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Abstract
As our society ages, more and more elderly or disabled people live in long-term care (LTC) facilities, which are vulnerable to fires and may result in heavy casualties. Because of the low mobility of LTC residents, firefighters often need to enter the facility to save people. In addition, due to LTC facility management needs, many doors or windows on the passages for a fire rescue operation may be blocked. Thus, firefighters have to employ forcible entry tools such as disk cutters for passing through, which may lengthen the rescue time if an incorrect route or tool is utilized. As new information technologies such as ontology and building information modeling (BIM) have matured, this research aims at proposing a BIM-based ontology model to help firefighters determine better rescue routes instead of using rules of thumb. Factors such as the path length, building components and materials encountered, and forcible entry tools carried are considered in the model. Real LTC fire investigation reports are used for the comparisons between the original routes and the ones generated by the proposed model, and seven experts joined the evaluation workshop to provide further insights. The experts agreed that using the proposed approach can lead to better fire rescue route planning. The proposed BIM-based ontology model could be extended to accommodate additional needs for hospital fire scenes, in the hopes of enhancing the efficiency and effectiveness of firefighters’ rescue operations in such important facilities.
Original language | English |
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Article number | 1060 |
Journal | Buildings |
Volume | 12 |
Issue number | 7 |
DOIs | |
State | Published - Jul 2022 |
Keywords
- building information modeling (BIM)
- firefighter
- long-term care (LTC) facility
- ontology
- search and rescue
- semantic web rule language (SWRL)
- shortest path
- wayfinding
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- 1 Finished
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Digital Avatar Value-Added Application for Built Environments: Applying Deep Learning / Bim / Smart Meters / Iot for Energy Management(3/3)
Chou, C.-C. (PI)
1/08/21 → 31/07/22
Project: Research