Use of Digital Human Modeling for Estimating Physiological Workloads of Construction Tasks
Bibliographic record
Abstract
Labor productivity and its influencing factors including ergonomics play a vital role in affecting the performance of construction projects. In fact, studying ergonomics and understanding the interactions among workers and their assigned tasks have shown a decrease in workers’ discomfort, a positive impact on labor productivity, a reduction in project costs, and an increase in value creation. As such, several studies have been conducted in an attempt to properly assign construction tasks and optimize the performance of crews. However, no study has yet been carried out to estimate the physiological workloads of construction tasks and match them with the corresponding workers’ capabilities. Therefore, this research study takes the initial steps and aims at using Digital Human Modeling (DHM) to model different construction activities and generate physiological task demands. Several construction activities that require various body postures and affect different body parts are selected and modeled using DHM. The ergonomic and physiological results are then recorded for each activity. The resulting physiological task demands will, in future work, become the foundation of a simulation framework targeted at enhancing the worker-task assignment process and properly mapping the modeled tasks to construction workers based on their physiological capabilities.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".