Slope stability evaluation of iron ore fines during marine transport in bulk carriers
Bibliographic record
Abstract
A commodity, such as iron ore fines, shifting in the hold of a bulk carrier can lead to the vessel listing or capsizing. The objective of this study is to investigate the factors of safety pertaining to slope failure for both untrimmed and trimmed cargoes of iron ore fines during marine transportation. To determine the shear strength parameters needed to perform this analysis, triaxial testing was performed on samples of iron ore fines under varying densities and moisture contents. Using the shear strength parameters of the material, the Morgenstern–Price method of slices and infinite slope analysis, referred to as rotational and translational slope stability analyses, were used to determine the factors of safety against slope failure. The study concludes that, considering a factor of safety of 1.5, an untrimmed cargo of iron ore fines is unstable at angles of heel that bulk carriers are expected to experience during a typical voyage. If the cargo is trimmed it is shown to be significantly more stable. Results support the recommendation that it become mandatory for cargoes of iron ore fines to undergo trimming to reduce the chance of slope failure occurring, which may result in the loss of human life and industry assets.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".