Use of Digital Human Modeling for Estimating Physiological Workloads of Construction Tasks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it