Developing Virtual Reality Workflow for Panelized Construction Assembly Training
Bibliographic record
Abstract
Prefabricated construction, while demonstrating significant benefits of advancing construction efficiency, requires an increasing number of trained prefabrication assembly workers. However, the construction market has been facing the long-standing challenges of skilled labor shortages, especially in projects in remote areas. In addition, prefabrication projects can subtly and negatively impact the local labor market, as people may lack skills for prefabrication. Upskilling the local laborers to improve their understanding of the assembly processes of the prefabrication can solve both challenges. While virtual reality (VR)-based construction assembly training has been widely studied in the past to focus on the user’s immersive experience, its integration with assembly processes has not been explored adequately as a convenient and low-cost solution to address installation needs. In this research, we explore how VR can be used to train local laborers in the assembly of prefabricated Structural Insulated Panels. The research presents a simplified workflow to create a VR training application that uses commodity VR equipment and can be easily adapted at a low cost. The presented workflow was tested on the concept of “gamification of VR training” to increase the skills transfer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".