Fire Risk Factor Analysis of High-Rise Building Based on Bayesian Network and Fuzzy Fault Tree

Dong, Penghui; Wang, Sanming

doi:10.1109/iccar.2019.8813698

Cited by 6 publications

(14 citation statements)

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“…The experiment result confirms that the method proposed in this paper has good theoretical significance and practical value, and can provide a reference for fire rescue work. We further compare our proposed methods with the existing methods including BN + fuzzy fault tree approach [9], BN + 4D Radar chart approach [13], and BN method [20]. The comparisons illustrate that our proposed method makes the entire fire risk classification process more comprehensive, rational, and scientific.…”

Section: Introductionmentioning

confidence: 94%

“…Attribute decomposition is based on daily fire-fighting practice and related work [6][7][8][9]18,25,27], as well as the Chinese fire alarm and emergency rescue classification standard [3]. The process of attribute decomposition is demonstrated in figure 3.…”

Section: B Attribute Decompositionmentioning

confidence: 99%

“…(1) Setting prior probability Firstly, it is necessary to estimate the prior probability of 63 leaf nodes(input nodes). The prior probability in this paper came from fire data analysis, expert knowledge, and on-site sampling statistics of urban buildings over the past years [9,11,13,20]. As shown in figure 5, the node ClosedDoorAnd-Window shows that normally fireprevention doors and windows should be closed during fire inspection.…”

Section: ) Determine Network Parametersmentioning

confidence: 99%

“…Liu et al [8] use the consequence probability estimation method to divide fire risk into three dimensions: the number of serious injuries, the number of deaths, and the direct property loss, and evaluate the fire risk value and grade of building cluster by estimating the probability of risk dimensions. Dong et al [9] adopt the method of combining Bayesian Network and Fuzzy Fault Tree to analyse the fire risk factors of the high-rise building. Zhang et al [10] propose a Bayesian network analysis model of the fire in the dormitory of colleges and universities, establish a relevant index system, build a Bayesian model for fire risk analysis.…”

Section: Introductionmentioning

confidence: 99%

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A Trustworthy Classification Model for Intelligent Building Fire Risk

Wang

Chen

2022

IEEE Access

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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.

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Section: Introductionmentioning

confidence: 94%

Section: B Attribute Decompositionmentioning

confidence: 99%

Section: ) Determine Network Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Trustworthy Classification Model for Intelligent Building Fire Risk

Wang

Chen

2022

IEEE Access

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“…In less severe cases, fire may burn buildings and furniture. If things get worse, people could be injured or killed by fire [3][4][5]. The current fire safety standards for buildings cannot satisfy the demand of modern buildings for fire safety.…”

Section: Introductionmentioning

confidence: 99%

Fire Safety Assessment of High-Rise Buildings Based on Fuzzy Theory and Radial Basis Function Neural Network

Xu¹,

Peng²

2020

ISI

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Featuring complex functions, dense population, and large span, high-rise buildings are an iconic product of economic and technical growths in modern society. Fire poses an imminent threat to high-rise buildings. Once a high-rise building catches fire, the loss of life and property will be incalculable. However, the traditional assessment methods for fire safety of high-rise buildings are incapable of handling the complex influencing factors. To solve the problem, this paper combines the fuzzy logic inference system and radial basis function neural network (RBFNN) into an intelligent assessment method mimicking the nonlinear inference process of fire safety experts. Firstly, the factors affecting the fire safety of high-rise buildings were quantified, and the relevant rating standard was established. Next, the classic statistics were transformed into fuzzy indices, using the fuzzy logic system. To avoid the local minimum trap, the RBFNN was adopted to replace the traditional backpropagation neural network (BPNN), and integrated with the fuzzy logic system, creating an adaptive fuzzy-RBFNN to assess the fire safety of high-rise buildings. The proposed network was trained by numerous expert evaluation samples, and verified by examples. The simulation results show that the proposed network could mimic the nonlinear inference process of fire safety experts, and evaluate the fire safety of high-rise buildings in real time with little error. The research results provide new insights on the application of artificial intelligence (AI) in fire safety assessment.

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

Su¹,

Yang²,

Shen³

et al. 2022

Fire and Materials

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At 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,

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Fire Risk Factor Analysis of High-Rise Building Based on Bayesian Network and Fuzzy Fault Tree

Cited by 6 publications

References 0 publications

A Trustworthy Classification Model for Intelligent Building Fire Risk

A Trustworthy Classification Model for Intelligent Building Fire Risk

Fire Safety Assessment of High-Rise Buildings Based on Fuzzy Theory and Radial Basis Function Neural Network

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

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