Please use this identifier to cite or link to this item: https://doi.org/10.14627/537724022
Title: Deriving Green Plot Ratio (GnPR) from a Building Information Modelling (BIM) Vegetation Library
Authors: Lin, ES 
Gobeawan, L
Xuan, L
Lim, CCW
He, Y 
Tan, CL 
Tan, PY 
Wong, NH 
Koon, AYT
Issue Date: 1-Jan-2022
Publisher: VDE Verlag GmbH
Citation: Lin, ES, Gobeawan, L, Xuan, L, Lim, CCW, He, Y, Tan, CL, Tan, PY, Wong, NH, Koon, AYT (2022-01-01). Deriving Green Plot Ratio (GnPR) from a Building Information Modelling (BIM) Vegetation Library. Journal of Digital Landscape Architecture 2022 (7) : 224-235. ScholarBank@NUS Repository. https://doi.org/10.14627/537724022
Abstract: The adoption of Building Information Modelling (BIM) for Landscape Architects is ham-pered by a multitude of administrative as well as technical hurdles. One such hurdle is the lack of a unified vegetation library that can be drawn on and done independent of the BIM authoring software to be used. The latter consideration points towards the use of the Industry Foundation Classes (IFC) format as an international standard for openBIM data exchange. This paper demonstrates the use of a developed vegetation library that exists outside of any authoring platform and uses IFC instead as a mediator to generate 3D vegetation models and enrich them with information drawn from the library developed for the Singaporean context. Specifically, the workflow demonstrated in this paper is the calculation of Green Plot Ratio (GnPR) automatically from the enriched 3D vegetation models. It is shown in this study that such automation not only greatly reduces the time required to calculate GnPR but also reduces the possibility of human error in doing so. It is anticipated that in future, changes to how GnPR is calculated at the species level can be much more easily resolved using the developed method thereby allowing for more accurate metrics other than just GnPR to be tabulated for various greening efforts in Singapore.
Source Title: Journal of Digital Landscape Architecture
URI: https://scholarbank.nus.edu.sg/handle/10635/228039
ISSN: 23674253
2511624X
DOI: 10.14627/537724022
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