTime, and Transformer. The models were trained on data collected from controlled fire scenarios and validated for classification accuracy, loss, and real-time performance. The results indicated that the LSTM-based models (particularly Bi-LSTM and LSTM) could achieve high classification accuracy and low false alarm rates, demonstrating their effectiveness for real-time fire detection. The findings highlight the potential of advanced deep-learning models to enhance the reliability of sensor-based fire detection systems. [ABSTRACT FROM AUTHOR]

Ensuring fire safety is essential to protect life and property, but modern infrastructure and complex settings require advanced fire detection methods. Traditional object detection systems, often reliant on manual feature extraction, may fall short, and while deep learning approaches are powerful, they can be computationally intensive, especially for real-time applications. This paper proposes a novel smoke and fire detection method based on the YOLOv8n model with several key architectural modifications. The standard Complete-IoU (CIoU) box loss function is replaced with the more robust Wise-IoU version 3 (WIoUv3), enhancing predictions through its attention mechanism and dynamic focusing. The model is streamlined by replacing the C2f module with a residual block, enabling targeted feature extraction, accelerating training and inference, and reducing overfitting. Integrating generalized efficient layer aggregation network (GELAN) blocks with C2f modules in the neck of the YOLOv8n model further enhances smoke and fire detection, optimizing gradient paths for efficient learning and high performance. Transfer learning is also applied to enhance robustness. Experiments confirmed the excellent performance of ESFD-YOLOv8n, outperforming the original YOLOv8n by 2%, 2.3%, and 2.7%, with a mean average precision (mAP@0.5) of 79.4%, precision of 80.1%, and recall of 72.7%. Despite its increased complexity, the model outperforms several state-of-the-art algorithms and meets the requirements for real-time fire and smoke detection. [ABSTRACT FROM AUTHOR]

High-volume space structures are characterized by high combustible loads, rapid fire development, difficulty in firefighting, and potential building collapse risks, making early fire detection particularly crucial. The effectiveness of early fire detection technologies relies on their ability to adapt to the characteristics of smoke-dominant combustion products in the protected space. However, there is a lack of targeted research on the characteristics of the smoke generated during the early low-power stages of fires in high-volume spaces, which has not supported the development of early fire detection technologies for such environments. To address this, this paper presents an experimental study that, for the first time, collects data on fire smoke parameters such as temperature, velocity, concentration, and particle size at heights ranging from 6.5 m to 18.5 m using lightweight sensors. The study analyzes the characteristic and correlations of these parameters and their impact on early fire detection in high-volume spaces for the first time, presenting variation patterns in the plume velocity and particle size distribution of early fire smoke with height. It identifies three patterns of particle size distribution, contrasting with previous studies, and offers a qualitative explanation for these findings. This research enhances the understanding of early fire smoke signals in large spaces and offers valuable insights for developing more accurate and efficient fire detection strategies and technologies. [ABSTRACT FROM AUTHOR]

The study of fire simulation and emergency evacuation in cruise ships is a challenging aspect of cruise safety research. Investigating the impact of fire byproducts on passenger evacuation is a critical issue that urgently needs to be addressed. This paper utilizes PyroSim 2022 software to establish five fire cases, analysing the fire products concentration under each case. The influence of fire products on passenger evacuation was analysed using PathFinder. The results showed that when the fire source is in the stage area, the impact of fire byproducts on passenger evacuation is relatively minor. However, when the fire source is near the exit, especially in cases 1 and 2, fire byproducts tend to accumulate in the right area of the second floor, significantly affecting passenger evacuation. Moreover, during the evacuation process, a large number of passengers exhibit herd behaviour, leading to some passengers being unable to evacuate safely. To address the congestion issue, an optimized evacuation guidance plan is proposed, that can effectively improve evacuation efficiency and reduce the average congestion time per person. This has a positive role in enhancing the safety level of cruise ship fires. [ABSTRACT FROM AUTHOR]

Wildland firefighters often work in remote settings with multiple hazards that can cause life-threatening injuries. Prompt access to medical care is key to reducing injury consequences. For the last decade, a spatial model of wildland firefighter estimated ground evacuation time (GET) has been used when developing operational response strategies in the contiguous United States (CONUS). This paper describes our updated and improved GET model and the resulting decision support spatial data representing the estimated time to evacuate to a hospital from anywhere within the CONUS using ground transportation only. The new GET model leverages updated input datasets and has improved off-road travel time estimation methods that incorporate the latest science on how terrain slope influences pedestrian travel rates. It also accounts for a novel set of landscape variables not previously considered, including minor roads and trails, streams, woody debris, cliffs, and an improved handling of shrub cover. When compared to a set of recent safety incidents, the reported evacuation times were correlated with the model predictions. The spatial patterns of GET from the new model are similar to the old product; however, we found that, on average, the new version of GET yields slightly faster evacuation times, but with regional variation in this trend. [ABSTRACT FROM AUTHOR]

Formic acid is a promising candidate fuel that can be produced by reacting renewable hydrogen with carbon dioxide. However, the burning safety characteristics of formic acid–air mixtures have not been fully studied. This paper presents an extensive theoretical study of the adiabatic explosion pressure of formic acid–air premixed laminar flames at various initial conditions (composition of formic acid: 17–38% volume; initial pressure: 0.1–1.5 bar; initial temperature: 333–500 K), using the GASEQ software package. GASEQ software calculates chemical equilibria based on ideal gas behavior and is based on the hypothesis of adiabatic expansion inside a closed containment that allows ideal expansion. The influence of the initial conditions (pressure, temperature, and concentration) of formic acid–air mixtures on the adiabatic explosion pressures, maximum flame temperature, and peak concentrations of the main reaction intermediates is investigated and discussed. It is found that the adiabatic peak explosion pressure (calculated equilibrium pressure) of the studied concentrations decreases with increasing initial temperature and increases linearly with increasing initial pressure. [ABSTRACT FROM AUTHOR]

Jet fires are the second most common fire scenario after spill fires. This type of fire is characteristic of gas and gas–oil fires occurring on oil platforms and gas production and processing plants. The consequences of such fires are characterized by high material damage; this is associated with extensive networks of technological communications, since there is a high density of technological facilities and installations in the territory where these fires occur. At such facilities, there is a large number of steel structures, which under the action of high temperature quickly lose their strength and deform. To protect steel structures in the oil and gas industry, fire protection is used, which consists of different types: boards in the form of flat plates, plasters, and epoxy paints. This paper compares three types of fire protection materials for steel structures under jet fire: board fireproofing, plaster composition, and epoxy coating. When comparing the efficiency in jet fire, cement boards were found to be the best. However, despite the better fire protection efficiency, their low application is expected due to their massiveness and the high cost of such protection and the difficulty of installation. Nevertheless, the development of fire depends on the place of its origin, the size of the initial fire zone, and the stability and massiveness of the metal elements of the vessel structure or the structure of the boards on which the equipment can be placed. Therefore, it is necessary to take these factors into account when selecting fire protection and to apply it depending on the required fire resistance limits of structures, which should be determined depending on the fire development scenarios. [ABSTRACT FROM AUTHOR]

A thermal dynamic disaster in the goaf is one of the most serious coal mine disasters formed by coal spontaneous combustion and gas interweaving. However, the influence of the high-temperature hidden fire source formed in the goaf on the evolution law of thermal dynamic disasters is not clear, and effective prevention and control measures cannot be taken. Therefore, this paper uses the experimental platform of thermal dynamic disaster in the goaf to study the influence of different fire point positions on the development of thermal dynamic disaster in the goaf through a similar simulation experiment of thermal dynamic disaster evolution in the goaf and analyzes the corresponding relationship between temperature and CO concentration in the upper corner. The results show that under different locations of heat source, the high-temperature heat source of coal spontaneous combustion migrates to the air leakage side with sufficient oxygen supply, and an oxygen-poor circle is formed near the ignition point. Under the action of air leakage flow, CH4 accumulates in the deep part of the goaf on the return air side. Due to the increase in coal, part of CH4 is produced, which leads to the increase in concentration of CH4 at the ignition point. Under the action of different heat sources, the changing trend of concentration of CO and temperature in the return air corner is the same, but the temperature change in the return air corner shows a lag compared with the change in the concentration of CO, so concentration monitoring of CO can reflect the evolution process of the fire field in the goaf more quickly than temperature monitoring. [ABSTRACT FROM AUTHOR]

Smoke detectors are the most widely used fire detectors due to their high sensitivity. However, they have persistently faced issues with false alarms, known as nuisance alarms, as they cannot distinguish smoke particles, and their responsiveness varies depending on the particle size and concentration. Although technologies for distinguishing smoke particles have shown promising results, the hardware limitations of smoke detectors necessitate an intelligent approach to analyze scattering signals of various wavelengths and their temporal changes. In this paper, we propose a pipeline that can distinguish smoke particles based on scattering signals of various wavelengths as input. In the data extraction phase, we propose methods for extracting datasets from time series data. We propose a method that combines traditional approaches, early detection methods, and a Dynamic Time Warping technique that utilizes only the shape of the signal without preprocessing. In the learning model and classification phase, we present a method to select and compare various architectures and hyperparameters to create a model that achieves the best classification performance for time series data. We create datasets for six different targets in our presented sensor and smoke particle test environment and train classification models. Through performance comparisons, we identify architecture and parameter combinations that achieve up to 98.7% accuracy. Ablation studies under various conditions demonstrate the validity of the chosen architecture and the potential of other models. [ABSTRACT FROM AUTHOR]

BRIDGE (bridging science and local communities for wildfire risk reduction) is an action–research project developing a community-based disaster risk reduction (CBDRR) process which is being fostered through a collaborative innovation laboratory (InnoLab). BRIDGE integrates different forms of knowledge and action, linking science and local communities to reduce their vulnerability and to enhance strategies for forest fire risk reduction. Applied in Monchique, a forest-fire-prone municipality in Portugal, the InnoLab creates a space for dialogue and knowledge sharing between multiple actors that, directly or indirectly, manage forest territories. BRIDGE attempts to facilitate social learning about forest fire risks, strengthen collaborative networks and enhance adaptive capacities (socially and institutionally) for forest fire prevention. This paper shares results of the InnoLab activities, which include (i) school community activities on wildfire risk perception; (ii) participatory mapping of Monchique's vulnerabilities and risks; (iii) participatory sessions to create the community visioning for Monchique's territory, to define priorities and capacities to be improved, and to identify functions and responsibilities to act upon; and (iv) workshop on CBDRR strategies for action. Lessons are shared on how InnoLab can represent an innovative participatory approach to promote CBDRR processes in forest-fire-prone territories by both contributing to the recognition of local knowledge systems and encouraging the active role of forest communities through strengthened local networks for a more lasting commitment to forest fire risk reduction policy. [ABSTRACT FROM AUTHOR]

The increasing frequency of wildfires has posed significant challenges to communities worldwide. The effectiveness of all aspects of disaster management depends on a credible estimation of the prevailing risk. Risk, the product of a hazard's likelihood and its potential consequences, encompasses the probability of hazard occurrence, the exposure of assets to these hazards, existing vulnerabilities that amplify the consequences, and the capacity to manage, mitigate, and recover from their consequences. This paper employs the multiple criteria decision-making (MCDM) framework, which produces reliable results and allows for the customization of the relative importance of factors based on expert opinions. Utilizing the AROMAN algorithm, the study ranks counties in the state of Arizona according to their wildfire risk, drawing upon 25 factors categorized into expected annual loss, community resilience, and social vulnerability. A sensitivity analysis demonstrates the stability of the results when model parameters are altered, reinforcing the robustness of this approach in disaster risk assessment. While the paper primarily focuses on enhancing the safety of human communities in the context of wildfires, it highlights the versatility of the methodology, which can be applied to other natural hazards and accommodate more subjective risk and safety assessments. [ABSTRACT FROM AUTHOR]

This study explores the intricate behaviors of smoke flow, temperature distribution, carbon monoxide (CO) levels, and visibility dynamics within complex underground spaces during fire incidents. A key revelation is the profound impact of ventilation speed, with the identification of a critical range between 2 and 3 m/s that consistently proves to be instrumental in curbing smoke-related hazards and ensuring the safe evacuation of personnel. Furthermore, this paper underscores the influence of accelerated longitudinal winds on temperature profiles, particularly under high HRR conditions, underscoring the importance of accounting for wind effects in comprehensive fire response strategies. Regarding CO concentration, which is a critical safety concern, this study demonstrates that higher ventilation speeds effectively reduce hazardous gas levels, thereby fortifying overall safety measures. The visibility is analyzed, with the findings indicating that elevated ventilation speeds enhance visibility, albeit with considerations about potential drawbacks on personnel evacuation due to excessive wind speed. In conclusion, this paper offers a comprehensive understanding of the pivotal role played by ventilation speed in underground space safety by encompassing smoke control and temperature management. [ABSTRACT FROM AUTHOR]

Bushfires are classified as catastrophic disasters capable of inflicting significant destruction. The key detrimental consequences of bushfires include the loss of human lives, trauma within communities, economic losses and environmental damage. For example, the estimated economic loss from the September 2019 to March 2020 bushfires in New South Wales (Australia) was about AUD 110 billion, including more than 3000 burned houses. There has been a notable increase in both the frequency and intensity of bushfires, as clearly demonstrated by recent bushfire events. Bushfires are an intricate phenomenon that transpires across various spatial and temporal scales. Further, the changing circumstances of landscapes, vegetation patterns, weather conditions and ecosystems account for the complexity. Therefore, continual attention is essential for the development of bushfire management strategies. In this context, this paper undertakes a comprehensive literature review of bushfire management strategies, encompassing aspects such as bushfire prediction, detection, suppression and prevention. Based on the review, a bushfire management framework is proposed that can eliminate or successfully mitigate the consequences of bushfires. Further, the paper delves into the domains of fire weather conditions, the initiation of bushfires and the adverse consequences stemming from these fires. Both terrestrial and aerial remote sensing methods have proven to be effective in predicting and detecting bushfires. Nevertheless, a simple unique solution cannot be proposed for bushfire management. Changing weather conditions, topography and the geographic mix of asset types need to be considered when deciding on bushfire management strategies and their breadth and depth of application. [ABSTRACT FROM AUTHOR]

The collapse and deterioration of infrastructures due to fire events are documented annually. These fire incidents result in multiple deaths and property loss. In this paper, a reliable and practical numerical methodology was introduced to facilitate the whole process of fire simulations and increase the practicality of performing comprehensive parametric studies in the future. These parametric studies are crucial for understanding the factors that affect thermal–structural responses and avoiding the high cost of destructive tests. The proposed algorithm comprises a fully nonlinear coupled thermal-stress analysis involving thermal and structural material nonlinearity and the thermal–structural response during a fire. A detailed numerical modeling analysis was performed with ABAQUS to achieve the proposed algorithm. The results of the proposed numerical methodology were validated against published experimental work. The experimental work includes a full-scale RC beam loaded with working loads and standard heating conditions to simulate real-life scenarios. The tested beam failed during the fire, and its fire resistance was recorded. The results demonstrated a good correlation with the experimental results in thermal and structural responses. Moreover, this paper presents the direct coupling technique (DCT) and the advantages of using DCT over the traditional sequential coupling technique (SCT). [ABSTRACT FROM AUTHOR]

This paper aimed to provide technical support for fuel management by exploring different strengths of fuel removal on the physical and chemical properties and flammability of Betula platyphylla forests in the wildland–urban interface. After investigating the northeastern region during the forest fire prevention period in May 2023, a typical WUI area was selected, and three different treatment strengths, combined with a control, were set up to carry out indoor and outdoor experiments for 27 weeks. Compared with previous studies, this study mainly investigated and analyzed the dynamic changes in the physical and chemical properties and fuel flammability after different intensities of treatments on a time scale. By processing and analyzing the data, the following results were obtained. Significant differences existed in the fuel loading of different time-lag fuels over time (p < 0.05). The ash and ignition point of 1 h time-lag fuel after different treatment intensities generally increased first and then decreased, and the higher heat value and ash-free calorific value generally decreased first and then increased. The physical and chemical properties of 10 h and 100 h time-lag fuel fluctuated with time, but the overall change was insignificant. The indicator that had the greatest impact on the combustion comprehensive score for different time-lag fuels was fuel loading. The change in the flammability of dead surface fuel with time varied significantly, and different treatment intensities effectively reduced the fuel's flammability. The reduction effects, presented in descending order, were as follows: medium-strength treatment > low-strength treatment > high-strength treatment > control check. In conclusion, different treatment intensities have significant effects on the flammability of the fuel, and the medium-strength treatment has the best effect. Considering the ecological and economic benefits, adopting the medium-strength treatment for the WUI to regulate the fuel is recommended. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of window partition walls on the spread of fire on building facades under the impact of environmental wind through Fire Dynamics Simulator simulation experiments. A four-story building model was constructed using a Fire Dynamics Simulator incorporating six different wind speed conditions and six different partition wall widths. The fire-blocking performance of window partition walls of varying widths was systematically compared and analyzed, and the data indicated: (1) Under calm wind conditions, the installation of window partition walls is observed to facilitate the vertical spread of facade fires. Moreover, as the width of these partition walls increases, this facilitative effect becomes increasingly prominent; (2) Under wind speeds of 0 to 5 m/s, the temperature on the leeward side is lower when window partition walls are present than when they are absent. This indicates that window partition walls inhibit the horizontal spread of building facade fires, and wider window partition walls have better horizontal fire resistance performance. [ABSTRACT FROM AUTHOR]

The increasing concerns about CO2 emissions and climate change have pointed out the urgency of promoting sustainability in the building sector. One promising solution to enhance the energy efficiency of buildings and diminish environmental impact is the integration of phase-change materials (PCMs) into ventilated façade systems. This review article critically examines the current state of research on this innovative approach, with a particular focus on fire safety considerations. The paper explores the integration of PCM into ventilated façades, highlighting the potential for significant improvements in energy consumption, thermal comfort, and reductions in CO2 emissions. However, the flammability of PCMs introduces substantial fire safety challenges that must be addressed to ensure the safe application of this solution. The fire safety of both ventilated façades and PCMs is approached, followed by specific fire safety concerns when PCMs are integrated into ventilated façade systems. The conclusion states that while the integration of PCMs into ventilated façades offers substantial environmental benefits, attention to fire safety is essential. This necessitates the implementation of rigorous fire protection measures during the design and construction phases. By addressing both the environmental advantages and fire safety challenges, this review aims to provide a comprehensive understanding of the potential and limitations of PCM-integrated ventilated façades, offering valuable insights for researchers, engineers, and policymakers in the field of sustainable buildings. [ABSTRACT FROM AUTHOR]

This paper presents an experimental investigation into shockwave and flame characteristics of compressed hydrogen released through various types of pressure relief devices (PRDs) for which data have not been previously reported. Burst disks and safety valves with different set pressure (P0) of 22–140 MPa were tested. Shockwave intensity/velocity spontaneous ignition/flame behavior was monitored by in-situ pressure/light sensors, respectively. Previous works have mostly focused on burst disks with low P0 (under 10 MPa), leaving safety valves and high-pressure burst disks uninvestigated. It was found that shockwave/spontaneous ignition behavior differs with PRD types. Spontaneous ignition occurs in all burst disk cases, along with an ignition/self-extinguishment/reignition process with relatively low P0, which has not been revealed previously. In contrast, none of the safety valves cause spontaneous ignition, resulting from the absence of shockwave due to lower overpressure values/rise rate during release. This suggests that shockwave formed by sudden release is the most dominant factor in spontaneous ignition. Also, the occurrence of self-extinguishment does not guarantee the absence of jet flame. This work provides a comprehensive database revealing shockwave and flame characteristics of hydrogen released through different PRDs, which offers basic data and theoretical support for the safety and risk assessment of high-pressure hydrogen facilities. [ABSTRACT FROM AUTHOR]

As the conventional inert gas, it is used for the prevention and control of coal's spontaneous combustion, mainly N2 and CO2. However, there is limited research focusing on the inerting effect of composite inert gas. This paper studied the impact of using a premixed inert gas (N2 accounted for 50%, 60%, 70%, and 80%) instead of CO2 on the inerting effect of nearly horizontal and gently inclined goaf by building a physical similarity simulation experiment platform. The experimental results showed that the inerting effect of premixed inert gas was better than that of CO2. For instance, in a nearly horizontal goaf, the inerting effect of the premixed inert gas was optimal when the N2 accounted for 70%. The average O2 concentration in the monitored area decreased from 9.7% with CO2 to 6.4%. In addition, in the gently inclined goaf, the premixed inert gas exhibited an accumulation state similar to CO2, primarily occurring in the lower part region adjacent to the working face. Furthermore, the accumulation state of premixed inert gas was inversely proportional to its inerting effect. This study has important reference significance for applying inert gas fire prevention and extinguishing technology in mines. [ABSTRACT FROM AUTHOR]

With the increase in types of fire risk and the expansion of fire management coverage, it is urgent to involve multiple subjects in fire management. Cross-sectoral joint fire management is a new fire management mode based on collaboration between the fire management sector and the industry management sector in China. Additionally, fire information from multiple sources plays a significant role in the formation of the cross-sectoral joint fire management mode. To explore the operating characteristics and influencing factors of the cross-sectoral joint fire management mode, this paper constructed a cross-sectoral joint fire management game model by focusing on the interactions and game relationships between multiple organizations. Through numerical simulation analysis, the mechanisms by which the sharing level, coverage range, and disclosure degree of fire information influence the evolution of the game system are investigated. The results show that with the improvement in the sharing level, the coverage range, and the disclosure degree of fire information, although the evolutionary paths of the game system and game subjects' strategies are different, the cross-sectoral joint fire management game system can form a stable strategy combination of (1,1,1). This indicates that the sharing level, coverage range, and disclosure degree of fire information play positive driving roles in the formation of the cross-sectoral joint fire management mode. Furthermore, it is found that the fire management sector has a greater influence on the cross-sectoral joint fire management mode. Finally, the implications of improving the effectiveness of cross-sectoral joint fire management are proposed: enhancing institutional support, promoting information sharing, and expanding channels for information disclosure. [ABSTRACT FROM AUTHOR]

Timber is the most widely used material for furniture in view of its characteristics of light mass, high strength, easy processing, coloring, and decorative appearance. However, the flammability of wood has been frequently associated with increased fire intensity and the rapid spread of fire in buildings. In this paper, the combustion performance of six kinds of common furniture timber was investigated based on thermogravimetric analysis at 25–500 °C, cone calorimetry with 50 kW/m2 thermal radiation intensity, and flame spread experiments with 3 kW/m2 thermal radiation intensity. The ignition, weight loss, thermogenesis, smoke, and flame spread characteristics of these timbers were obtained. Subsequently, a comprehensive index system including thermal stability, heat release ability, smoke production capacity, and flame spreading speed was constructed to evaluate the combustion performance of the selected timbers. In addition, a grey correlation method relying on the game theory to assign weight was proposed for the quantitative analysis of the relevant evaluation indexes. As a result, the combustion performance of the six kinds of timber, which was defined as a specific value from poor to good, was as follows: pine (0.8696) > Chinese fir (0.8568) > Oriented Strandboard (OSB) (0.8425) > density board (0.8122) > plywood (0.8087) > elm (0.7909). Timber with poor combustion performance contributes to the reduction in fire risk in buildings. Our suggestions are of great significance for selecting furniture timber from the perspective of the prevention and control of building fires. [ABSTRACT FROM AUTHOR]

With the rapid development of hydrogen energy worldwide, the number of hydrogen energy facilities, such as hydrogen refueling stations, has grown rapidly in recent years. However, hydrogen is prone to leakage accidents during use, which could lead to hazards such as fires and explosions. Therefore, research on the safety of hydrogen energy facilities is crucial. In this paper, a study of high-pressure hydrogen jet flame accidents is conducted for a proposed integrated hydrogen production and refueling station in China. The effects of leakage direction and leakage port diameter on the jet flame characteristics are analyzed, and a risk assessment of the flame accident is conducted. The results showed that the death range perpendicular to the flame direction increased from 2.23 m to 5.5 m when the diameter of the leakage port increased from 4 mm to 10 mm. When the diameter of the leakage port is larger than 8 mm, the equipment on the scene will be within the boundaries of the damage. The consequences of fire can be effectively mitigated by a reasonable firewall setup to ensure the overall safety of the integrated station. [ABSTRACT FROM AUTHOR]

In satellite imagery, clouds obstruct the ground information, directly impacting various downstream applications. Thus, cloud annotation/cloud detection serves as the initial preprocessing step in remote sensing image analysis. Recently, deep learning methods have significantly improved in the field of cloud detection, but training these methods necessitates abundant annotated data, which requires experts with professional domain knowledge. Moreover, the influx of remote sensing data from new satellites has further led to an increase in the cost of cloud annotation. To address the dependence on labeled datasets and professional domain knowledge, this paper proposes an automatic cloud annotation method for satellite remote sensing images, CloudAUE. Unlike traditional approaches, CloudAUE does not rely on labeled training datasets and can be operated by users without domain expertise. To handle the irregular shapes of clouds, CloudAUE firstly employs a convex hull algorithm for selecting cloud and non-cloud regions by polygons. When selecting convex hulls, the cloud region is first selected, and points at the edges of the cloud region are sequentially selected as polygon vertices to form a polygon that includes the cloud region. Then, the same selection is performed on non-cloud regions. Subsequently, the fast KD-Tree algorithm is used for pixel classification. Finally, an uncertainty method is proposed to evaluate the quality of annotation. When the confidence value of the image exceeds a preset threshold, the annotation process terminates and achieves satisfactory results. When the value falls below the threshold, the image needs to undergo a subsequent round of annotation. Through experiments on two labeled datasets, HRC and Landsat 8, CloudAUE demonstrates comparable or superior accuracy to deep learning algorithms, and requires only one to two annotations to obtain ideal results. An unlabeled self-built Google Earth dataset is utilized to validate the effectiveness and generalizability of CloudAUE. To show the extension capabilities in various fields, CloudAUE also achieves desirable results on a forest fire dataset. Finally, some suggestions are provided to improve annotation performance and reduce the number of annotations. [ABSTRACT FROM AUTHOR]

Fire is considered a common hazard for civil structures. Public and administrative buildings are commonly designed by considering the standard fire rating and, in many cases, contain large compartments with central domes, in which fire growth can be significant. Moreover, tanks and underground fortified structures may be constructed as domes to support the heavy soil above. This paper numerically addressed such a case. First, an axisymmetric finite element model was developed and validated to predict the dome's transient, thermal, structural, and thermal-structural behavior. Next, the model was used to conduct a parametric study to investigate the effects of the dome ring reinforcement, thickness, stiffness, central angle, base restraints, load type (external pressure or gravitational), and load ratio on the fire endurance of the dome. Design recommendations to increase the fire endurance of concrete domes were formulated based on the parametric study. [ABSTRACT FROM AUTHOR]

This paper presents a research program called CP2DIMG conducted at the Federation of Environment and Society Research at the University of Corsica. The goal of CP2DIMG is to better understand the influence of emotions on operational personnel's decision-making, aiming to test training systems dedicated to individuals facing high stress during their professional activities. This type of training system is intended to enhance emotional and mental resilience, thereby improving decision-making ability in uncertain situations under the influence of emotions related to the event. For implementation, the method will be tailored to the specificities of two categories of operational personnel: firefighters and municipal police officers. The expected results will address significant demands from operational professionals in the Mediterranean region for firefighting safety but also for large-scale or highly complex interventions. This study fully integrates into the challenges of the Mediterranean region: forest management, risk prevention plans, and preparedness of local actors responsible for crisis management. Furthermore, individuals responsible for crisis management, including local government officials and risk management and security personnel, will be able to use the obtained results for effective decision-making. [ABSTRACT FROM AUTHOR]

The natural wind velocities in tunnels under different natural conditions are distinct, and the longitudinal ventilation velocity significantly impacts the evacuation environment. This paper examines the evacuation conditions and strategies under varying wind velocities in bifurcated tunnels. Using Fire Dynamics Simulator (FDS) and Pathfinder software, the fire development and evacuation of three distinct longitudinal positions in a bifurcated tunnel are simulated. The simulation results demonstrate that the evacuation conditions for disparate fire sources at varying wind velocities are markedly disparate. In consideration of the construction cost and the maximization of evacuation capacity, the width of the evacuation doors at the three locations should be set to 2 m, 1.5 m, and 1.5 m, respectively. Furthermore, an analysis of the safety of individual personnel through Fractional Effective Dose (FED) revealed that directing evacuees towards the upstream of the fire after the fire is detected can significantly reduce individual personnel injuries while ensuring the overall success of the evacuation. [ABSTRACT FROM AUTHOR]

Wildfires cause great losses to the ecological environment, economy, and people's safety and belongings. As a result, it is crucial to establish wildfire susceptibility models and delineate fire risk levels. It has been proven that the use of remote sensing data, such as meteorological and topographical data, can effectively predict and evaluate wildfire susceptibility. Accordingly, this paper converts meteorological and topographical data into fire-influencing factor raster maps for wildfire susceptibility prediction. The continuous convolutional neural network (CCNN for short) based on coordinate attention (CA for short) can aggregate different location information into channels of the network so as to enhance the feature expression ability; moreover, for different patches with different resolutions, the improved CCNN model does not need to change the structural parameters of the network, which improves the flexibility of the network application in different forest areas. In order to reduce the annotation of training samples, we adopt an active learning method to learn positive features by selecting high-confidence samples, which contributes to enhancing the discriminative ability of the network. We use fire probabilities output from the model to evaluate fire risk levels and generate the fire susceptibility map. Taking Chongqing Municipality in China as an example, the experimental results show that the CA-based CCNN model has a better classification performance; the accuracy reaches 91.7%, and AUC reaches 0.9487, which is 5.1% and 2.09% higher than the optimal comparative method, respectively. Furthermore, if an accuracy of about 86% is desired, our method only requires 50% of labeled samples and thus saves about 20% and 40% of the labeling efforts compared to the other two methods, respectively. Ultimately, the proposed model achieves the balance of high prediction accuracy and low annotation cost and is more helpful in classifying fire high warning zones and fire-free zones. [ABSTRACT FROM AUTHOR]

This paper presents a specific 3D computational fluid dynamics model to quantify the effects of the combustion of asphalt road pavement on user safety in the event of a fire in a bi-directional road tunnel. Since the consequences on tunnel users and/or rescue teams might be affected not only by the tunnel geometry but also by the type of ventilation and traffic flow, the environmental conditions caused by the fire in the tunnel under natural or longitudinal mechanical ventilation, as well as congested traffic conditions, were more especially investigated. The simulation results showed that the combustion of the asphalt pavement in the event of a 100 MW fire, compared to the case of a non-combustible road pavement, caused (i) an increase in smoke concentrations; (ii) a greater number of users exposed to the risk of incapacity to escape from the tunnel; (iii) a more difficult situation for the firefighters entering the tunnel upstream of the fire source in the case of natural ventilation; (iv) a higher probability of the domino effect for vehicles queued downstream of the fire when the tunnel is mechanically ventilated. [ABSTRACT FROM AUTHOR]

With the development of the social economy and the improvement of electrification, cables and wires play an important role in people's lives and industrial development. Meanwhile, the large-scale laying of cables has also made them a fire hazard that cannot be ignored in land construction such as residential buildings, utility tunnels, nuclear power plants, refineries, marine systems such as submarines and ships, and airborne systems such as spacecrafts and aircrafts. In this work, studies on fire the characteristics of cables and wires over the last decades have been reviewed. Based on different experimental forms and objects (laboratory wires and commercial cables), this paper summarizes the theories of the fire dynamics in wire combustion, including the models of ignition and flame propagation, the criteria for blowing off and quenching, and the critical conditions for dripping behavior. The effects of materials, layouts, and environments on wire combustion phenomena such as airflow, ambient pressure, oxygen, gravity, and orientation angle have been discussed in detail according to the theories of heat transfer and combustion. In addition, test standards and studies on the fire behavior and release of toxic gases of commercial cables have also been fully described. Through the summary of the above content, it is expected to build a preliminary theoretical framework and future research directions for researchers in the field of cable fires. [ABSTRACT FROM AUTHOR]

This paper addresses the evacuation of people from multipurpose halls and introduces an innovative approach that uses a probabilistic model, specifically the Monte Carlo method, to analyse iterative evacuation processes. The aim is to explore how this modern technology can contribute to the development of effective and safe evacuation plans for mass events. The Monte Carlo method was applied to a specific example of a multipurpose hall that offers different configurations for events such as sports matches, concerts, or performances. The evacuation of people was analysed for two configurations: a hockey match with a capacity of 9500 people and a concert with a capacity of 11,000 people. In both cases, the total evacuation of people from the hall was analysed, and the evacuation time was evaluated when two parameters were changed: speed of movement and preference for door selection. The results of the simulations can provide valuable information for the design of effective safety measures in multipurpose halls and other similar multipurpose venues. This innovative approach to evacuation analysis allows for a comprehensive assessment of the evacuation process, identification of critical areas, and verification of the layout of the space. [ABSTRACT FROM AUTHOR]