Estimation of permanent deformation behaviour of crushed waste rocks using multistage repeated load triaxial and CBR tests
Bibliographic record
Abstract
Crushed waste rocks (CWR) are widely used to build mine haul roads. However, the permanent deformation in waste rocks layers can result in surface rutting. Realistic prediction of pavement rutting requires models that can accurately capture the permanent deformation behaviour under repeated loading. However, such models are usually based on advanced laboratory apparatus such as multistage (MS) repeated load triaxial (RLT) tests. In this study, a new approach, using MS repeated load California Bearing Ratio (RLCBR) tests, was proposed to estimate the permanent deformation behaviour of CWR. MS RLCBR tests are faster, easier and more often available than MS RLT tests. A series of MS RLCBR and MS RLT tests for different stress levels were therefore carried out on the same material to characterise CWR permanent deformation behaviour. Results showed that Rahman and Erlingsson model that modified by time hardening approach could satisfactorily capture CWR permanent deformation behaviour for MS RLT tests. A new model was proposed and fitted on MS RLCBR test results to predict CWR permanent deformation behaviour. This model performed well in describing MS RLCBR test results and predicting the CWR permanent deformation behaviour. Results indicate that MS RLCBR tests could be an effective alternative to MS RLT tests for estimating the permanent deformation behaviour of CWR.
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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 | 0.001 |
| 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".