Extraction and Association Analysis of Risk Factors in Gas Pipeline Network Emergencies Through Fusing Expert Knowledge and Pre-Trained Model - Xinghao Zhao; Yanzhu Hu; Xiaoyu Liu; Yingjian Wang (IEEE Access, 2024)
The gas pipeline network constitutes a critical component of urban infrastructure. Emergencies, such as leaks or explosions, can lead to severe casualties, extensive property damage, and long-lasting effects on societal and environmental safety. The genesis of such emergencies typically involves a complex interplay among various risk factors. Therefore, the meticulous identification of these risk factors and the effective modeling of their interrelations are crucial for assessing the risks associated with such emergencies and enhancing urban safety. In this research, we initially developed a specialized dataset tailored specifically to analyze emergencies within gas pipeline networks. Recognizing the significance of unstructured domain-specific knowledge, we introduced an innovative approach Fusing expert knowledge and pre-trained model. This approach facilitates the fusion of domain experts’ knowledge into the process of risk factor identification and extraction in different steps, ensuring a thorough and precise determination and filtration of risk factors. Subsequently, a network depicting the associations between risk factors was established, drawing on their co-occurrence relationships, followed by a quantitative analysis of this network. Supported by real-world gas network management and emergency cause investigations, the methods proposed in this study provide strong support for gas network safety management and emergency decision-making.