A VR Model of Ergonomics and Productivity Assessment in Panelized Construction Production Line
Bibliographic record
Abstract
Factory based (modular and panelized) building methods have been applied to a high proportion of construction projects due to their reduced waste, limited environmental impact, and decreased cost and construction times. Redesigning the production process, facility layout, and material handling process can thus improve productivity and reduce costs for manufacturers. Virtual Reality (VR) is increasingly being used to investigate the best methods for balancing the flow of construction activities and optimizing resources to prevent costly on-site errors. This tool can be applied in planning and designing module systems in manufacturing production lines to promote healthy, safe, and productive working conditions by reducing workers' fatigue and injuries, and their associated costs. This paper identifies and quantifies work-related ergonomic hazards from residential construction floor panel framing activities, using the VR model of the construction process to replace onsite observation and an ergonomic assessment based on ErgoCheck, a comprehensive ergonomic rating and assessment framework. The VR model uses an internal timer for productivity (cycle time) assessment. The impact of the ergonomic interventions on work productivity is thus assessed and the results show the potential of ergonomic interventions in improving production line productivity through a reduction in idle and cycle time.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".