Tire-reinforced earthfill. Part 1: Construction of a test fill, performance, and retaining wall design
Bibliographic record
Abstract
The satisfactory disposal of scrap tires is a major environmental problem worldwide. This waste occupies valuable space in landfill sites, and tire stockpiles pose serious health and fire hazards. The use of scrap tires as reinforcement for construction of retaining walls and slopes is a viable method towards reduction of this waste. This paper describes the construction of a 57 m high × 17 m wide instrumented test fill, comprising both retaining wall and reinforced slope sections. Approximately 10 000 whole tires and tires with one sidewall removed, tied together with polypropylene rope, were used in both cohesionless and cohesive backfills. The testing program also included plate loading tests, field pull-out tests on tire mats, water-quality assessment in the field and laboratory, and other complementary laboratory testing. This first paper, in a series of three, demonstrates the practical feasibility of constructing reinforced earth fills using scrap tires. Results of large plate load tests and the field behaviour with particular reference to the design of the retaining wall sections are presented. The paper emphasizes the role of negative wall friction in increasing the active thrust when the retaining wall becomes more compressible than the backfill. Recommendations for the design of retaining walls using scrap tires are presented.Key words: scrap tires, earth reinforcement, retaining walls, reinforced slopes, plate load test, construction, performance.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".