Impact of tridem and trunnion axle groups on premature damage of pavement infrastructure
Bibliographic record
Abstract
To make appropriate decisions on overload limits of various axle configurations that can be endorsed for routine permitting, highway agencies need to understand the impact of these axle groups in terms of pavement infrastructure damage. This paper examines the relative damage to pavements induced by tridem and trunnion axle groups. The analysis was conducted with typical structures of both flexible and rigid pavements by first analyzing the mechanistic responses of pavements to tridem and trunnion axle groups. Then the mechanistic responses were used as the input to performance-based fatigue models to quantify the relative accumulative damage to the pavements. The use of the performance-based fatigue models ensured that all types of damage (such as rutting and cracking) induced by the axle groups were taken into consideration. Based on the analysis results, it was found that for flexible pavements, tridem axle groups are more damaging than trunnion axle groups, whereas for rigid pavements, trunnion axle groups are more damaging than tridem axle groups.Key words: trunnion, tridem, load equivalency, pavement damage.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".