A combined suspension seat–vehicle driver model for estimating the exposure to whole–body vehicular vibration and shock
Bibliographic record
Abstract
A nonlinear single–degree–of–freedom suspension seat model is combined with a four–degree–of–freedom vehicle driver model derived from the biodynamic response characteristics of the seated body under typical vehicular vibration in the 0 to 10 Hz frequency range. The vertical whole–body biodynamic response behaviour, in terms of driving–point mechanical impedance and seat–to–head transmissibility, was established for seated subjects maintaining a predefined driving posture under the influence of excitations characterizing the vibration in particular classes of off–road vehicles. The combined suspension seat–driver model is analysed under random and shock excitations predominant at frequencies below 10 Hz, and the vibration exposure levels are computed at the driver–seat interface using various assessment methods defined in the current IS0 2631/1 standard and its proposed revised version. A methodology is applied to compute the exposure levels in terms of frequency–weighted root–mean–square and rootmean–quad accelerations in the convenient frequency domain. The combined suspension seat–vehicle driver model is validated by comparing the exposure levels computed from the model with those measured with a subject seated on a low natural frequency suspension seat, mounted on a whole–body vehicular vibration simulator, driven by the corresponding types of random and shock excitations. Good agreement is obtained between the measured data and the computed response for both categories of excitations. The proposed model is further used to estimate the driver's response and the seat performance as a function of the severity of the shock excitation and vehicle speed. The study indicates that the suspension seat–driver system performs very poorly under high level shock excitations due to interactions with the bump stops.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".