![](/images/graphics-bg.png)
A Method to Extract Causality for Safety Events in Chemical Accidents from Fault Trees and Accident Reports
Joint Authors
Yu, Yangyang
Du, Junwei
Zhao, Hanrui
Hu, Qiang
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-06-19
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Chemical event evolutionary graph (CEEG) is an effective tool to perform safety analysis, early warning, and emergency disposal for chemical accidents.
However, it is a complicated work to find causality among events in a CEEG.
This paper presents a method to accurately extract event causality by using a neural network and structural analysis.
First, we identify the events and their component elements from fault trees by natural language processing technology.
Then, causality in accident events is divided into explicit causality and implicit causality.
Explicit causality is obtained by analyzing the hierarchical structure relations of event nodes and the semantics of component logic gates in fault trees.
By integrating internal structural features of events and semantic features of event sentences, we extract implicit causality by utilizing a bidirectional gated recurrent unit (BiGRU) neural network.
An algorithm, named CEFTAR, is presented to extract causality for safety events in chemical accidents from fault trees and accident reports.
Compared with the existing methods, experimental results show that our method has a higher accuracy and recall rate in extracting causality.
American Psychological Association (APA)
Du, Junwei& Zhao, Hanrui& Yu, Yangyang& Hu, Qiang. 2020. A Method to Extract Causality for Safety Events in Chemical Accidents from Fault Trees and Accident Reports. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1138802
Modern Language Association (MLA)
Du, Junwei…[et al.]. A Method to Extract Causality for Safety Events in Chemical Accidents from Fault Trees and Accident Reports. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1138802
American Medical Association (AMA)
Du, Junwei& Zhao, Hanrui& Yu, Yangyang& Hu, Qiang. A Method to Extract Causality for Safety Events in Chemical Accidents from Fault Trees and Accident Reports. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1138802
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1138802