Reliability assessment of ship hull girders considering pitting corrosion and crack
Bibliographic record
Abstract
Abstract The current study aims to investigate the combined effect of cracking and pitting damage on the ultimate strength of ships. The well-known Smith’s approach is modified considering the random number and distribution of cracked-pitted plates in the ship cross-section. Using the Monte Carlo approach, the structural reliability index of the cracked-pitted ship is determined. A single-bottom oil tanker’s ultimate strength is computed, and it turns out that the reliability indices for various damage scenarios are nearly identical when the ship is at its early age. When the ship ages, its reliability index rises to its maximum if the damage is concentrated at the bottom under sagging conditions and at the sides and longitudinal bulkheads in hogging conditions. The reliability indices in the hogging conditions are often greater than those in the sagging conditions. Furthermore, it is determined that, while the ship is at its early age, the detrimental effect of pitting, cracking, or a combination of both on the reduction of the ship’s hull girder ultimate strength is equal. The lowest reliability index is seen in aged ships when cracking and pitting are combined, followed by cracking and pitting damage separately. It is shown that pitting corrosion has a lower reliability index than the general type of corrosion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.000 | 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 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".