Field Evaluation of Load-Bearing Capacity of Tire Fill Embankment Pavements
Bibliographic record
Abstract
Abstract Tire-derived aggregates (TDAs) are produced by shredding scrap tires. These materials have desirable engineering properties and can be appropriately used in pavement embankment fills. Several pavements have been constructed using TDA fill embankment; however, the seasonal performance of the pavements composed of these materials has not been widely investigated. This study investigates seasonal changes in load-bearing capacity of tire-filled embankment pavements after two years of construction in comparison to conventional pavement in a test road in Edmonton, Alberta, Canada. Three sections were constructed using different TDA materials, including passenger and light-truck tires (PLTT), off-the-road (OTR) tire particles, and a mix of PLTT and local subgrade soil, which was placed adjacent to a conventional section that acted as a control section. Falling weight deflectometer (FWD) tests were conducted in different seasons, and the back-calculation results revealed that although the subgrade of the TDA sections showed higher deflection and a lower resilient modulus compared with the control section, the load-bearing capacity of the TDA sections was greater than that for the control section. The section constructed using a mix of TDA material and soil showed almost the same performance as the control section.
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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".