Reality Capture (RC) Technology for Drywall Installation: A Scan-toPrefab Approach
Bibliographic record
Abstract
Drywall, also known as gypsum board, sheetrock, or plasterboard, is a widely used building sheathing material in the US and Canada to create interior walls and ceilings. Typically, the design and construction documents of a project exclude detailed information about the layout of drywall sheets on interior surfaces. Such information is left to the drywall installation crews to determine solely based on their experience. This inconsistent approach often results in substantial rework and waste of material in the field. The construction industry has seen a significant increase in the adoption of Reality Capture (RC) technology in recent years, with the goal of improving the quality and productivity of various construction activities. This research aims to investigate the implementation of RC technology, explicitly Terrestrial Laser Scanning (TLS) and Structure from Motion (SfM, also referred to as photogrammetry), in drywall installation. The research team has developed a framework that utilizes RC tools to capture the as-built information of the framing members of interior walls and penetrations of the MEP systems and uses these RC data to develop prefabricating shop drawings in a Building Information Modeling (BIM) platform for drywall cutting and installation. This framework has been tested and studied on active construction project sites. The preliminary findings indicate that this framework has the potential to lead to a more precise and efficient drywall installation process. This paper also proposes a process model for the execution of the proposed framework for improving drywall installation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".