Pavement performance over deep trenches backfilled with recycled aggregates under different compaction efforts
Bibliographic record
Abstract
Insufficient understanding of the stress–strain behavior of pavements built over backfilled trenches, particularly with recycled aggregates, often leads to overdesign or overcompaction, raising costs and project delays. This research investigates how compaction levels during backfilling impact the pavement performance over these trenches. Various recycled material mixtures, both unbound and cement-treated, are compared with conventional crushed rock. Investigations included repeated load triaxial (RLT) tests, microstructural analysis with scanning electron microscopy, environmental assessments, and modeling with FlexPAVE™, a pavement response and performance analysis software. RLT test results were incorporated into the FlexPAVE™ models by utilizing established constitutive resilient modulus models. Stress–strain responses of pavements over recycled aggregate backfill, compacted with standard and modified Proctor efforts, were compared with those over crushed rock and natural clay subgrades. Outcomes revealed that the standard compaction energy was sufficient for the desired performance. Fatigue and rutting strains with recycled mixtures closely resembled those with crushed rock, making them viable green alternatives. Pavements over backfilled trenches exhibited 1.5 and 1.8 times longer fatigue and rutting lives, respectively, than those over natural clay subgrades.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".