Structural Capacity of Sections Constructed with Different Waste and Recycled Embankment and Insulation Materials at the Integrated Road Research Facility Test Road after Five Years of Operation
Bibliographic record
Abstract
The Integrated Road Research Facility test road was constructed in 2012 to study the application of waste and recycled materials in road construction in cold regions. Bottom ash (BA) and polystyrene board were used in test sections as insulation materials. Tire-derived aggregate (TDA) from passenger and light-truck tires (PLTT), off-the-road (OTR) tires, and a mixture of TDA from PLTT and soil were used as embankment fill materials in three additional test sections. Two control sections were used to evaluate the performance of the test sections. To compare the long-term impact of these materials on the load-bearing capacity of the pavement, falling weight deflectometer (FWD) tests were conducted after five years of operation. FWD data were used to back-calculate the subgrade modulus, effective modulus, and effective structural number. It was found that although embankments backfilled with TDA from PLTT and OTR gave an initial improvement in the load-bearing capacity of the pavement, a significant loss in load-bearing capacity of pavements with TDA embankments was observed after five years. In contrast, the test section with an embankment backfilled with a mixture TDA from PLTT and soil performed close to the control section, and no significant loss in load-bearing capacity was observed during this study. The test section insulated with polystyrene showed lower load-bearing capacity and a higher loss in load-bearing capacity after five years of operation. However, the test section insulated with BA performed close to the control section and had a lower loss in load-bearing capacity than the control section.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".