Bibliographic record
Abstract
Priority waste materials currently generated in Australia include construction wastes, demolition wastes, glass fines, waste tyres, plastics, industrial wastes and organic wastes. The increase in generation of these wastes have led to significant research over the past decade on the reuse of recycled waste materials in geotechnical engineering applications. An estimated 7.9 Mt of wastes, which accounts for 36% of Australia's current landfilled waste, have the potential to be diverted into civil engineering applications, such as for the design and construction of roads, railways and ports. This paper discusses recent advances in the usage of recycled materials in pavement geotechnology projects in Australia. Recycled materials have been evaluated in the laboratory and new specifications successfully developed, to incorporate their usage in pavement geotechnology and ground improvement applications. Recycled materials are increasingly being used in unbound and stabilised pavement applications. In addition, industrial wastes such as fly ash and slag have also been evaluated in recent years as alternative binders to Portland cement in pavement and ground improvement applications. Several unique field case studies in Australia, where recycled materials have been used in roads and footpaths, as well as the development of several prototype testing equipment will also be discussed. Ongoing research projects on new priority waste materials will also be briefly discussed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".