Hydraulic and mechanical response of base course aggregate with recycled glass particles
Bibliographic record
Abstract
The industry of glass recycling remains a challenge in the province of Quebec due to the lackof equipment required to produce new glass that meets the manufacturing quality. All kinds of glass can be recycled indefinitely because it can retain the similar properties of the reference glass used in the manufacture. Using recycled glass in fabricating new glass needs to separate glass by colors. In the province of Quebec, the collect of recyclable materials is done in a way that glass containers break during transportation and all recyclable materials such as carton, plastic, paper and glass are mixed together. Hence, mixed-colored glass that is unsuitable for producing new containers is utilized in other applications or sent to landfill. This thesis is devoted to using recycled glass (RG) as a replacement for aggregate in pavement structures, which can be a solution for the problems of glass recycling in Quebec. Reusing recycled material in the geotechnical engineering and roadwork has proved a great attraction. However, insufficient knowledge about mechanical characteristics of recycled glass as an aggregate and the shortage of information about the benefit of using it as the aggregate in unbound layers of pavement structures prevents its widespread use. This research program aims to study the advantages and disadvantages of using glass in base course materials from hydraulic and mechanical aspects. The program was divided into three parts. The first part was studying the physical and hydraulic properties of the separate sizes of RG and crushed limestone aggregate, as the reference material. In the second and third parts, the experimental tests were applied to trace the impact of utilizing RG blends with limestone as the unbound granular materials in the pavement structures. A wide range of geotechnical laboratory testing was analyzed to find conclusive evidence for the use of blends of this recycled material in road applications. The findings of this research show that RG blends can provide adequate drainage that is valuable to pavement performance during freeze-thaw cycles in cold region like province of Quebec. The properties of RG aggregate reveals that RG can be safely integrated in pavement base/subbase course regarding hydraulic aspects as well as mechanical aspects. However, it is recommended to limit RG ratio to 25% of fine fraction (0-5 mm) of base course aggregate to keep the least decrease of mechanical properties of blends.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".