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Sawhney, A and Maheswari, JU (2013) Design Coordination Using Cloud-based Smart Building Element Models. International Journal of Computer Information Systems and Industrial Management Applications, 5 (2013). pp. 445-453. ISSN 2150-7988
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Abstract
Construction industry involves a multitude of stakeholders who perform their project-centric work from various dispersed locations. This involvement of numerous stakeholders, which is common in the construction industry compared to the other industries, has led to fragmentation of the design process. The limitations and complexities resulting from this fragmentation can be overcome by proper design coordination. Shifting from two-dimensional design process, the industry has recently embraced Building Information Models (BIM) that allow an information rich visualization of the facility from the feasibility stage of the project enabling efficient information transfer among the stakeholders. Deploying the BIM on the ‘cloud’ platform or cloud computing (CC) can further enhance the design coordination especially clash detection. While BIM is certainly an improvement compared to the paper-based design process no direct mechanism exists to capture the iterative characteristic feature of the process. It is important to capture the design evolution that emerges as a result of change propagation while resolving any coordination issues requiring a record of the history of design changes. Very few researchers have investigated the use of building element level versioning using object parameters that can store the design information changes. A conceptual framework using the Industry Foundation Classes (IFC) that allows version and audit history to be retrieved at any instance of time via a cloud-based BIM model server is proposed. The utility of the framework is also discussed with the help of an example.
| Item Type: | Article |
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| Subjects: | Q Science > QA Mathematics > QA76 Computer software T Technology > TA Engineering (General). Civil engineering (General) T Technology > TH Building construction |
| Divisions: | Civil Engineering & Built Environment |
| Publisher: | Machine Intelligence Research Labs (MIR Labs) |
| Date Deposited: | 03 May 2017 08:51 |
| Last Modified: | 04 Sep 2021 11:39 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/6334 |
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