Effects of fines on liquefaction behaviour in well-graded materials
Bibliographic record
Abstract
This study uses a critical state soil mechanics perspective to understand the mechanics behind the liquefaction of metallic ores during transport by ship. These metallic ores are transported at relatively low densities and have variable gradings containing a wide range of particle sizes and fines contents. The effect of the fines content on the location of the critical state line (CSL) and the cyclic liquefaction behaviour of well-graded materials was investigated by performing saturated, standard drained and undrained monotonic and compression-only cyclic triaxial tests. Samples were prepared at four different gradings containing particle sizes from 9.5 mm to 2 μm with fines (<75 μm) contents of 18%, 28%, 40%, and 60%. In the e versus log[Formula: see text] plane, where e is void ratio and [Formula: see text] is mean effective stress, the CSLs shifted upwards approximately parallel to one another as the fines content was increased. Transitional soil behaviour was observed in samples containing 28%, 40%, and 60% fines. A sample’s cyclic resistance to liquefaction depended on a combination of its density and state parameter, which were both related to the fines content. Samples with the same densities were more resistant to cyclic failure if they contained higher fines contents. The state parameter provided a useful prediction for general behavioural trends of all fines contents studied.
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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 | 0.000 |
| 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) | 0.001 | 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".