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Parallel computing-based online geometry triangulation for building information modeling utilizing big data
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DOI：10.1016/j.autcon.2019.102942.png)
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Abstract
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Building Information Model/Modeling (BIM) upgrades the digitization of buildings from 2D to 3D and has become a common paradigm in architecture, engineering, construction, operations, and facility management (AECO/FM) industry. However, embedding BIM in decision support systems is still a challenge because the BIM standard (Industry Foundation Classes, or IFC) is complex, and the geometries in BIM cannot be directly rendered in decision support systems, e.g., web and mobile-phone applications. Current efforts mainly focus on rendering BIM triangulation data, and limited studies investigate solutions solving quite long running time and enormously large memory usage in BIM triangulation process, especially in big BIM files. This study addresses this issue by introducing a parallel computing framework and providing an online geometry triangulation service. First, the reference relationships among the BIM objects were modeled as a graph according to the IFC specification. Second, the original large IFC file was split into several small independent IFC files in which all geometric objects that share the same shape representation were aggregated. Finally, the small separate IFC files were assigned to and triangulated in different computers in a-parallel computing cluster. Experiments showed that the proposed online service could greatly reduce memory usage and time consumption when triangulating the geometry of BIM objects. Processability has become a critical issue for BIM in the era of construction Big Data. The proposed scheme can triangulate big BIM files efficiently using limited memory and thus can dramatically improve the processability of BIM Big Data.
Keywords:
Building information modeling
Industry foundation classes
Parallel computing framework
Geometry triangulation
Big data
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