Effect of acceleration time history in motorcoach frontal collision on passenger safety under uncertainty of seating posture
Bibliographic record
Abstract
National authorities constantly work on enhancing the crash safety regulations for motorcoaches in North America, promoting further research on the factors affecting passenger safety in severe collisions. The thesis presents the results of a comprehensive study on the effect of the acceleration load experienced by a coach passenger compartment during a frontal collision on passenger safety. As a large variation in seating posture is found among the coach passengers and owing to its high influence on passenger kinematics during a crash, a special methodology is developed to account for posture uncertainty. The study on the influence of the pulse shape on the occupant’s injury criteria is performed numerically using a validated model of the motorcoach sled test equipped with two belted and two unbelted Anthropomorphic Test Devices. The results indicate that the probability of an injury for unbelted occupants seating in a comfortable posture can be significantly increased compared to the standard posture specified in the regulations, with up to 113% increase in Head Injury Criterion and a 97% increase in Femur Loads. For the parametric pulse shape variations under the uncertainty of seating posture, the dependence of most of the injury criteria values on pulse shape is found to be insignificant on the scale of the values’ deviations due to stochastic posture alternations. Overall, the obtained results emphasize the importance of accounting for posture variations in both regulation testing and numerical parametric studies. It is suggested that a parametric study can be carried out independently from a posture variation analysis, enabling considerable computational time savings while still accounting for the dispersion of the results. For regulation testing, a preparatory numerical posture variation study is recommended before the physical test to identify dangerous seating postures for a given impact scenario and to ensure the worst-case scenario is reproduced. The proposed methodology provides the basis for the development of a contemporary approach to the problems of the occupant protection under real-world conditions relying on probabilistic rather than traditional deterministic design philosophy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".