摘要
提出基于IOWA-RBF神经网络的地铁车站火灾安全评价方法。从消防设计、消防管理、消防设备、应急设备、消防环境5个角度构建指标体系;按照降序规则重新排序得到新数据,在IOWA算子中引入正态密度分布函数并结合θ系数得到新数据最终权重;最后将各指标数据作为RBF神经网络输入,得到地铁车站火灾安全评价值。以某地铁车站为例进行案例分析,结果认为其火灾安全等级高,与政府评估结果一致,验证了评价方法的科学性。
The fire safety evaluation method of metro station based on IOWA-RBF neural network is proposed. The index system is constructed from five aspects: fire protection design, fire management, fire equipment, emergency equipment and fire protection environment. According to the descending rules, the new data is reordered, the normal density distribution function is introduced in the IOWA operator, and the final weight of the new data is obtained by combining with the coefficients θ. The index data is input as the RBF neural network, and the fire safety evaluation values of subway stations was gotten. Taking a subway station as an example,the case study was conducted. It is considered that the fire safety level of the station is high, which is consistent with the government evaluation results, and the scientificality of the evaluation method is verified.
作者
陈佳
闫帅平
邓曦
CHEN Jia;YAN Shuai-ping;DENG Xi(Civil Engineering College,Institute of Disaster Prevention,Hebei Langfang 065201,China;Jiyuan Vocational and Technical College,Henan Jiyuan 459000,China)
出处
《消防科学与技术》
CAS
北大核心
2019年第10期1476-1479,共4页
Fire Science and Technology
基金
廊坊市科技支撑计划项目“基于PPP模式的廊坊市环保项目投融资研究”(2018029012)
中央高校基本科研业务费专项资金青年教师资助计划项目“基于BSC-熵值-FCA的建筑工程项目管理信息化绩效评价研究”(ZY20140209)
