Fire Situation Research Articles

Computational Analysis for the Evaluation of Fire Resistance in Constructive Wooden Elements with Protection

Wood is a material whose properties vary depending on different conditions, being particularly vulnerable to changes induced by high temperatures. When exposed to a fire situation, the wood properties suffer degradation, causing a char layer formation. Despite ensuring the protection of the inner core of the wood, the char layer reduces its resistant section. The evaluation of wood behavior under fire conditions is possible through experimental tests, simplified analytical models, and numerical models. To overcome difficulties in the development of experimental tests and in the approximations made to analytical methods, numerical models allow the evaluation of the fire resistance in a parametric way. First, this study will present a numerical model validated with an experimental test, using the finite element method. The validation of the results is based on the evolution of the temperature field, the char layer formation on the wooden elements, and its residual section, as well as the application of the thermal insulation criterion. The second part of the study evaluates the influence of geometric parameters, associated with different wooden constructive models with gypsum board exposed to fire. Different numerical tests are presented to evaluate the thermal and transient analysis of different wooden constructive elements with gypsum board. This type of constructive element presents cavities, making the numerical analysis in the study complex when approaching real models. The methodology applied allowed us to conclude that, at the same time, a smaller distance between wooden centers, a greater dimension of the wooden beam in height and width, as well as a greater thickness of gypsum board guarantee a better performance of the constructive wooden model.

Fire Source Determination Method for Underground Commercial Streets Based on Perception Data and Machine Learning

Determining fire source in underground commercial street fires is critical for fire analysis. This paper proposes a method based on temperature and machine learning to determine information about fire source in underground commercial street fires. Data was obtained through consolidated fire and smoke transport (CFAST) software, and a fire database was established based on the sampling to ascertain fire scenarios. Temperature time series were chosen for feature processing, and three machine learning models for fire source determination were established: decision tree, random forest, and LightGBM. The results indicated that the trained models can determine fire source information based on processed features, achieving a precision exceeding 95%. Among these, the LightGBM model exhibited superior performance, with macro averages of precision, recall, and F1 score being 99.01%, 98.45%, and 99.04%, respectively, and a kappa value of 98.81%. The proposed method for determining the fire source provides technical support for grasping the fire situation in underground commercial streets and has good application prospects.

Intelligent Evacuation Sign Control Mechanism in IoT-Enabled Multi-Floor Multi-Exit Buildings.

In contemporary evacuation systems, the evacuation sign typically points fixedly towards the nearest emergency exit, providing guidance to evacuees. However, this static approach may not effectively respond to the dynamic nature of a rapidly evolving fire situation, in particular if the closest emergency exit is compromised by fire. This paper introduces an intelligent evacuation sign control mechanism that leverages smoke and temperature sensors to dynamically adjust the direction of evacuation signs, ensuring evacuees are guided to the quickest and safest emergency exit. The proposed mechanism is outlined through a rigorous mathematical formulation, and an ESP heuristic is devised to determine temperature-safe, smoke-safe, and congestion-aware evacuation paths for each sign. This algorithm then adjusts the direction light on the evacuation sign to align with the identified evacuation path. To validate the effectiveness of this approach, fire simulations using FDS software 6.7.1 were conducted in the Taipei 101 shopping mall. Temperature and smoke data from sensor nodes were utilized by the ESP algorithm, demonstrating superior performance compared to that of the existing FEL algorithm. Specifically, the ESP algorithm exhibited a notable increase in the probability of evacuation success, surpassing the FEL algorithm by up to 34% in methane fire scenarios and 14% in PVC fire scenarios. The significance of this improvement is more pronounced in densely congested evacuation scenarios.

Forest fire risk zoning based on fuzzy logic and analytical network process.

Forest fires have a significant impact on human life, property safety, and ecological environment. Deve-loping high-quality forest fire risk maps is beneficial for preventing forest fires, guiding resource allocation for firefighting, assisting in fire suppression efforts, and supporting decision-making. With a multi-criteria decision analysis (MCDA) method based on geographic information systems (GIS) and literature review, we assessed the main factors influencing the occurrences of forest fires in Youxi County, Fujian Province. We analyzed the importance of each fire risk factor using the analytic network process (ANP) and assigned weights, and evaluated the sub-standard weights using fuzzy logic assessment. Using ArcGIS aggregation functions, we generated a forest fire risk map and validated it with satellite fire points. The results showed that the areas classified as level 4 or higher fire risk accounted for a considerable proportion in Youxi County, and that the central and northern regions were at higher risk. The overall fire risk situation in the county was severe. The fuzzy ANP model demonstrated a high accuracy of 85.8%. The introduction of this novel MCDA method could effectively improve the accuracy of forest fire risk mapping at a small scale, providing a basis for early fire warning and the planning and allocation of firefighting resources.

Fire detection using deep learning methods

Fire detection is an important task in the field of safety and emergency prevention. In recent years, deep learning methods have shown high efficiency in solving various computer vision problems, including detecting objects in images. In this paper, monitoring wildfires was considered, which allows you to quickly respond to them and prevent their spread using deep learning methods. For the experiment, images from the satellite and images from the FireWatch sensor were taken as initial data. In this work, the deep learning algorithms you only look once (YOLO), convolutional neural network (CNN), and fast recurrent neural network (FastRNN) were considered, which makes it possible to determine the accuracy of a natural fire. As a result of the experiments, an automated fire recognition algorithm using YOLOv4 deep learning methods was created. It is expected that the results of the study will show that deep learning methods can be successfully applied to detect fire in images. This may lead to the development of automated monitoring systems capable of quickly and reliably detecting fire situations, which will help improve safety and reduce the risk of fires.

Field detection of indoor fire threat situation based on LSTM-Kriging network

Obtaining the fire development information quickly and accurately is an important part of indoor fire emergency evacuation and rescue. However, at present, buildings only use fire sensors to monitor whether a fire is occurring, and there is a lack of means to obtain real-time threat situation information on the development of fires. An LSTM-Kriging neural network model is proposed in this paper, which can quickly generate an indoor fire threat field using the temporal data of fire sensors when an indoor fire occurs. Firstly, the Kriging algorithm was improved by replacing the Euclidean distance used in the variational function with the nearest passable distance to obtain a more accurate representation of the indoor threat distribution trend, and combined it with the Long Short-Term Memory (LSTM) network to construct the LSTM-Kriging network. Secondly, indoor fire scenarios were constructed in PyroSim, and fire parameters were simulated randomly. After data augmentation, the indoor fire threat situation dataset was generated. Finally, the optimized LSTM-Kriging model was trained and compared with Transformer, LSTM, and Recurrent Neural Network (RNN) on the indoor fire hazardous situation dataset. The experimental results show that LSTM-Kriging is the most effective model compared with several models mentioned above and the prediction of threatening situation reaches an accuracy of 97.8 %, with an average computation time of 0.19s on the GPU, which can meet the requirement of real-time fire situation monitoring. The indoor fire threat situation field detection method proposed in this paper achieves intelligent indoor fire detection and improves the building fire safety.

A study on rapid simulation of mine roadway fires for emergency decision-making

In traditional mine fire simulation, the FDS simulation software has been verified by large-scale and full-size fire experiments. The resulting calculations closely align with real-world scenarios, making it a valuable tool for simulating mine fires. However, when a fire occurs in a mine, utilizing FDS software to predict the fire situation in the mine entails a sequence of steps, including modeling, environmental parameter setting, arithmetic, and data processing, which takes time in terms of days, thus making it difficult to meet the demand for emergency decision-making timelines. To address the need for rapid predictions of mine tunnel fire development, a method for swiftly estimating environmental parameters and the concentration of causative factors at various times and locations post-fire has been devised. FDS software was employed to simulate numerous roadway fires under diverse conditions. Parameters such as fire source intensity, roadway cross-sectional area, roadway wind speed, roadway inclination angle, time, and others were utilized as the input layer for a neural network. In contrast, wind flow temperature, carbon monicide (CO) concentration, fire wind pressure, visibility, and others were designated as the output layer for training the neural network model. This approach established a fire prediction model to resolve issues related to time-consuming numerical simulations and the inability to provide a rapid response to disaster emergencies. The trained neural network model can instantaneously predict the environmental parameters and concentrations of the causative factors at different times and locations. The model exhibits an average relative error of 12.12% in temperature prediction, a mean absolute error of 0.87 m for visibility, a mean absolute error of 3.49 ppm for CO concentration, and a mean absolute error of 16.78 Pa for fire wind pressure. Additionally, the mean relative error in density is 2.9%. These predictions serve as crucial references for mine fire emergency decision-making.

Dataset of United States Incident Management Situation Reports from 2007 to 2021

This paper presents a unique 15-year dataset of Incident Management Situation Reports (IMSR), which document daily wildland fire situations across ten geographical regions in the United States. The IMSR dataset includes summaries for each reported day on national and regional wildfire activities, wildfire-specific activities, and committed fire suppression resources (i.e., personnel and equipment). This dataset is distinct from other wildfire data sources as it provides daily information on national fire suppression resource utilization, national and regional preparedness levels, and management priority for each region and fire. We developed an open-source Java program, IMSR-Tool, to process 3,124 IMSR reports available from 2007 to 2021 to generate this structured IMSR dataset, which can be updated when future reports become available. The dataset presented here and its future extension enable researchers and practitioners to study historical wildfire activity and resource use across regions and time, examine fire management perceptions, evaluate strategies for fire prioritization and fire resource allocation, and exploit other broader usage to improve wildfire management and response in the United States.

Probabilidade de falha de pilares de concreto armado em situação de incêndio

Abstract The reliability of reinforced concrete (RC) columns in a fire situation is investigated in this paper by combining the General, Equilibrium, and Isotherm at 500 ° C Methods. The well-known Monte Carlo (MC) and the First Order Reliability Method (FORM) are used in a parametric study for the column’s safety. The columns, in a biaxial bending-compression situations, are analyzed by an algorithm that takes into consideration physical and geometric nonlinearities. The dead and live load actions, steel and concrete material strengths, section geometry, model error, and temperature are among the random variables taken into consideration. The findings indicate that increasing the cover can dramatically reduce the probability of failure.

Как создавалась мировая пожарная статистика

Purpose. The article presents a brief history of the world fire statistics creation since the formation of the Center for World Fire Statistics in 1995. The article presents the results of the main indicators statistical study of the fire situation in the world for 30 years and the distribution of integral fire risks using the example of several countries (Russia, Hungary, Czech Republic, Austria, Great Britain, Poland, USA). The main objectives of the study are to review the periods of global fire statistics development, to analyze indicators characterizing the number of fires and their consequences in the world for the period from 2012 to 2021. Methods. Methods of mathematical statistics have been used. Findings. The article presents the main historical milestones in the creation of a system for recording global indicators of the situation with fires and their consequences. The results of a statistical analysis of the fire situation in the world for the period from 1993 to 2021 are summarized. The dynamics of relative indicators of the fires and death toll in fires in the above countries is presented. Research application field. The results of the study can be used by fire service specialists from around the world for a comparative analysis of the fire risk level in countries and cities. Conclusions. Over 28 years the Center for Fire Statistics (hereinafter referred to as CFS) has analyzed 104 million fires, in which 1.1 million people died. At the same time the Center annually collects statistical data from 35–40 countries and the largest cities in the world (with a delay of 2 years). In total, CFS reports have been translated into 8 languages. Since 1993 the CFS has published 28 statistical reports in the public domain. Thus, it becomes possible to analyze the fire situation in countries and cities around the world.

Numerical study of vertical compartmentation in compartments in fire situations

Numerical study of vertical compartmentation in compartments in fire situations

Characterization of the elevated temperature behaviour of demountable shear connectors in steel-concrete composite beams through push-out tests

Demountable shear connectors are a key component of reusable structural systems. The intention behind the development of these systems is substituting, or at least providing a valid alternative to the widely utilized beam-to-slab connection via shear studs, which are welded to the flange of the steel profile and embedded in the concrete slab, therefore eliminating the possibility for the slabs to be reused in a new structure. To ensure that these demountable connectors are a valid option in practice, it is necessary to analyse them under different actions, including the fire situation. This is the primary purpose of the experimental campaign presented in this investigation, where a series of push-out tests at elevated temperatures are performed to extend the previously available results at ambient temperature, as a response for the need of characterization of the behaviour of the proposed demountable shear connectors in the fire situation, thus facilitating their applicability in the construction sector. This study shows how temperature affects the shear resistance of the connectors, their ductility, and the slip resistance. The available design provisions in EN 1993–1-8 and the reduction factors in Annex D of EN 1993–1-2 for bolts loaded in shear are tentatively applied to high temperatures, as a way to assess their validity for fire design.

Análise de confiabilidade de seções transversais de vigas simplesmente apoiadas de CA em situação de incêndio de acordo com normas brasileiras

Abstract This article aims to evaluate the reliability of cross-sections of simply supported reinforced concrete (RC) beams in a fire situation considering the utilization of the simplified method proposed in the Brazilian standard NBR 15200 [1]. The impact of the concrete cover, the live-to-total load ratio, and the quantity of heated beam faces were all taken into account in this analysis. Besides, a sensitivity analysis is performed showing the random variables that are of major impact on reliability. In the end, it was concluded that the number of heated faces of the beam is very important to the safety of these structures. Also, it is found that a higher concrete cover can promote an increase in reliability and that a bigger proportion of live load relative to the total load generates a decrease in the probability of failure. Regarding the sensitivity analysis, the fire temperature and the concrete cover were the variables with the greatest observed impact.

Dynamic Communication in Emergency Response: A Data-Driven Evaluation with the Emergency Communication Test

This research provides a data-driven assessment of dynamic communication in emergency response, highlighting important findings supported by actual data. In comparison to police officers in law enforcement situations, EMTs responded to medical crises 25% quicker, according to the response time research. When it came to communication accuracy, firemen performed at a 96% accuracy rate during fire situations, compared to a 91% accuracy rate in law enforcement circumstances. When compared to law enforcement situations, there was a 3% improvement in the completeness of information shared in fire incidents. Additionally, compared to accident situations, police officers' communication efficacy in law enforcement occurrences was 2.3% greater. These results highlight how crucial customized communication plans, data-driven insights, and technology and training integration are to maximizing dynamic communication in emergency response systems.

Research on typical fire impact and emergency escape planning in Yushuling Coal Mine

Abstract Mine fire is a very dangerous underground disaster. In order to better study the situation and influence range of mine fire, this paper uses Ventsim to establish a three-dimensional visual ventilation model of Yushuling coal mine. The fire simulation of coal mining face and central substation is carried out by simulation method to study the change law of CO concentration, visibility and temperature in different places. The results show that the CO concentration and visibility change greatly in the downstream of the fire source point when a fire occurs in the coal face; As for the temperature, the temperature at the fire source varies greatly, and the temperature below the inclined air shaft hardly changes; All of them show a decreasing trend with the increase of the distance from the fire source; In case of fire in central substation, there are great changes in all substances in the roadway before and after the air door is opened and closed; After the air door is closed, the CO concentration drops sharply, the change rate at the end of the fire is reduced, the end time is advanced to half of the original, the temperature change is relatively small, and the visibility remains low for a long time after the damper is closed and the recovery time is late. The research results have important guiding significance for the escape of underground fire.

Математические модели поддержки управленческих решений по снижению уровня пожарной опасности в административно-территориальных единицах Вьетнама

Purpose. The article classifies and analyzes significant factors affecting fire situation and fire risks in the administrativeterritorial units (ATUs) of Vietnam from 2019 to 2022. Based on this data, mathematical models have been developed to show the relationship between fire risks and influencing factors. Optimal managerial decisions have been formulated to reduce fire risks and the level of fire hazard in Vietnam's ATUs based on the obtained models. Methods. Methods of mathematical statistics, systems analysis, and mathematical modeling were used. Findings. The study analyzed statistical data and developed three multiple regression mathematical models showing the dependence of fire risks on significant factors. Research application field. The results of the study can be used to support managerial decisions to ensure fire safety in Vietnam. Conclusions. The analysis of various significant and less significant factors affecting fire situation draws attention to specialists in fire prevention. The developed mathematical models have a sufficiently high level of adequacy and significance. The application of the managerial decisions derived from these models had a positive impact on reducing fire risks and the level of fire hazard in ATUs. Specifically, the risk of human fatalities in fires (Rf) decreased by 22.1%, the risk of injuries from fires (Ri) decreased by 14%, and the risk of direct fire damage (Rd) decreased by 16.1%. The obtained research results enable the improvement of managerial decisions in fire safety operations and reduce fire hazard levels across the country's territories.

Simulated interventions based on virtual reality to improve emergency evacuation under different spatial perception models

People's emergency evacuation behavior is heavily reliant on spatial knowledge. Different spatial cognitive ways shape spatial knowledge differently. To investigate the effect of different cognitive ways on people's wayfinding performance in an indoor fire situation. The study used a virtual reality simulation of a shopping mall environment, and 128 participants were recruited for the experiment. The experiment divided the participants into six groups and placed them in two scenarios, one with and one without fire. This study also explored two other intervention behaviors to improve the participants' evacuation behaviors. The results showed that participants who used navigation had shorter walking distances and wayfinding times than those who used the You are here (YAH) map. Secondly, participants in the fire scenario walked longer distances and took longer to evacuate than those in the no-fire scenario, and they perceived successful evacuation to be more difficult. Thirdly, the intervention behavior effectively improved the participants' evacuation behavior and increased the efficiency of the evacuation. The leader's intervention was more effective in helping participants evacuate than the range intervention in this experimental setting. The findings may advance the understanding of human evacuation behavior and provide important practical implications for people's safety and emergency preparedness in shopping malls.

Prospects of application of modern technologies of remote sensing of the earth in the forest industry

Currently, forest enterprises are characterized by depletion of forest resources and their significant remoteness. To solve a whole range of tasks related to forest management, monitoring of the environmental situation, control of the forest fire situation, remote sensing systems of the Earth have been developed and, to date, are successfully used. At the moment, such systems are located in almost every region, allowing dispatch centers to quickly perform their work. On the basis of the centers there are several operators who transmit information to the forestry and monitor a lot of video surveillance cameras in real time. Cameras help to detect fires and various smoke points much faster and more efficiently than satellite photography. However, the existing methods for detecting fires in forests have long been outdated and require the introduction of modern auxiliary engineering software solutions in this process. Research and development of systems that allow tracking fires that may be located far from human habitats, and their implementation in the work of forest protection enterprises is a top priority in the Ministries of Forestry.

A building information modeling-based fire emergency evacuation simulation system for large infrastructures

During fire emergencies, people in large infrastructures are difficult to comprehend real-time fire dynamics and identify available evacuation routes, leading to lengthy evacuation time and unnecessary loss of life and property. This study aims to develop a fire emergency evacuation simulation system for large infrastructures. This system integrates the data from building information modeling platform, fire dynamics simulator, and evacuation simulation platform through customized developments. Real-time fire situation is transmitted to the evacuation simulation platform to assess the impact of dynamic fire spread on the evacuation of people. The behavioral and psychological characteristics of evacuees of different genders and age groups are considered in simulated evacuations. An algorithm for optimizing evacuation route planning is designed to improve the utilization of each evacuation exit and provide a visualization of evacuation routes. This proposed system was validated at a busy subway station in China. The evacuation efficiency increased by 12.87%. It is concluded that this system can provide real-time dynamic optimal evacuation route planning for enhanced emergency management.

МЕТОДИКА ОЦЕНКИ ЛЕСОПОЖАРНЫХ РИСКОВ ИХ ВОЗМОЖНЫХ ПОСЛЕДСТВИЙ

The article discusses the issues of assessing forest fire risks and factors determining the likelihood of their occurrence. Using methods of combinatorics of risk factors, the most dangerous fire situations in forest areas were identified. An assessment method and a forecast model are proposed that takes into account the influence of variable and conditionally constant factors on the implementation of forest fire risks, which makes it possible to further rank forest areas according to the likelihood of forest fires and the level of costs for their prevention and elimination.