Utilizing augmented reality for the assembly and disassembly of panelized construction
Bibliographic record
Abstract
Prefabricated construction allows for efficient resource usage while creating higher-quality products that can be assembled on-site within a short time. While this translates to significant benefits for the overall construction, challenges arise from an increased demand for trained prefabrication assembly workers. As prefabrication calls for skills differing from traditional construction, the local labor force can be negatively affected to impede the successful uptake of prefabricated construction. Upskilling the local workforce to take on prefabrication assembly and potential disassembly can solve this problem. This is more relevant to remote construction projects as they stand to gain more from prefabricated construction. This study presents two workflows for creating Augmented Reality (AR) solutions. The AR solutions are aimed to help workers transition between traditional and prefabrication assembly in a panelized construction project. They are: (1) using QR codes to identify a panel’s intended location and construction sequence and (2) using predefined markers to show required equipment and on-site assembly procedures. The solutions are delivered through smartphones, which are readily available and provide a cost-effective medium. Furthermore, developed workflows present an opportunity to implement Design for Disassembly (DfD) concepts in a project. The proposed workflows show the potential to substantially help communicate to the workers the instructions on both the panel assembly and disassembly activities and upskill the local workforce to support the transition to prefabrication assembly in construction projects.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.006 | 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".