Crashworthiness of Motor Vehicle and Traffic Light Pole in Frontal Collisions
Bibliographic record
Abstract
The mitigation of severe problems resulting from vehicle collisions with roadside objects has become one of the major research areas in automotive engineering. The literature review shows that few attempts in finite-element computer simulation of vehicle collision with roadside hardware have been conducted. However, limited research has been conducted to enhance the safety performance of traffic light poles when impacted by vehicles. The objective of this paper is to generate information that can be used to enhance energy absorption characteristics of transportation infrastructure involved in vehicle crash accidents. A finite-element computer model, using the available LS-DYNA software, was developed to simulate vehicle collision with a traffic light steel pole in frontal impact. Five configurations of steel pole supports were examined, including embedding the pole directly into the soil. Different types of soil conditions were examined to study their effects on vehicle occupant safety. The study of structural response focused on the energy absorption, acceleration, and deformation of the steel pole and the vehicle. It is demonstrated from numerical simulations that the steel pole embedded directly into the soil is proved to be strong enough to offer protection under service loading and to remain flexible enough to avoid influencing vehicle occupants, thus reducing fatalities and injuries resulting from vehicle impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".