贝叶斯网络
风险分析(工程)
概率逻辑
管道(软件)
管道运输
计算机科学
因果关系
风险评估
过程(计算)
天然气
工程类
数据挖掘
可靠性工程
人工智能
计算机安全
法学
操作系统
废物管理
程序设计语言
环境工程
医学
政治学
作者
Yiping Bai,Jiansong Wu,Qingru Ren,Jianquan Yao,Jitao Cai
标识
DOI:10.1016/j.psep.2023.01.060
摘要
As one of the most essential process facilities, the natural gas pipeline may be affected by various hazards and result in frequent accidents. Although Bayesian Network (BN) and other methods have been applied for risk assessment of gas pipelines, most attempts rely on experts instead of data. In this paper, a novel risk assessment model integrating Knowledge Graph (KG), Decision-Making Trial and Evaluation Laboratory (DEMATEL), and BN is proposed to analyze gas pipeline accidents in a data-driven way to minimize the reliance on experts for the current BN-based approach. First, the KG is used to extract and illustrate the causal network from accident reports on the Internet instead of a limited number of experts. Then, DEMATEL is applied to quantify the complex correlations in the causal network to simplify the topology structure and convert it into a BN structure. Moreover, by conducting BN analysis, a probabilistic causation model of gas pipeline accidents is established to identify critical hazards, predict potential consequences and optimize risk reduction strategies. The proposed model can more objectively support the safety management and risk reduction of natural gas pipelines and other process installations in the digital age.
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