An Exploratory Study of Whole-Body Vibration Exposure and Dose While Operating Heavy Equipment in the Construction Industry
Bibliographic record
Abstract
Whole-body vibration measurements were recorded for various types of heavy equipment used within the construction industry. The purpose of these measurements was to provide more information about the potential levels of whole-body vibration experienced by equipment operators in the construction industry, as well as to identify types of equipment warranting further research. In total, 67 pieces of equipment were tested from 14 different equipment types. Testing took place at various construction sites including corporate, public, and residential work projects. Measurements were made (following the 1997 International Standards Organization's 2631-1 whole-body vibration standards) for 20-minute testing periods using a Larson Davis HVM100 vibration monitor and a triaxial accelerometer. The mobile equipment tested was associated with greater levels of whole-body vibration than the stationary equipment. When whole-body vibration levels were compared to the International Standards Organization's 2631-1 standards, wheel loaders, off-road dump trucks, scrapers, skid steer vehicles, backhoes, bulldozers, crawler loaders, and concrete trowel vehicles exceeded the recommendations based on measured vibration dose values. Further research incorporating larger sample sizes and controlled testing conditions is required to better understand the levels of exposure experienced by operators as well as the amount to which seating, terrain, mobility, and vehicle structure might affect whole-body vibration.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".