COMPARISON OF CHARACTERISTICS OF BIM VISUALIZATION AND INTERACTIVE APPLICATION BASED ON WEBGL AND GAME ENGINE
Bibliographic record
Abstract
Abstract. How can we make the building information model accessible to all stakeholders on a project? An efficient approach is to access the building information model is to use the software that created the model. However, not all stakeholders will be able to use this highly specialized software—due to lack of training and expensive licences—even if some software specially developed a simplified version of the viewer to browse the model, however, it still failed to provide convenient access to these models for participants from a wide range of backgrounds. The current development of BIM model visualization and interactive applications is mainly based on two technologies: WebGL and game engines. What is the general workflow of WebGL and Game Engines supporting application development? What are their characteristics respectively? What conditions are restricted? There are no relevant academic papers to discuss and compare these two types of platforms. Therefore, this is the content of this essay. By comparing the workflow and characteristics of BIM visualization and interactive application development based on WebGL and Game Engine, it can provide a reference for heritage building managers when planning the development of relevant application tools and meet the participation needs of different stakeholders.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".