Energy absorbing cab guards for log trucks
Bibliographic record
Abstract
This article examines the performance of an existing log truck cab guard subject to impact from the load of logs shifting forward during sudden truck deceleration, and considers modifications to the cab guard to reduce the effect of impact. It was found that the logs impacted the existing cab guard with a load sufficient to reach the design displacement of 0.25 m and to exceed the yield stress in the foot and in the gusset for log truck decelerations greater than 32.5 m/s2. Increasing the cross section of the cab guard foot reduces the maximum stress found in the foot; however, this resulted in the maximum stress in the gusset increasing from the original design. Increasing the gusset thickness resulted in a slight decrease in the maximum stress found in the gusset, with little change in the maximum stress found in the foot. Increasing the yield stress of the steel did not change the stress distribution; however, this did result in the maximum stress found in the foot being below the new yield stress. Adding an energy absorbing pad to the cab guard improved all the performance metrics resulting in the maximum stress in the foot being well below the original yield stress, reducing the maximum stress in the gusset to near the yield stress, and reducing the displacement by almost a third.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".