Laboratory Characterization of Saskatoon Tire Derived Aggregate (TDA)
Bibliographic record
Abstract
Tire derived aggregate (TDA) is composed of shreds of scrap tires varying in size. Tire derived aggregate has been used as a replacement for crushed rock aggregate in various Civil Engineering applications including embankments, pavement structures, fill, and leachate collection systems. Using shredded tire in lieu of conventional rock aggregates offers environmental and economic benefits. Tire derived aggregate has good thermal insulator characteristics and can therefore be used to reduce frost penetration. It also promotes good drainage, is lightweight, compressible, and has no harmful leachates. Using tire derived aggregate offers these unique properties at a reduced cost compared to conventional aggregates. Using tire derived aggregate also reduces pressures on aggregate pits and landfills, where the tires would otherwise be discarded. This study examined the physical characteristics of tire derived aggregate and sand mixes in the laboratory for road sub-structure drainage applications. Non-linear permeability and stiffness analysis of 100% TDA, 100% sand, and various TDA-sand blends was performed. It was found that a 70/30 blend of clean sand and tire derived aggregate provides adequate structural capacity while still maintaining good drainage characteristics. The material properties of these mixes were used as inputs in a three dimensional finite element model to perform simulations and generate road primary response outputs. Based on the analysis performed, it was determined that tire derived aggregate systems exhibit highly non-linear material constitutive behaviour in terms of permeability as well as mechanical primary response with respect to stress state. It was also determined that when designed properly, tire derived aggregate is a technically and economically sound alternative for road substructure drainage layers.
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 | 0.000 | 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".