Modeling Analysis of B-Train Trucks on Saskatchewan Pavement Structures
Bibliographic record
Abstract
Saskatchewan's economy depends on bulk commodity transport including agriculture, logging, livestock, oil, and mined mineral resources. The primary mode of transport for these bulk goods is by truck. Saskatchewan's economy is dependent on interprovincial and international trucking transport and relies on over 25,000 centerline kilometers of primary and secondary highways to move these goods. The Ministry of Highways and Infrastructure policies and regulations enforce truck type and axle weight on Saskatchewan provincial highways. Recently, the Ministry decided to investigate the effect of a 9-axle B-train loading configuration on Saskatchewan's highway network. The purpose of this study was to quantify the damage caused by a 9-axle B-train truck on Saskatchewan road structures with different axle loads and to compare it to the damage caused by a typically loaded 8-axle B-train truck configuration. This analysis was carried out using a computational mechanics road model that considers non-elastic material behaviour, Saskatchewan field state conditions, pavement structures, materials, road construction, climatic effects, and truck configurations. For this study, damage is defined as the permanent deformation of the roadway directly beneath the truck tires after one 10 s truck cycle. This study found that when loaded past its baseline load, the 9-axle B-train truck resulted in more damage than an 8-axle B-train truck, when comparing across an asphalt concrete pavement in good and poor condition, under freeze-thaw and high temperature climatic effects. (A) For the covering abstract of this conference see ITRD record number 201310RT334E.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".