Automated Recognition of Unlabeled Items in BIM Models
Bibliographic record
Abstract
In early stages of fast tracked construction projects—such as mega industrial projects—contractors do not have access to detailed and final drawings. Yet, they usually need rough estimates of material quantities in the project. They usually depend on initial versions of BIM models that contain all trades (e.g., pipes, steel, etc.) information. Because they are premature models, 3D object attributes may be missed, omitted, inconsistent, or incomplete which makes extracting quantities from them a manual, time-consuming, and inaccurate task. In this research, we aim to automate this task by applying shape recognition techniques on unlabeled 3D models. These techniques have been used in different fields such as handwriting recognition, object recognition, and 3D object search and retrieval. We experiment with some of these techniques to estimate material quantities for industrial construction projects based on the geometry of unlabeled 3D objects. We investigate a number of these techniques keeping in mind that the main usage will be a preliminary estimate in early stages of the project; therefore, we favor fast techniques with a margin of error over more accurate—but relatively slow—ones. This paper discusses applicability of these techniques and shows the results of applying and testing them on real industrial construction projects from a partner contractor.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".