A Case Study for 3D Modeling Process Analysis based on BIM Log File
Bibliographic record
Abstract
Building Information Modeling (BIM) design becomes popular with the growing needs of enhancing construction efficiency. During the design process, 3D Modeling is the foundation of generating and analyzing building information. The modeling tool, Autodesk Revit, automatically creates log files, which can record all design activities. This paper investigates and analyzes the operation data from unstructured temporal Revit log files. These data were recorded when executing the general and external command to build a 3D model of a typical residential two-story building. The objectives of this analysis were to figure out the influence elements of producing a good framing model and to give some suggestions for improving modeling efficiency. In order to extract the useful specific commands, the information in the text journal file was transferred to an excel file, before reading by a data mining tool, RapidMiner. It generated a pie chart to show the repetition amount of modeling activities. Analyzing the reasons of repeating some commands frequently, this paper contributes the possibility to reduce the useless repetition for building a good 3D model.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".