Heavy Truck Dynamic Rollover: Effect of Load Distribution, Cargo Type, and Road Design Characteristics
Bibliographic record
Abstract
The dynamic response of a five-axle tractor–trailer unit carrying loads of various weights was evaluated under actual operating conditions as the vehicle traveled along roadway curves with various radii. A data acquisition system (DAS) was designed and developed to record information for a vehicle moving at highway speeds. The DAS used sensors interfaced to a central processing unit. The test runs were performed over a total of 1,110 km of highway under three load configurations: empty, loaded with less than the truck load, and loaded with bottled spring water packed in boxes. Data on lateral, longitudinal, and vertical accelerations; steering activity; vehicle speed; and roll angle of the trailer were recorded. It was found that in most cases the average level of lateral acceleration exhibited on both the tractor and the trailer exceeded expected values calculated for the curves on the basis of geometric design characteristics. Comparisons of actual results with theoretical considerations confirmed this observation. Analyses of field data indicated that under certain motion and load conditions, the occasional peak lateral acceleration values generated were in the vicinity of rollover threshold values estimated for the instrumented vehicle. The results also showed that although the vehicle traveled at or below the posted speed limit in the majority of cases, lateral accelerations recorded for the trailer exceeded expected lateral accelerations under all load configurations. This suggests the need to consider establishing speed limits on curves that take into consideration the different responses of heavy trucks compared with those of smaller and lighter vehicles.
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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".