An Interactive Simulation Approach for an Ergonomic-Driven Workplace Design in Off-Site Construction Facilities
Bibliographic record
Abstract
Workers in off-site construction suffer frequent exposure to ergonomic risks that lead to work-related musculoskeletal disorders (WMSDs). It is therefore essential to perform ergonomic risk assessments to identify awkward postures and accordingly adjust workplace arrangements to ensure a safe work environment. Numerous studies have been conducted to assess risks. However, trial and error method are typically used to suggest modified settings without adequately investigating dynamic interactions between worker postures and design parameters. This paper presents a framework for a worker-friendly workplace design in off-site construction facilities that achieves lowest posture risk along with optimal workplace configurations to consequently enhance productivity. The proposed approach combines interactive worker-workplace simulation with definitive screening design method (DSD) as a new powerful tool of Six Sigma (SS) to examine correlations between design variables and postures to reach an optimal design. 3D modelling that imitates actual worker motions is developed, and inverse kinematics are then applied to predict how worker’s body reacts to workplace changes for each DSD run. As a case study, the suggested method is implemented to propose an ergonomically friendly workplace for the drywall marking task in an off-site construction plant. The results indicate that the proposed framework enables to identify effective design factors and optimal workplace settings, which yield a risk score for the upper body 0.5187 less than the initially proposed solution according to rapid upper limb assessment (RULA).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".