Fire Risk Assessment Method Research Articles

A fire risk assessment method for offshore platforms: applications of 24Model with BN

ABSTRACT This paper integrates the 24Model with a BN and proposes a fire risk assessment method for offshore platforms. Triangular fuzzy membership function fuzzy set theory, coefficient of variation method and the combination of node state distance function and fuzzy inference are used to address the subjectivity and uncertainty due to insufficient objective data and conflicting opinions of multiple experts. Based on the proposed method, the human, organisational and technical factors of an offshore platform are comprehensively analysed from the perspective of the system. Based on diagnostic inference and sensitivity analysis, the main affecting factors and causal path of the risk scenario are determined. The results show that the proposed method could help in identifying the root causes of the failures and provide new insights into the probabilities and relationships among different factors.

Adaptive dynamic fire danger evaluation of logistics warehouses with fusion of evidential reasoning and smart optimization

Considering the background that the current fire risk assessment methods often rely on subjective assessments based on professional knowledge and experience, they are usually limited to the special fire scene. Given the lack of effective tools for assessing fire risks in logistics warehouses across varying scenes, this paper aims to propose a multi-source data-driven, adaptive dynamic fire danger evaluation method. The method is established based on the fusion strategy of evidential reasoning and artificial fish swarm algorithm. According to the method, rules of danger evaluation levels related to each attribute of fire factors can be generated automatically without necessary of professional knowledges and experiences. The lightweight multi-source data at this moment can be fused to evaluate current fire danger level directly, which does not require training on extensive datasets in advance. Finally, multi-source data including temperature and CO concentration in two fire scenes of one logistics warehouse at different times are utilized to support the ability of the method. The results show that the estimated fire source location according to the center of domain with the evaluated highest danger level matches well with the real fire source location. In conclusion, the main contribution of the research is that the reasonable dynamic danger level of the logistics warehouse fire with time variation can be evaluated automatically. The method has an excellent generality, which is not limited to the specific fire scene. The danger zone of the logistics warehouse under sudden fires can be determined adaptively, which can support the fire prevention and fire safety countermeasures of logistics warehouses.

Next-gen regional fire risk mapping: Integrating hyperspectral imagery and National Forest Inventory data to identify hot-spot wildland-urban interfaces

The increasing threat of high-severity wildfires in Mediterranean Wildland-Urban Interface (WUI) areas demands to develop effective fire risk assessment and management strategies. Simultaneously, the newfound accessibility of spaceborne hyperspectral data represents a significant potential for generating fire severity assessments, whereas National Forest Inventories (NFI) offer a vast dataset related to vegetation and fuel loads, which is essential for shaping the planning and strategies of forest services. This research work aims to advance the state-of-the-art in WUI fire risk mapping in the western Mediterranean Basin by combining PRISMA spaceborne hyperspectral data and Spanish NFI data. The proposed methodology had three main stages: (i) fire severity assessment at local scale (a wildfire) by using PRISMA hyperspectral data and Multi-Endmember Spectral Mixture Analysis (MESMA) leveraging field-based measurements of the Composite Burn Index (70 plots); (ii) development of a high fire severity probability map at regional scale from the extrapolation of a Random Forest predictive model calibrated from fire severity estimates, NFI data and topo-climatic variables at local scale (overall accuracy = 92 %; Kappa = 0.8); and (iii) identification and characterization of zones that concentrate WUIs with high probability of high fire severity if a fire event occurs (hot-spot WUIs) by crossing the information from the previous regional high fire severity probability map and a WUI cartography developed at regional scale. Study area was Castilla y León Autonomous Region (larger Spanish region, 94,226 km2), where the second-largest extreme Spanish wildfire event (28,000 ha) occurred. We identified hot-spot WUIs so that stakeholders and decision-makers could (i) prioritize resources and interventions for effective fire management and mitigation, (ii) allocate resources for prevention, and (iii) plan evacuation measures to safeguard lives and property. This study contributes to the development of next-generation fire risk assessment methods that combine remote sensing technologies with comprehensive ground-level datasets.

Fire Risk Assessment Method of Energy Storage Power Station Based on Cloud Model

In response to the randomness and uncertainty of the fire hazards in energy storage power stations, this study introduces the cloud model theory. Six factors, including battery type, service life, external stimuli, power station scale, monitoring methods, and firefighting equipment, are selected as the risk assessment set. The risks are divided into five levels. Membership function is constructed using cloud model. The forward generator is responsible for calculating the complete coefficient matrix and the comprehensive evaluation matrix, while the reverse generator handles the same calculations separately. By utilizing fuzzy synthesis operators and cloud computing, the numerical attributes of the evaluation cloud model are derived, resulting in the creation of a visual representation that illustrates the fire hazard level for energy storage power stations. The results show that the cloud model can be used for fire risk assessment in energy storage power stations. Fuzzy variables can be accurately and clearly represented and corresponded to different safety levels. The effectiveness and feasibility of this assessment method have been verified through case analysis.

Research on an Equivalent Heat Source Model of the AC Arc in the Short Gap of a Copper-Core Cable and a Fire Risk Assessment Method.

The magnetohydrodynamics (MHD) model of the alternating current (AC) arc is complex, so a simplified equivalent heat source (EHS) model can be used to replace the complex model in studying the AC arc's thermal characteristics and cable fire risk. A 2D axisymmetric AC arc MHD simulation model in the short gap of a copper-core cable is established in this paper. The AC arc voltage and current obtained by the model are consistent with experiments. The AC arc's heat source distribution obtained by the MHD model is fitted to obtain the heat source function Q of the AC arc. Q is divided into 16 independent segmented heat sources, and a correction matrix is constructed to optimize the segmented heat sources. A neural network and a genetic algorithm give the prediction model and the optimal correction matrix of the segmented heat source. The EHS model optimized by the optimal correction matrix can obtain a minimum temperature error of 5.8/4.4/4.2% with the MHD model in different AC arc peak currents 2/4/6 A. The probability of a cable fire is calculated by using AC arc's optimized EHS model when different numbers of AC arcs are generated randomly in AC half-waves. The EHS model can replace the complex MHD model to study the thermal characteristics of AC arcs and quickly calculate the probability of a cable fire caused by random AC arcs.

Fire risk assessment of Kashan's commercial and shopping centers using fire risk assessment method for engineering (FRAME)

AbstractCommercial buildings are considered as high‐risk units in fire due to the existence of expensive equipment and large presence of people with different ages. Therefore, the present study aimed to assess the fire risk of commercial and shopping centers in Kashan using the fire risk assessment method for engineering (FRAME). This cross‐sectional study was conducted using FRAME in 15 commercial and shopping centers in Kashan, Iran. The risk assessment data and FRAME checklist were collected by observation and interviews. Data analysis was conducted using Excel and Geographic Information System (GIS) software. The results of 46 checklists showed that the mean score of fire risk R for property was equal to 3.12, for people 3.81 and for activities 1.86. Also, the lowest risk for the property, people and activities belongs to Golestan Center with a score of 1.40, Valiasr with a score of 1.25 and Ataee with a score of 1.15, and the highest risk for the property, people and activities belongs to Mir Seyfi Center with a score of 5.80, Shafagh with a score of 9.96 and Sabz was 2.62, respectively. The present study showed the lack of proper ventilation system, lack of emergency exits, and lack of automatic fire alarm and extinguishing system were the factors affecting fire risk. Therefore, in order to prevent disasters and uncontrollable critical conditions, it is necessary to review the equipment of these buildings and to design and implement fire detection and alarm equipment and different ways of fire control in these centers.

Fire risk forecasting for preschool and educational institutions

Using the statistical data on fires in Russia, the study concludes that it is necessary to assess fire risks for preschool and educational institutions. The factors influencing the occurrence of fires are considered. Tools and resources for predicting fire risks are described. The methodology of assessing fire risks for preschool and educational institutions is analyzed. A fire risk for the facility under study is caluclated. The calculation is based on the method of constructing “bounce” trees, successfully used in the graph theory. The key event is a fire, the risk of which is etermined. The logic elements “and”, “or” and “inverter” are used in the construction. The block diagram has several levels - from the key to the simplest failures. The advantage of this scheme is described. In addition to the main fire risk for the facility, the professional risk is caluclated. Taking into account the result, measures aimed to ensure fire safety at these facilities are suggested. The article concludes that it is necessary to use the fire risk assessment method.

A High-Resolution Spatial Distribution-Based Integration Machine Learning Algorithm for Urban Fire Risk Assessment: A Case Study in Chengdu, China

The development and functional perfection of urban areas have led to increasingly severe fire risks in recent decades. Previous urban fire risk assessment methods relied on subjective judgment, rough data collection, simple linear statistical methods, etc. These drawbacks can lead to low robustness of evaluation and inadequate generalization ability. To resolve these problems, this paper selects the indicator and regression models based on the high-resolution data of the spatial distribution characteristics of Longquanyi distinct in Chengdu, China. and proposes an integrated machine learning algorithm for fire risk assessment. Firstly, the kernel density analysis is used to map the fourteen urban characteristics related to fire risks. The contributions of these indicators (characteristics) to fire risk and its corresponding index are determined by Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and eXtreme Gradient Boosting (XGBoost). Then, the spatial correlation of fire risks is determined through Moran’s I, and the spatial distribution pattern of indicator weights is clarified through the raster coefficient space analysis. Finally, with these selected indicators, we test the regression performance with a backpropagation neural network (BPNN) algorithm and a geographically weighted regression (GWR) model. The results indicate that numerical variables are more suitable than dummy variables for estimating micro-scale fire risks. The main factors with a high contribution are all numerical variables, including roads, gas pipelines, GDP, hazardous chemical enterprises, petrol and charging stations, cultural heritage protection units, assembly occupancies, and high-rise buildings. The machine learning algorithm integrating RF and BPNN shows the best performance (R2 = 0.97), followed by the RF-GWR integrated algorithm (R2 = 0.87). Compared with previous methods, this algorithm reduces the subjectivity of the traditional assessment models and shows the ability to automatically obtain the key indicators of urban fire risks. Hence, this new approach provides us with a more robust tool for assessing the future fire safety level in urban areas.

Scaled experimental study on the initial oil thickness effect on the combustion characteristic of the typical transformer insulating oil

Compared with mineral oil, plant insulation oil has a higher flash point, excellent natural degradation ability, and higher electrical insulation. It is expected to become the main component of transformer insulation oil, but the fire safety of this oil needs to be further analyzed and improved. In this paper, a cone calorimeter was used to test the combustion of typical plant insulating oil (camellia seed oil, soybean oil) and mineral oil (25# mineral oil) under different radiant heat flows and initial oil thickness. The combustion parameters such as ignition time, heat release rate, and carbon generation rate were measured. The combustion characteristics of 25# mineral oil were greatly affected by radiative heat flow and oil thickness. The peak heat release rate and carbon generation rate of mineral oil under high heat flow (50 kW/m2) were significantly higher than those under low heat flow (25 kW/m2) and increased with the increase of initial oil thickness. Plant insulating oil (camellia seed oil and soybean oil) had similar laws, but the peak value of the heat release rate and carbon generation rate of soybean oil were relatively small. Petrella fire risk assessment method showed that the fire risk ranking in descending order was mineral oil, camellia seed oil, and soybean oil, which provided a reference for the further popularization and application of plant insulation oil transformer.

HGV fire risk assessment method in highway tunnel based on a Bayesian network

The complex characteristics of long-distance highway tunnels may lead to serious loss of life and property in fire incidents. According to the statistical analysis, heavy goods vehicle (HGV) is one of the primary risk factors for tunnel fires. In this study, a quantitative risk assessment model of HGV-involved tunnel fires is established based on the functional resonance analysis method (FRAM) and Bayesian network (BN). Using FRAM, the mechanisms of the incident occurrence and evolution and critical risk factors of HGV-involved tunnel fires are determined, and the BN model is used to quantify the risk based on a probabilistic analysis. Focusing on operational management, monitoring system construction, type of transporting goods, emergency rescue, and egress facilities, the incident severity is determined in terms of casualties and economic losses. The proposed risk assessment method is expected to assist the tunnel operational management and the fire services in identifying critical risk factors in HGV-involved tunnel fires.

Fire risk assessment of airborne lithium battery based on entropy weight improved cloud model

Purpose The purpose of this paper is to combine the entropy weight method with the cloud model and establish a fire risk assessment method for airborne lithium battery. Design/methodology/approach In this paper, the fire risk assessment index system is established by fully considering the influence of the operation process of airborne lithium battery. Then, the cloud model based on entropy weight improvement is used to analyze the indexes in the system, and the cloud image is output to discuss the risk status of airborne lithium batteries. Finally, the weight, expectation, entropy and hyperentropy are analyzed to provide risk prevention measures. Findings In the risk system, bad contact of charging port, mechanical extrusion and mechanical shock have the greatest impact on the fire risk of airborne lithium battery. The fire risk of natural factors is at a low level, but its instability is 25% higher than that of human risk cases and 150% higher than that of battery risk cases. Practical implications The method of this paper can evaluate any type of airborne lithium battery and provide theoretical support for airborne lithium battery safety management. Originality/value After the fire risk assessment is completed, the risk cases are ranked by entropy weight. By summarizing the rule, the proposed measures for each prevention level are given.

Electrical fire risk assessment of high‐rise buildings based on hybrid decision model considering asymmetric proximity

AbstractAt present, there are some problems in the research of electrical fire risk assessment of high‐rise buildings, such as the evaluation index is relatively single and the fuzziness of evaluation state boundary is not considered. This study proposed an electrical fire risk assessment method of high‐rise buildings based on hybrid decision model considering asymmetric proximity. Based on the occurrence mechanism of electrical fire in high‐rise buildings, a four‐level evaluation index system considering disaster causing body, fire site environment, affected body and fire driving factors is established based on FP growth (Frequent Pattern Tree Growth) mining association rules, and the risk grade is divided further. An improved index weight assignment method for balancing the interaction relationship between indexes is proposed, and a hybrid decision‐making model for electrical fire risk assessment of high‐rise buildings is established. Combined with specific examples, the effectiveness of the proposed building electrical fire risk assessment method is verified. The weight standard deviation of the proposed method is 0.0464, which is about 13.8% smaller than that of analytic hierarchy process (0.0528). So the method can better balance each risk evaluation index, fully consider the fuzziness and randomness in the electrical fire risk assessment of high‐rise buildings, and improve the accuracy and applicability of electrical fire risk assessment.

A fire risk assessment method for high-capacity battery packs using interquartile range filter

Lithium-ion batteries are chosen as the most suitable device for energy storage system (ESS) due to their high energy density. However, lithium-ion batteries have high chemical reactivity, which increase the fire risk of products using them. Accordingly, various studies have been conducted to prevent lithium-ion battery-fire accidents but the main purpose of the conventional studies was to prevent the spread of fire after it started rather than predicting the risk of fire. Thus, there is a limit to reducing the maintenance time and cost of ESS. In this paper, a new method for real-time monitoring of the fire risk during operation of the battery pack is proposed. It combines with the electrochemical theory while using the real-time data from the electrically measured factors to select the aged cells. Also, a method of assessing the fire risk of battery packs by applying the interquartile range (IQR) filter to real-time data obtained from the electrically measured factors of battery packs is proposed. The feasibility of the proposed method is verified by the experiment using the specifications of the high-capacity battery packs used for railway vehicles.

Fire risk assessment of battery transportation and storage by combining fault tree analysis and fuzzy logic

Battery transportation and storage is an important part in the whole lifespan of batteries. It's significant to make a way to assess the fire risk of batteries during transportation and storage preventing from fire accident. This study presents a novel fire risk assessment method for lithium-ion batteries during transportation and storage. 8 possible failure paths and 9 basic events are deduced by fault tree analysis method. Likehood, severity, and hazard control number are selected as indexes for assessing the hazard risk number (HRN) of each possible failure path and fire accident. To comprehensively assess the value of these indexes, corrosponding sub-indexes are proposed and analyzed by synthesis through fuzzy logic method. To ensure the safety of batteries, four parameters including the synthesis HRN, HRNs of all possible paths, likelihood and severity of all basic events are suggested to set the limitation. The case study of battery transportation by marine verified the proposed method can accurately assess fire risk and locate the possible problems.

A Trustworthy Classification Model for Intelligent Building Fire Risk

The occurrence of intelligent building fires causes huge economic losses to the country and society, and even people’s safety. It is necessary to accurately assess the degree of intelligent building fire risk so that the fire emergency management department can make scientific decisions. In this paper, a trustworthy classification model for intelligent building fire risk is proposed, which provides a scientific and reasonable model supporting the classification assessment of intelligent building fire risk. The model integrates Bayesian Network (BN) and software trustworthy computing approach. BN is used to calculate the risk value of attributes that describe the fire risk situation of the intelligent building from 7 profiles. Based on the fire risk attribute values, trustworthy computing is adopted to classify the fire risk into 5 ranks which indicates the severity degree of building fire risk: the higher the rank is, the greater the harm is. Taking the Shanghai Jing’an 11.15 fire as an example, the result confirms that the method proposed in this paper has good theoretical significance and practical value. In addition, we compare our method with 3 fire risk assessment methods in the reference. The comparisons illustrate that the trustworthy classification model proposed in this paper is more comprehensive, rational, and scientific.

Поддержка принятия управленческих решений на основе экспресс-технологий моделирования пожара по полевой модели

Introduction. In order to improve the well-being of citizens and economic growth of the state in the Russian Federation reform of control and Supervisory activity, which is based on the introduction of a risk-based approach when planning inspections, developing and applying safety requirements, including fire safety. To implement a risk-based approach, it is necessary to take into account the degree of risk of harm to legally protected values, which requires an assessment of fire risks based on deterministic methods (techniques). However, the complexity of these methods does not allow them to be applied in an operational mode without special software and highly qualified specialists, which makes it difficult to implement a risk-based approach and may lead to making unjustified management decisions. Goals and objectives. To support management decision-making when implementing a risk-based approach, it is necessary to develop an information equivalent of basic (full, detailed) versions of fire risk assessment methods that meet the conditions of brevity, simplicity and accessibility to a wide range of specialists, while ensuring high accuracy of calculation results. Method of research. To obtain the results, we used general scientific and special methods of scientific knowledge – system, regression, retrospective and comparative analysis, synthesis, generalization, graph theory, and numerical experiment. Research result. Rapid fire risk assessment formulas have been developed that allow determining the required evacuation time when the critical values of three fire hazards are reached: loss of visibility, elevated temperature and HCl, when two types of solid fire loadings are burning in rooms with an area of 1000 to 10000 m2 and a height of 10 to 20 m. Express formulas are developed on the basis of a field mathematical model of fire, the relative error of calculations based on Express formulas is no more than 8 %. Conclusion. The use of Express formulas as a support for management decision-making will allow officials of GPN bodies in the shortest possible time (up to 5 minutes) and with high reliability (accuracy) make informed decisions about the presence (absence) of violations of mandatory fire safety requirements that pose a threat to the life and health of people, and the need to apply administrative measures, which will exclude cases of insufficient measures taken by the audited persons to ensure the safety of citizens, as well as unjustified restrictions on the right to risk their own property. Keywords: rapid assessment, fire risk, risk-based model, fire hazards, state fire supervision.

Fire Pathology in a Military Hospital Using the FRAME Technique

Background and Aim: Hospitals are One of the vital and sensitive places as well as practical that maintaining their function in normal conditions and in times of crisis is one of the biggest concerns of health system managers. By using the principles of fire safety, hospitals' vulnerability can be reduced. Fire is one of the risks that can lead to many human and financial losses as well as disruption of hospital performance. Fire risk assessment is a more effective way to assess the vulnerability, capacity and capability of the hospital. The aim of this study was to evaluate the risk of fire in one of the hospitals in Tehran to analyze the vulnerability of these uses. Methods: This cross-sectional study was performed by using the Fire Risk Assessment Method for Engineers (FRAME) in the hospital building of a military hospital. Fire risk was calculated using the formulas of the mentioned method in EXCEL software. Factors influencing the level of risk obtained were identified and evaluated. Results: The level of fire risk for residents in all units was higher than one. Level factor, fire load factor and normal protection factor were identified as effective factors that result in the existing fire risk level. Conclusion: The numerical value of the risk for the residents was at an unfavorable level. The results of this study showed that the use of FRAME method in assessing the risk of health facilities such as hospitals, by determining the strengths and weaknesses of the assessed locations, the possibility of identifying effective factors to predict and prevent accidents and maintain the performance of these centers at times. Be sensitive to such man-made or natural emergencies.

Review of vulnerability indicators for fire risk assessment in cultural heritage

Indicator-based methods are able to provide a simple and reliable way to assess the level of fire risk of existing constructions. While many of these methods were not developed for cultural heritage assets, those that were often involve indicators connected to provisions defined by standards that are only valid in certain countries. As such, a method with the required versatility to analyse the fire safety of cultural heritage assets is still needed. Developing a method with the necessary characteristics requires a prior review of existing approaches to understand their strengths and weaknesses. A review of existing indicator-based methods for fire risk assessment is, therefore, presented, covering methods applicable to built cultural heritage assets and other types of constructions. In addition, the review also analyses existing fire safety guidelines and recommendations dedicated to cultural heritage assets. As an outcome, the review proposes twenty-two vulnerability indicators grouped into four categories that can be used to develop a fire risk assessment method dedicated to built cultural heritage assets. The review also establishes a classification of cultural heritage assets according to four classes that focus on their fire vulnerability and discusses the applicability of the proposed indicators.

A fire risk assessment method based on the combination of quantified safety checklist and structure entropy weight for shopping malls

In recent years, various large- and medium-sized shopping malls have been essential components of each city with the speed-up of China’s urbanization process and the improvement of residents’ living standard. A method for evaluating fire risk in shopping malls based on quantified safety checklist and structure entropy weight method was proposed according to related literatures as well as laws and regulations by analyzing the characteristics of fires occurring in shopping malls in recent years. At first, the factors influencing the fire risk in shopping malls were determined by carrying out on-site survey and visiting related organizations to construct an evaluation index system for fires occurring in shopping malls; afterwards, a quantified safety checklist composed of four parts (i.e. safety grade, grade description, scoring criterion and index quantification) was established based on related laws and regulations; subsequently, index weights were determined by utilizing structure entropy weight method, thus putting forward a method for assessing fire risk in shopping malls based on quantified safety checklist and structure entropy weight method. Eventually, the applicability of the evaluation method was validated exampled by Wal-Mart. The research result provides a theoretical basis for further improvement of the theoretical system for fire risk evaluation in shopping malls, and also exerts practical and guidance significance on timeous and effective early warning as well as prevention and control of building fires.

Determining Factors Affecting Fire Risk in a Hospital in Qazvin, Iran

Background: Hospitals are highly vulnerable to fire because of the presence of vulnerable people (patients, medical staff, and visitors), expensive equipment, and the ignorance and low-risk perception of occupants. Injuries caused by fire can result in life and financial losses and can disrupt the performance of a hospital. Fire risk assessment is an effective way to assess vulnerability, capacity, and capability. This study aims to evaluate the risk of fire and identify the effective factors and their contribution to a hospital. Materials and Methods: This cross-sectional study was conducted using the Fire Risk Assessment Method for Engineering (FRAME) in the equipment room of a hospital in Qazvin, Iran. The fire risk was first calculated by using the related formulas in Excel software. Then, the influential factors and their contribution to the overall risk were determined to perform corrective measures for reducing the risk. Results: The numerical value of risk for the building and its contents, occupants, and activities were 2.075, 3.315, and 2.481, respectively (>1), indicating its unacceptable level. Factors affecting the potential risk level for the building and its content and occupants were fire load, venting, and access. Regarding the acceptable risk level, the activation factor was identified as an influential factor in all domains. The highest contribution in the potential risk level for the building and its content and occupants was related to the fire load factor (1.6). In the acceptable risk level, the highest contribution was related to the activation factor (0.4). Conclusion: The FRAME method can also identify effective factors and their contribution to the overall fire risk of medical centers such as hospitals to help develop plans and special measures to reduce the risk.