Cartesian Points Visualization in Game Simulation for Analyzing Geometric Representations of AEC Objects in IFC
Bibliographic record
Abstract
Cartesian Points Visualization in Game Simulation for Analyzing Geometric Representations of AEC Objects in IFC Jiansong Zhang, Yunfeng Chen, Rui Liu and Luciana Debs Pages 144-151 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: Industry foundation classes (IFC) is widely accepted as a promising standard for building information modeling (BIM). IFC data can be processed with many open toolkits such as IfcOpenShell and java standard data access interface (JSDAI), which greatly supports BIM research and technology development. However, IFC data is not intuitive and requires training to understand it fully. As the core of almost any IFC data, understanding geometric representation is critical in most BIM research and technology development. The official IFC schema specifications provide detailed explanations of entities and attributes in IFC, which are helpful for gaining such understanding. However, understanding the explanations in the specifications requires certain knowledge and background. To facilitate an easier understanding of IFC data and to promote a wider adoption of IFC-based BIM, in this paper, an interactive visualization of the formation of fundamental 3D representations of a selected architecture, engineering, and construction (AEC) object was created in game simulation in a first-person view. The interactive simulation can help people gain understanding of 3D geometric formation and representation in IFC in an intuitive and speedy manner, which is expected to achieve retention of such knowledge comparable to or better than the conventional way of reading the specifications. The visualization was tested by 14 volunteers in comparison to reading the IFC schema specifications. A survey based on the experiment showed that the game simulation-based visualization was significantly easier to understand and took significantly less time to understand comparing to reading the specifications. Keywords: BIM; IFC; Geometric Information; AEC Objects; Game Simulation; Visualization; Cartesian Points DOI: https://doi.org/10.22260/ISARC2019/0020 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".