Using TDA underneath shallow foundations: simplified design procedure
Bibliographic record
Abstract
Tire recycling and reuse in North America and worldwide have increased considerably, with the aim of reducing the harmful effects of scrap tires on the environment. Accordingly, the use of tire derived aggregates (TDA) in civil engineering applications is on the rise at an unprecedented rate. In comparison to conventional backfill aggregates, TDA is an inexpensive, lightweight material that costs about 25% of the cost of conventional backfill. Thus, there is an increasing trend of using TDA under shallow foundations as a lightweight backfill alternative. However, limited information exists for the design of shallow foundations built on TDA. Hence, the main objective of this paper is to develop a simplified design procedure for shallow foundations built over a TDA layer. Rigorous finite element models were developed and validated using field tests results. Subsequently regression analyses were used to develop the proposed ultimate bearing capacity equation, taking into account the granular layer thickness, TDA layer thickness, footing width, footing shape, footing depth, and the allowable settlement.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".