Effects of Reduced Tyre Pressures on Wear of Thin-Surfaced Roads
Bibliographic record
Abstract
There are about 230,000 km of forest roads in Sweden, and maintenance and construction costs for forest roads per harvested cubic meter of wood is increasing. This is to some extent caused by increased demands for precision in delivery of fresh wood cut to customer specifications, shorter periods with frozen roads, increased vehicle weights and an ageing forest road network. Although many roads are constructed using the Swedish Forest Agency's Guidelines, deviations are made to reduce construction costs. Further, short planning horizons reduces the period of settling, intended to allow the road to dry and consolidate under its own weight. In northern Sweden, central tyre inflation systems are used to reduce road wear from logging trucks and increase the period a road is accessible for traffic with logging trucks. To reduce road costs, the industry needs efficient road building methods and ways to reduce road wear. Thus, they want to know how thinner surface layers influence road wear and if reduced tyre pressures can help to reduce road wear on such roads. The aim of this study was to compare rutting by vehicles with low tyre pressures to rutting by vehicles running with standard highway tyre pressures on forest roads with thin surface layers.Three test roads, each divided into six sections with systematical sampling points for measurement of rut depth and road strength, were built. The test roads were trafficed by a fully laden CTI equipped log truck and trailer. Low tyre pressures were used on one side of the road and standard pressures on the other. Rutting was measured throughout the study. Reduced tyre pressures reduced rut development on two roads, while no differences could be found on the road that had not dried prior to testing. The positive effects of reduced pressures were largest on the best built road sections. The effect of thin aggregate layers should be further studied. This study failed in that sense as the variability in gravel thicknesses was to large within sections. Although the need to access the road may be high, access should not be approved until the terrace has dried and settled. Road wear can be mitigated by using CTI equipped trucks, but not on roads of too low quality.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 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".