Building Fire Research Articles

Beyond the Ire of Flames: Work Experiences of Bureau of Fire Protection (BFP) Station Chiefs

Fire occurrences have significantly risen in recent years. These fires endanger lives, cause economic damage, and harm the environment. Uncontrolled fires are a major reason for building collapses and injuries. Building fires cause trauma due to the loss of belongings and the building itself. Existing fire safety systems are failing due to unaddressed factors causing building fires (Rathnayake et al., 2020). This study explored the experiences of the station chiefs of the Bureau of Fire Protection in Iloilo Province, Philippines, through a qualitative research method. The participants of this study were the selected ten (10) BFP station chiefs. All participants were subjected to an individual in-depth interview. The findings of this study are described in emergent themes. Six (6) emergent themes described the positive and negative experiences of the informants in the performance of their duties. For the positive experiences, two (2) emergent themes were developed, namely Personal Satisfaction Through Job Fulfillment and Workplace Camaraderie and Support. For the negative experiences, four (4) emergent themes were also developed, namely: Inadequate Resources and Financial Challenges, Complexity of Leadership and Personnel Management, Bombarded with Public Dissatisfaction, and Operational Delays and Equipment Failures. Meanwhile, three (3) emergent themes were formulated to explain how the participants address the challenges encountered in the performance of their duties, namely: Empowering Collaboration and Support Networks, Bolstering Preventive Measures and Education, and Embodying Resilience and Adaptability. Finally, three (3) emergent themes were created to express the aspirations of participants to improve the quality of their services, namely Continuous Training and Professional Development, Adequate Fiscal Allocation and Service Facilities, and Improvement of Personnel and Staffing Issues. Based on the emergent themes identified, it is recommended that the fire station chiefs be enhanced by providing them with specialized training and career development programs. This can be accomplished by the successful integration of the latest firefighting technologies in the specialized training to enhance the response effectiveness of their station.

A fire risk pre-warning framework for high-rise buildings based on unascertained method.

The growing global interest in preventing and controlling fires in high-rise buildings reflects the increasing significance of this issue today. This research aims to establish an early warning framework for fire risk in high-rise buildings. Firstly, considering the importance of a scientific indicator system for the application of the model, this study combines the event analysis method with the building design fire code to identify 11 key risk factors that have a far-reaching impact on the prevention of fires in high-rise buildings. Based on identifying the risk factors, a high-rise building fire risk warning tree is also established, which scientifically solves the problem of the indicator system of the warning object. Subsequently, in response to the various complex issues arising from the uncertainty of fire occurrence in high-rise buildings, this study adopts the unascertained method to model the fire risk of high-rise buildings for early warning. In addition, the developed methodology was empirically validated through case studies and analyses of empirical data on fire risks in nine representative high-rise buildings. The results of the unascertained method were also compared with the results of the K-means method, from which it was concluded that the unascertained method can predict building fires more accurately. The research results provide a reliable decision support system for fire disaster prevention and control in high-rise buildings.

Integration of Proactive Building Fire Risk Management in the Building Construction Sector: A Conceptual Framework to Understand the Existing Condition

Integration of Proactive Building Fire Risk Management in the Building Construction Sector: A Conceptual Framework to Understand the Existing Condition

A transient network model for cross-regional layered fire smoke diffusion in high-rise buildings

At present, research on smoke diffusion in high-rise building fires mainly focuses on the single smoke layer propagation within a limited area, with less attention paid to the smoke propagation under the joint action of the fire floor and the vertical shaft area. Moreover, research on fire spread often focuses on the composition of combustible materials and building structures, neglecting natural ventilation. To address the above issues, we propose a transient network model for layered smoke diffusion (CLDTN) based on multi-region integration and complex control parameters. This model considers the smoke stratification mode of the fire floor affected by the fire source and ventilation, as well as the vertical stratification mode of the shaft affected by the stack effect, achieving cross-regional smoke stratification and diffusion modeling through smoke coupling. Then, a time-dependent transient network model is used to analyze furniture materials and open ventilation conditions of doors and windows, and a differential equation for energy conservation in multiple scene layers is constructed to analyze the dynamic behavior of smoke propagation.The comparative experiment of smoke propagation between CLDTN and the Fire Dynamics Simulator (FDS) shows that CLDTN is more efficient in computation time and the model is more accurate.

Automated image-based fire detection and alarm system using edge computing and cloud-based platform

To tackle the increasing wildfire challenges, this paper presents an automated image-based fire detection and alarm system utilizing edge computing and a cloud-based platform, specifically designed for urban building fire detection. The system captures both RGB and infrared images from thermal cameras and employs existing computer vision techniques to detect fire characteristics such as flames and smoke. By integrating edge computing, the system minimizes latency to enhance the accuracy of fire detection and alarm activation. The cloud platform supports extensive data storage, analysis, and remote monitoring, which can ensure data accessibility and scalable data management. The proposed system descriptions include a detailed system architecture design, data collection, and the selection and application of detection algorithms that leverage both RGB and thermal image data for fire detection. Using the campus building and surrounding risk-prone areas as a testbed, the proposed system demonstrated desired fire detection capabilities and a robust solution to quickly identify and respond to fire incidents within the urban area. The proposed system functionalities can be scaled and adapted to other fire risk-prone areas.

Dual-agent intelligent fire detection method for large commercial spaces based on numerical databases and artificial intelligence

Fires in commercial buildings can lead to significant casualties and property damage. Thus, rapidly and accurately identifying the source of a fire and its intensity is crucial for guiding rescue operations. This study aims to explore the use of distributed fiber optic temperature sensing systems, combined with deep learning algorithms, to predict key information about fires in commercial buildings, including the location of the fire source, the intensity of fire development, and the critical planar distribution of carbon monoxide. Based on the Fire Dynamic Simulation (FDS) data, a dual-agent model was developed, and with a sensor sampling interval of 3 s, 30 s of historical data achieved prediction accuracies of 94.23 % for fire intensity and 99.28 % for fire location. Upon reducing the intervals for fire location, the accuracy reached 93.56 %. Additionally, the Random Forest algorithm for mapping carbon monoxide distribution demonstrated an overall Mean Absolute Percentage Error (MAPE) of 5.87 %. Within the 0–150 ppm range, the MAPE was 2.46 %, far below the error levels of existing CO sensors. The database configurations and the sensors' spatiotemporal arrangement were also optimized to rapid and reliable fire prediction. This research demonstrates the feasibility of using artificial intelligence for critical fire scene information detection.

Experimental study of flame morphology in slope buildings under the influence of aspect ratio and ambient wind

Slope structures are commonly used in buildings, but such slope structures can exacerbate the rate of fire spread, complicate building fire safety and suppression, and lead to injuries, deaths, and property damage. Particularly, the thermal hazards posed by ambient wind on sloped fires remain unquantified. In this study, a propane burner was used to simulate the flame morphology behavior of a sloped building fire. 480 tests were conducted with varying aspect ratios (1∼8) of the fire source, different ambient winds (0 m/s∼3.5 m/s), and slope inclination angles (0°∼40°). Results demonstrate that the flame length initially increases and then decreases with an increment in ambient wind velocity. Moreover, with an increased aspect ratio of the fire source (length-to-width ratio), the turning point at which the flame length peaks shift to occur at a lower wind velocity threshold. The tilt angle and height of the flame change (respectively increases and decreases) significantly at lower ambient wind speeds and they tend to asymptotically approach respective constant values. As the ambient wind is further promoted. Existing physical models of flame morphology do not consider the combined effects of fire source aspect ratio, slope inclination angle, and ambient wind. A new slope entrainment restriction factor (EF) was proposed based on an analysis of the upward and leeward entrainment volumes. Using this factor, new models for flame morphology (including flame length, flame tilt angle, and flame height) were established. The accuracy and generalizability of the new flame models have been validated using reference data.

Real-time detection and temperature forecasting of large-space building fires using machine learning

Real-time detection and temperature forecasting of large-space building fires using machine learning

Applications of artificial intelligence in enhancing building fire safety

Applications of artificial intelligence in enhancing building fire safety

Development of a framework to track life safety levels of occupants in the event of a fire in buildings

ABSTRACT In the operational phase of buildings, tracking what modifications can have an impact on the expected facility performances through manual checks can be impractical, albeit very important from a facility management perspective. The transition from the traditional prescriptive method towards the performance-based approach for building design and management may facilitate quantitative assessment of performances and may lead to conscious decisions which includes preparedness in the event of emergencies. The main goal of this paper is to develop a framework, compliant with a Building Information Modelling (BIM) environment, to track fire safety performance levels, in terms of life safety of occupants. As long as this is done through the definition of Key Performance Indicators (KPIs) that are being tracked continuously, facility managers can have a comprehensive and updated overview of current performances of a facility. More specifically, a single representative KPI was set and evaluated for several fire scenarios, using a set of simulation and data management tools. The results of the assessment were processed and transformed into a visual representation, referenced to a grid superimposed over the building layout to quantitatively represent the safety of occupants at every point of the building. The results were then sent back and displayed into the BIM model, for seamless accessibility by facility managers. The proposed methodology was tested on a pilot building, assuming two fire scenarios. This approach enabled supervision of a building’s compliance with fire safety requirements, in terms of occupants’ life safety, throughout the building lifecycle.

Modelling Residential Fire Vulnerability of Denmark

AbstractThis study analyzes socio-economic demographics (including Geomatic conzoom® segmented demographic variables) as well as building and property registration information as risk factors in relation to the prevalence of residential building fires within 100 m × 100 m cells. The logistic regression model achieved a receiver operating curve (ROC) of 0.74 and a precision-recall curve of 0.12 on the testing dataset. The model identifies 19 significant variables related to the risk of residential fire. The top 5 highest performing variables in our model and their odds ratios are the following: number of people (OR 1.25), Multi/family residence-building type (OR 1.20), number of buildings (OR 1.18), conzoom® Type C—Country/Rural Communities (OR 0.85), construction year (OR 0.87). These results indicate that socio-economic factors play a large role in influencing fire vulnerability within residential areas and can help prioritize resource allocation to reduce fire vulnerability in the identified risk factor groups.

Experimental study on the transitional behavior of the room fire under sub-atmospheric pressures

Fuel diffusion combustion and flame behavior within a confined space is a fundamental scientific problem in the energy and combustion field, it involves complicated chemical reaction, fire behavior, thermal dynamics, heat transfer, room-opening flowing and sub-processes of over-ventilated and under-ventilated room fire. However, previous classical knowledges and models about it are usually at standard atmospheric pressure (100 kPa), no work considers the fire evolution and flame behavior within the room for various sub-atmospheric pressures. In this paper, the experiment is carried by utilizing a 0.3 m × 0.3 m × 0.3 m reduced-scale cubic room with an opening of various dimensions, and using propane as fuel to simulate fuel combustion inside, which was placed at the Low Pressure Chamber creating various atmospheric pressures. The flame behavior and temperature evolution within the room was recorded and measured with 693 experimental conditions. The experimental results show that, (1) the air inflowing rate through opening into room and heat release rate within the room increase with atmospheric pressure; (2) the flame extinction or reaching well-mixed condition are more easily occurred for lower atmospheric pressure and opening dimension. These characteristic parameters and fire behaviors could be well described by new corrected opening ventilation factor involving effect of atmospheric pressure and the opening dimension. The research work fills the gap in basic scientific research of building fire at sub-atmospheric pressures, which also has important practical applicability of urban building fire protection design code (such as the materials in wall) in different altitudes, room fire suppression, and protecting urban safety.

Evacuation safety assessment in corridor-type high-rise building under fires

This study aims to elucidate the characteristics of personnel evacuation in corridor-type high-rise building under fires and to assess their evacuation performance. Utilizing Building EXODUS software, the study simulated the evacuation behavior of occupants, particularly focusing on the impact of corridor structures on the building's fire evacuation capabilities. A performance evaluation model was developed, which incorporates factors such as building conditions and natural environments. This model employs an enhanced Analytic Hierarchy Process (AHP) combined with Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) analysis for more accurate assessment. The findings reveal that traditional evacuation performance assessment methods, which often overlook the impacts of corridor location, time of day, and illumination conditions, are not adequately suited for the evaluation of evacuation performance in corridor-type high-rise building under fires. Under similar external conditions, the evacuation time for buildings with connecting corridors is significantly shorter than that for conventional high-rise buildings, showing reductions of 25.0 %–34.0 % at the same time of day and 26.0 %–37.8 % under the same illumination conditions. Additionally, nighttime evacuations are 29.4 % longer than those conducted during daytime. The implementation of a layered evacuation strategy reduces evacuation time by 9.7 % compared with free evacuation methods. The enhanced model proposed in this study provides a more effective framework for assessing the evacuation performance of corridor-type high-rise buildings.

Simulated fire video collection for advancing understanding of human behavior in building fires.

The goal of the present research was to develop a video collection of simulated fires to investigate how people perceive growing building fires. In fire safety science, a critical factor to occupant responses to building fires is the pre-movement period, determined by how long it takes an individual to initiate taking protective action with an incipient fire. Key to studying the psychological processes that contribute to the duration of the pre-movement period is presenting human subjects with building fires. One approach used in previous research is to present videos of building fires to individuals via scenarios. The numerical simulations used to model fire dynamics can be used to render videos for these scenarios. However, such simulations have predominantly been used in fire protection engineering to design buildings and are relatively inaccessible to social scientists. The present study documents a collection of videos, based on numerical simulations, which can be used by researchers to study human behavior in fire. These videos display developing fires in different types of rooms, growing at different rates, different smoke thickness, among other characteristics. As part of a validation study, participants were presented with subsets of the video clips and were asked to rate the perceived risk posed by the simulated fire. We observed that ratings varied by the intensity and growth rate of the fires, smoke opacity, type of room, and where the viewpoint was located from the fire. These effects aligned with those observed in previous fire science research, providing evidence that the videos could elicit perceived risk using fire simulations. The present research indicates that future studies can utilize the video library of fire simulations to study human perceptions of developing building fires as situational factors are systematically manipulated.

Evolution Process of Fault Silica Aerogel under High Temperatures: A Molecular Dynamics Approach.

Building fire will seriously threaten human safety. Silica aerogel with low thermal conductivity and thermal stability as fire-retardant material has been widely used in building fireproof structures. However, the natural fragility of silica aerogel will limit its application. In this work, the effects of faults on the thermal stability of silica aerogel are studied by molecular dynamics simulation with large simulation time (20 ns). Additionally, the atomic model of silica aerogel with random faults is built by a straining structure (tensile strains are 10%, 20%, 30%, and 40%). It is found that when the tensile strain is less than 20%, the silica backbone can remain stable. The effects of faults on the thermal stability can be neglected. The silica backbone thermally vibrates during the heating process. However, when the tensile strain is over 30%, it is observed that the faults will enhance the silica backbone merging. Silica aerogel can be stable under 800 K. It is believed that the results of this study will pave the way for the development of fireproof materials.

Evaluation of Fire Protection System in Library Building Universitas Negeri Padang

The fire of a building will have a major impact on the building owner, both in the form of material, moral and environmental losses. In residential areas, offices and other public facilities often cause casualties. Therefore, a building needs to take into account the risk of fire so that building users can carry out activities safely and comfortably. One of the causes of fires is the fire suppression system that has not received full attention, where there is often an inadequate building fire protection system. The UNP Library Building is one of the public facilities buildings that are always crowded with students. It is necessary to evaluate the availability of the fire protection system to ensure the safety and comfort of its users. This research is in the form of an evaluation of the availability and completeness of the available fire protection systems and their remedies against the threat of fire. The data was taken through direct observation and question and answer and the distribution of questionnaires with the library. From the results of the analysis of the fire protection system inspection of the UNP Library building, it is known that on the 1st floor the lowest rediness value is 18.9%, for the 2nd and 3rd floors is 44.1%, the 4th and 5th floors are 45.9% and 45.6 % fulfilled the requirements based on the Minister of Public Works Regulation No. 26/PRT/M/2008.

Safety in Housing for Older Adults-A Qualitative Case Study.

Housing solutions for older adults aim at providing a safe environment to live in. The construction is heavily based on aspects of physical safety, often disregarding the social aspects of feeling safe. The older adults themselves are looking for a living environment that supports their individual capacities and social networks. The living environment can contribute to their physical, social, and emotional safety by providing accessible housing and spaces for community building. Interviews with older adults who had recently lived through a building fire highlighted the importance of the community in daily life, in emergency as well as in recovery from hazards. The results show that community building is related to access to common-use spaces and daily interaction with neighbors. The shared spaces in the immediate surroundings can enhance community cohesion and generate peer support. The common-use spaces and public facilities in urban environment have a significant role in emergencies and in the process of recovery from adversities.

Drones can be used to provide dispatch centres with on-site photos before arrival of EMS in time critical incidents

BackgroundDrones are able to deliver automated external defibrillators in cases of out-of-hospital cardiac arrest (OHCA) but can be deployed for other purposes. Our aim was to evaluate the feasibility of sending live photos to dispatch centres before arrival of other units during time-critical incidents. MethodsIn this retrospective observational study, the regional dispatch centre implemented a new service using five existing AED-drone systems covering an estimated 200000 inhabitants in Sweden. Drones were deployed automatically over a 4-month study period (December 2022–April 2023) in emergency calls involving suspected OHCAs, traffic accidents and fires in buildings. Upon arrival at the scene, an overhead photo was taken and transmitted to the dispatch centre. Feasibility of providing photos in real time, and time delays intervals were examined. ResultsOverall, drones were deployed in 59/440 (13%) of all emergency calls: 26/59 (44%) of suspected OHCAs, 20/59 (34%) of traffic accidents, and 13/59 (22%) of fires in buildings.The main reasons for non-deployment were closed airspace and unfavourable weather conditions (68%). Drones arrived safely at the exact location in 58/59 cases (98%). Their overall median response time was 3:49 min, (IQR 3:18–4:26) vs. emergency medical services (EMS), 05:51 (IQR: 04:29–08:04) p-value for time difference between drone and EMS = 0,05. Drones arrived first on scene in 47/52 cases (90%) and the largest median time difference was found in suspected OHCAs 4:10 min, (IQR: 02:57–05:28). The time difference in the 5/52 (10%) cases when EMS arrived first the time difference was 5:18 min (IQR 2:19–7:38), p = NA. Photos were transmitted correctly in all 59 alerts. No adverse events occurred. ConclusionIn a newly implemented drone dispatch service, drones were dispatched to 13% of relevant EMS calls. When drones were dispatched, they arrived at scene earlier than EMS services in 90% of cases. Drones were able to relay photos to the dispatch centre in all cases.Although severely affected by closed airspace and weather conditions, this novel method may facilitate additional decision-making information during time-critical incidents.

An investigation using resampling techniques and explainable machine learning to minimize fire losses in residential buildings

Urban residential fires seriously threaten public safety, causing significant property damage and severely impacting urban sustainability. To enhance the understanding of urban residential fire risks, a framework that combines tree-based machine learning algorithms and resampling techniques is proposed to predict damage and casualties in residential building fires. All algorithms achieved similar results on the original dataset, with 86 % and 93 % accuracy and the highest average F1 scores of 61 % and 51 %, respectively. Various resampling techniques addressed the issue of data imbalance, with the combination of random undersampling and SMOTE achieving the best model performance, elevating the average F1 scores to 75 % and 77 %, representing improvements of 14 % and 26 % over the original dataset, respectively. Furthermore, the internal mechanisms of the model were explored using the explainable Shapley additive explanations, which identified the key features influencing model outputs. Additionally, the study revealed significant heterogeneity in different regions, sources of ignition, causes of fires, types of residences, locations of fire origin, and types of households. This research not only improves emergency response strategies for urban residential fires but also provides tailored fire safety policies to reduce risks in various urban environments effectively.

Machine learning-driven real-time identification of large-space building fires and forecast of temperature development

In firefighting, real-time knowledge of fire dynamics is crucial yet often unavailable. The building geometry and measured gas temperatures are integrated to ascertain fire states and predict temperature evolution, utilizing a machine learning framework that combines long short-term memory networks and transfer learning. The model is pre-trained on datasets with 1000 samples from parametric fire models and 200 from field simulations, enabling real-time predictions with on-site data. Numerical simulations in portal frame buildings demonstrate over 95% accuracy in identifying fire states and 90% in predicting gas temperatures. A method using correlation coefficients and standard deviations effectively detects damaged thermocouples with over 96% accuracy, given a damage ratio below 30%. Validation in two real fire scenarios showed more than 92% accuracy in locating fires and over 89% in forecasting gas temperatures 20 min ahead, which costs 2.14 s and 1.83 s, respectively. The machine learning framework also exhibits robustness to changes in ventilation conditions by making temperature predictions using reliability theory. The proposed framework provides clear information about the state and development of the fire to firefighters and is useful for promoting smart firefighting.