Ergonomic Assessment of Residential Construction Tasks Using System Dynamics
Bibliographic record
Abstract
residential construction activities often require prolonged standing, bending, stooping, and material handling, while working in crowded spaces; these activities increase the potential risk of work-related musculoskeletal disorders (WRMSDs), which may worsen over time, resulting in permanent disability and, consequently, the loss of ability to work. The use of system dynamics (SD) modeling to assess ergonomic risks provides a decision support tool for job managers and job designers and delivers a powerful graphical illustration, showing the logical links between cause and effects and helps illustrate how ergonomic risks may lead to WRMSDs. This paper presents a SD model for ergonomic analysis of residential construction tasks. A case study is presented and used to evaluate variations in risk exposure to identify most contributing factors to potential ergonomic injury. Also a literature review is performed to identify the main ergonomic risk variables. The results are expected to assist project participants in controlling and assessing ergonomic risks leading to improved work efficiency, safety and reduced lost time injuries and related cost, insurance premium (WCB) and claims caused by WRMSDs.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".