Experimental Study and Mathematical Description of Gradation Effect on the Mechanical Characteristics of Crushed Waste Rocks
Bibliographic record
Abstract
Crushed waste rocks, generated from mining operations, have been widely used for mining infrastructure constructions such as haul roads because of their low cost, high strength, and availability. Crushed waste rock gradation can, however, vary greatly, depending on blasting, mineralogy, and crushing process, but it is a key factor influencing the mechanical properties of crushed waste rocks (including resilient modulus, permanent deformation, and shear strength). Gradation should therefore be optimized to enhance their performance in the field. A series of repeated load and monotonic triaxial tests were carried out on crushed waste rocks with different gravel-to-sand (GS) ratios and fines contents (FC). Results showed that the optimum GS ratio was between 1 and 1.5 and contributed to provide higher resilient modulus and shear strength, and lower permanent strain. An increase in FC could, to the contrary, result in the decrease of resilient modulus and permanent strain and also a significant increase in shear strength. The structure state of crushed waste rocks was quantified using the cm model, and the mechanical properties of crushed waste rocks were dominated by sand and fines when the content of sand and fines was higher than 80%, while it was dominated by gravel particles as the content of sand and fines was lower than 60%. Here, the MR − θ model and the Rahman and Erlingsson model (extended using time-hardening approach) were well adapted to describe resilient modulus and accumulated permanent strain, respectively. Prediction models were also developed based on correlation analyses to predict the resilient modulus and permanent strain of crushed waste rocks based on gradation parameters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".