Ontology-Based Hazard Knowledge Representation and Identification for Deep Refurbishment Projects
Keywords:Hazard identification, Artificial intelligence, Ontology, Knowledge-based system, Protégé platform, BIM, Building renovation, Scenario-based simulation, RINNO project
The delivery of construction projects in general can be complex and demanding and presents well-documented challenges to the control of cost, safety, and quality. This situation becomes even more challenging in the case of renovation projects due to the high level of interaction with occupants, especially when they remain in the building over the renovation period. The safety of project participants as well as that of occupants when they are present in the renovation site must be ensured. Although the planning and management of such projects can be greatly enhanced by exploiting some of the advantages of Building Information Modelling (BIM), the process of construction hazard identification and renovation scenarios assessment is still human-based and so requires considerable time and effort. Moreover, there is little research that addresses how hazard identification can best be represented and processed automatically in order to optimise and develop more effective strategies for managing construction projects, particularly those involving the systematic renovation of existing properties for better energy performance. Using BIM along with Artificial Intelligence (AI) tools could help in processing the massive amount of newly-available data and knowledge (e.g., feedback, images captured from smart devices, IoT sensors) that are increasingly obtainable. A prerequisite for doing so is the development of a dedicated ontology that would enable the formalisation of domain knowledge, including associated concepts, relations, and constraints that are specific to renovation project hazard. The authors propose an ontology and demonstrate its application by developing a knowledge-based system for application within the context of deep renovation projects that are part of a large European research project: the RINNO project.
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