| Summary: | 博士 === 國立臺灣大學 === 土木工程學研究所 === 100 === Construction industry has higher potential on occupational hazard than other industries do. To prevent from the fatalities and injuries occurred in construction project, Job Hazard Analysis (JHA) is a possible approach. It identifies all the activities in a construction project, recognizes the potential hazards behind each activity, then recommends possible safety approaches to eliminate the potential hazards. In order to assist JHA, this research proposes a semi-automated approach to develop a construction safety domain ontology which is based on Information Retrieval (IR) and automatic document classification techniques. Different from similar research, this research adopts more general text resources to develop the ontology. In the first step, this research collects three different types of documents which can provide references to JHA. The three types of construction safety documents are: (1) JHA documents which contains activities, hazards and safety approaches (2) fatality case reports (3) construction safety standards. In the second step, this research performs Machine Learning techniques over the JHA documents to find the best strategies for optimizing the effectiveness of automatic document classification. In the third step, the strategies are combined with Information Retrieval (IR) techniques and then applied to the automatic classification of fatality case reports. By these procedures, this research shows how to develop the construction safety domain ontology step by step. The conclusion is that although the effectiveness of integrating the different types of construction safety documents still has room for improvement, this research discusses the possible reasons behind the insufficient effectiveness and also provides several suggestions to improve the effectiveness. Moreover, the document classifying strategies this research suggests still achieve good effectiveness within JHA documents, meaning that it still has contributions to Job Hazard Analysis.
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