Predicting one-dimensional compression of tire derived aggregate using a simple method
Bibliographic record
Abstract
Tire derived aggregate (TDA) is a material that exhibits high compression under loading, a factor that governs its design and performance in civil engineering. Due to the large size of TDA particles (up to 305 mm), it is hard to obtain undisturbed TDA samples from the field to perform compression testing in the laboratory. Commonly, laboratory compression tests are conducted on TDA with small particle sizes, and hence the stress-strain curves obtained cannot be directly used to predict the field compression of TDA with different particle sizes and initial unit weights. To solve this problem, this study proposes a simple method to predict the compression of TDA based on its compression modulus (Ec)-void ratio (e) relationship under one-dimensional loading. The effectiveness of this method is evaluated using experimental data from laboratory and field tests. The results indicate that TDA samples with different particle sizes and initial unit weights have a very similar Ec − e relationship under one-dimensional loading. Hence, the Ec − e relationship can be determined from a relatively small-scale laboratory compression test, and can then be used to predict the compression of TDA with different particle sizes, initial unit weights, tire sources, and test scales. The accuracy of the prediction relies on the accuracy of the measurements for specific gravity and initial unit weight.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".