Differential rutting in Canterbury New Zealand, and its relation to road camber
Bibliographic record
Abstract
In Canterbury New Zealand, chip seal is the primary surface material for rural state highways. The roads are designed to accommodate various types of traffic; traffic that has grown steadily over the past decade. The growth in dairy and logging, two of New Zealand’s main exports, resulted in a large rise in milk and logging trucks. This significant increase in traffic has led to a significant amount of pavement failure due to rutting which predominantly occurs in the outside wheel path rather than the inside. This paper provides a review and analysis of LTPP data at two rural sites. Data from these sites show more rutting in the outside wheel path than the wheel path close to the crown of the road. Contributing factors observed from the literature are included in this paper and it was shown that the main contributing factor to rutting is load (traffic). Road pavements are constructed to be homogeneous but anecdotally it is known that using camber or a crown will divide the load from traffic more towards the outside wheel. Some general factors that increase the difference are the axle width, and height of the centre of mass, the camber percentage and present rutting depth. Calculations show that the difference in load on the left and right wheel can lead to quite different ESAL values compared to values calculated based on the average load. In fact, an example using Austroads shows that the ESAL value can almost double if actual wheel loads are used. It also shows that there are no other mechanisms that adequately account for this difference.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".