Safety of workers in the construction environment remains one of the greatest challenges faced by the construction industry today. Activity-based hazard identification and prevention is limited because construction safety information and knowledge tends to be scattered and fragmented throughout safety regulations, accident records, and experience. With the advancement of information technology in the building and construction industry, a missing link between effective activity-level construction planning and Building Information Modeling (BIM) becomes more evident. The objectives of this study are 1) to formalize the safety management knowledge and to integrate safety aspects into BIM, and 2) to facilitate activity-based hazard identification and prevention in construction planning. To start with, a Construction Safety Ontology is created to organize, store, and re-use construction safety knowledge. Secondly, activity-based workspace visualization and congestion identification methods are investigated to study the hazards caused by the interaction between activities. Computational algorithms are created to process and retrieve activity-based workspace parameters through location tracking data of workers collected by remote sensing technology. Lastly, by introducing workspace parameters into ontology and connecting the ontology with BIM, automated workspace analysis along with job hazard analysis are explored. Results indicate that potential safety hazards can be identified, recorded, analyzed, and prevented in BIM. This study integrates aspects of construction safety into current BIM workflow, which enables performing hazard identification and prevention early in the project planning phase.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/52235 |
| Date | 27 August 2014 |
| Creators | Zhang, Sijie |
| Contributors | Kurtis, Kimberly E., Eastman, Charles M. |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | application/pdf |
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