Abstract
Construction risk and safety management is a knowledge-intensive task. In addition to the construction risk knowledge itself, construction process knowledge is also important to the construction safety, since the different construction objects and processes selection often imply different potential risks may occur. However, most of construction risk management systems only focused on the construction risk knowledge itself. The process management and risk management usually are handled separately. In this paper, we explore to combine the construction process knowledge reuse methodology with the ontological modeling and semantic inferring approach to facilitate considering the risk issue during construction process reuse to directly improve the construction risk and safety management level. A meta ontology model is proposed to integrate risk knowledge domain with the risk monitor object domain. The meta model can be instantiated with the domain specific terms and relations and finally results into the specific domain model for specific application. The domain risk model for the deep foundation pit excavation using support systemcontiguous bored pile retaining wall system is shown as an illustrative example, in which the construction risk path knowledge is extracted via Fault Tree Analysis (FTA). In order to represent the construction risk knowledge in a computer-interpretable and semantically inferable way, the semantic web technique is used to represent the knowledge into OWL axioms and SWRL rules. Finally, based on the ontological model, the semantic inferring and application mechanism of construction risk knowledge are discussed and illustrated in the Protg platform. The proposed approach enables the construction risk knowledge to be presented in a computer-interpretable and semantically inferable way. The risk-oriented ontology model can be used as the basic structure of knowledge-based risk management system.
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Zhong, B., Li, Y. An Ontological and Semantic Approach for the Construction Risk Inferring and Application. J Intell Robot Syst 79, 449–463 (2015). https://doi.org/10.1007/s10846-014-0107-9
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DOI: https://doi.org/10.1007/s10846-014-0107-9