scholarly journals High-rise Building Group Regional Fire Risk Assessment Model Based on AHP

2016 ◽  
Vol 6 (1) ◽  
pp. 31
Author(s):  
Wei Zhu ◽  
Qiuju You
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhenguo Yan ◽  
Yanping Wang

In order to effectively reduce the risk of subway fires and to improve the safety of passengers, a review of the background to subway fires employing literature and comparative analyses, computer simulation, expert consultation, and other research methods has been employed to conduct an in-depth study of subway fire risk assessment and control measures. A subway fire risk assessment model based on analysis theory was established. Firstly, a subway fire risk evaluation index system was developed, and the weight values of each level were determined using the interval analytic hierarchy process (IAHP), then the evaluation was derived using the fuzzy evaluation method, and the passenger distribution simulation was introduced to improve the objectivity of the evaluation. The results show that the fire evaluation of this subway system is safe. The results show that a subway fire risk assessment model may provide a scientific basis for establishing prevention and control measures, extinguishing methods, passenger safety evacuation schemes, and carrying out fire safety management activities during subway operations.


2019 ◽  
Vol 8 (12) ◽  
pp. 579 ◽  
Author(s):  
Zohreh Masoumi ◽  
John van L.Genderen ◽  
Jamshid Maleki

A comprehensive fire risk assessment is very important in dense urban areas as it provides an estimation of people at risk and property. Fire policy and mitigation strategies in developing countries are constrained by inadequate information, which is mainly due to a lack of capacity and resources for data collection, analysis, and modeling. In this research, we calculated the fire risk considering two aspects, urban infrastructure and the characteristics of a high-rise building for a dense urban area in Zanjan city. Since the resources for this purpose were rather limited, a variety of information was gathered and information fusion techniques were conducted by employing spatial analyses to produce fire risk maps. For this purpose, the spatial information produced using unmanned aerial vehicles (UAVs) and then attribute data (about 150 characteristics of each high-rise building) were gathered for each building. Finally, considering high-risk urban infrastructures, like the position of oil and gas pipes and electricity lines and the fire safety analysis of high-rise buildings, the vulnerability map for the area was prepared. The fire risk of each building was assessed and its risk level was identified. Results can help decision-makers, urban planners, emergency managers, and community organizations to plan for providing facilities and minimizing fire hazards and solve some related problems to reduce the fire risk. Moreover, the results of sensitivity analysis (SA) indicate that the social training factor is the most effective causative factor in the fire risk.


2018 ◽  
Vol 95 ◽  
pp. 160-169 ◽  
Author(s):  
N.D. Hansen ◽  
F.B. Steffensen ◽  
M. Valkvist ◽  
G. Jomaas ◽  
R. Van Coile

Fire ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 57
Author(s):  
Zhen Zhang ◽  
Leilei Wang ◽  
Naiting Xue ◽  
Zhiheng Du

The increasing frequency of active fires worldwide has caused significant impacts on terrestrial, aquatic, and atmospheric systems. Polar regions have received little attention due to their sparse populations, but active fires in the Arctic cause carbon losses from peatlands, which affects the global climate system. Therefore, it is necessary to focus on the spatiotemporal variations in active fires in the Arctic and to assess the fire risk. We used MODIS C6 data from 2001 to 2019 and VIIRS V1 data from 2012 to 2019 to analyse the spatiotemporal characteristics of active fires and establish a fire risk assessment model based on logistic regression. The trends in active fire frequency based on MODIS C6 and VIIRS V1 data are consistent. Throughout the Arctic, the fire frequency appears to be fluctuating and overall increasing. Fire occurrence has obvious seasonality, being concentrated in summer (June–August) and highest in July, when lightning is most frequent. The frequency of active fires is related to multiple factors, such as vegetation type, NDVI, elevation, slope, air temperature, precipitation, wind speed, and distances from roads and settlements. A risk assessment model was constructed based on logistic regression and found to be accurate. The results are helpful in understanding the risk of fires in the Arctic under climate change and provide a scientific basis for fire prediction and control and for reducing fire-related carbon emissions.


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