School of Civil Engineering, Hefei University of Technology, Hefei 230009, China.
School of Property, Construction and Project Management, RMIT University, Melbourne City Campus, Melbourne, VIC 3000, Australia.
Int J Environ Res Public Health. 2020 Jun 1;17(11):3928. doi: 10.3390/ijerph17113928.
Early decision-making and the prevention of construction safety risks are very important for the safety, quality, and cost of construction projects. In the field of construction safety risk management, in the face of a loose, chaotic, and huge information environments, how to design an efficient construction safety risk management decision support method has long been the focus of academic research. An effective approach to safety management is to structuralize safety risk knowledge, then identify and reuse it, and establish a scientific and systematic construction safety risk management decision system. Based on ontology and improved case-based reasoning (CBR) methods, this paper proposes a decision-making approach for construction safety risk management in which the reasoning process is improved by integrating a similarity algorithm and correlation algorithm. Compared to the traditional CBR approach in which only the similarity of information is considered, this method can avoid missing important correlated information by making inferences from multiple sources of information. Finally, the method is applied to the safety risks of subway construction for verification to show that the method is effective and easy to implement.
早期决策和预防建设安全风险对于建设项目的安全、质量和成本非常重要。在建设安全风险管理领域，面对宽松、混乱和庞大的信息环境，如何设计高效的建设安全风险管理决策支持方法一直是学术研究的重点。安全管理的有效方法是结构化安全风险知识，然后识别和重用它，并建立科学系统的建设安全风险管理决策系统。基于本体和改进的案例推理（CBR）方法，本文提出了一种建设安全风险管理决策方法，通过整合相似性算法和相关算法来改进推理过程。与传统的 CBR 方法仅考虑信息的相似性相比，该方法可以通过从多个信息源进行推理来避免错过重要的相关信息。最后，该方法应用于地铁建设的安全风险进行验证，以证明该方法是有效和易于实施的。
