A metro collapse accident is the main type of metro construction accidents. How to scientifically analyze the key cause factors and their interaction coupling mechanism of the existing metro collapse accidents is crucial to reduce the occurrence of metro collapse. Based on the Fault Tree Analysis (FTA) and the Behavior security “2-4” Model (24Model), the FTA-24Model accident cause analysis framework was constructed by combing their respective characteristics. To be more specific, a logical analysis program was developed to analyze the accident causes by the four-module analysis method. An empirical study was carried out by taking the “12.1” major cave-in accident at the construction site of the Metro Line 11 in Guangzhou as an example. Compared with the case accident report, the FTA-24Model framework analysis method can not only systematically deduce the logical relationship between the accident causes and provides a panorama of the accident cause chain and its evolution process, but also identify the key causes of accidents and their coupling risk effects. For a metro construction accident, this method can not only effectively investigate the accident causes, but also provide a reference for the formulation of prevention strategies.
The data of Semantic Web exist in machine readable format called RDF, in order to promote data exchange on the web based on their semantics. As an expressive knowledge representation language for the Semantic Web, Web Ontology Language (OWL) plays an important role in modeling information in a semantic way. However, due to the nature of knowledge bases, ontologies tend to be very large, distributed, and interconnected. Thus, maintaining constraints and enforcing data consistency for a group of ontologies become very challenging. In addition, frequent updates on ontologies necessitate an automatic approach to checking for potential constraint violations before any change takes place. In this study, we conducted a pioneer study and presented a framework for checking global constraints and ensuring integrity on data that span multiple ontologies. As an update is issued to a single site, global constraints that can be potentially violated are broken down into sub constraints that only involve a very small subset of ontologies. The checking of sub constraints runs effectively in parallel and returns results about each subset. The collection of these results determines the violation of global constraints.
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