Rollover Analysis of Heavy Vehicle Bus
Bibliographic record
Abstract
Rollover is motor vehicle accident that occurs when vehicle is tipping over onto its side or roof. Due to its fatality rate, the Malaysian government reinforced an Economic Commission for Europe of the United Nations (UN/ECE) Regulation no. 66 (R66) upon bus construction. This is to prevent the catastrophic consequences of rollover accidents. The R66 regulation provides an option of certification based on full-scale vehicle testing that maitaning the survival space. Therefore research that contribute to the development of safe transportation vehicle under rollover is really important. The physical prototype of rollover test can be simplified using simulation model. Using this motivation, the characteristic of heavy vehicle rolleover is investigated in this paper. The simulation was performed using ANSYS simulation tool and simplified by locating the position of the bus in unstable equilibriumm, just before it hit the ground. Another method is to perform a quasi-static loading test. The quasi-static simulation test was performed using impact load that directed towards the side of beam around the centre of frame body. The dynamic response due to rollover impact was determined using an Explicit Dynamic Analysis in ANSYS. The stress maximum stress first developed around the impact area before lag the stress stream to the opposite side. It can be observed that the maximum stress point is located at the middle structure of impact side. After few times of impact, the maximum stress starts to changes to the opposite side. Quasi-static simulation result in higher total deformation on impact side area. It also indicates high maximum stress point around the middle structure.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".