Direct Measurement of the Impact of Heavy Loads on Thin Membrane Pavements
Bibliographic record
Abstract
This paper presents the results of a field study of the loads imposed by heavy oilfield cranes (with hydraulic suspensions and super single tires) on thin membrane asphalt pavements in Alberta, Canada. Three 150-m test road sections (thin asphalt wearing course, bituminous surface treatment, and granular surface) were built and instrumented for strain at the bottom of the asphalt layer, surface deflection, and subgrade pressures. Temperature and moisture profiles were also measured. Field testing involved controlled speed experiments of standard axle configurations and heavy-axle (12,000-kg) vehicles with and without hydraulic suspensions. Focusing on the hot-mix asphalt section, this paper presents a description of the test road design, instrumentation, and testing plan, followed by some results and findings from two seasons (spring and fall 2005) of testing. Vertical stress in the subgrade, longitudinal interfacial strain, and surface deflection are compared for three vehicle types used in the test. Results from tests show that subgrade stress and interfacial strain are very similar for the standard axle configuration during spring compared with those of the cranes without the dolly during the fall season. It could be argued that on the basis of the pavement response, the cranes could operate during the winter season without the dolly (and thereby increase road safety by removing a long combination vehicle from the traffic stream) without causing substantial long-term deterioration.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".