Surface rutting of thin pavements and gravel roads under standard and reduced tire inflation pressures
Bibliographic record
Abstract
In Canada, over 300 heavy vehicles equipped with central tire inflation systems are being used in forestry, mining, and grain hauling. Since 1995, Saskatchewan Highways and Transportation has permitted truck fleets to operate with primary highway axle loads on secondary highways under a partnership program. This paper reports on the comparison of rutting progression on an accelerated field experiment utilizing standard and reduced tire pressures. The experiment was conducted by Saskatchewan Highways and Transportation in the Big Quill Lake area of southern Saskatchewan. The vehicles used in the experiment were nine-axle B trains, eight-axle B trains, and six-axle semi-trailers. The statistical analysis of rutting data presented in the paper shows that reduced tire pressures can effectively extend the pavement service life of gravel and thin membrane surfaced roads. On the thin membrane pavements, there was less rutting on the low-pressure lane until twice as many vehicles had trafficked the road, and rutting was no worse on the low-pressure lane even when 10 times as many vehicles had trafficked the road. On the gravel surface, rut depth was lower on the low-pressure lane until twice as many trucks had trafficked that lane.Key words: rutting, pavement, central tire inflation, tire pressure, low-volume roads, unsurfaced roads.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".