Ship frame and grillage structural experiments
Bibliographic record
Abstract
This paper discusses an experimental ship structural research program that is well underway at Memorial University. The series of structural experiments is exploring the extreme plastic behaviour of full scale ship frames under intense local loads. The program began with tests on single frames with support from Transport Canada and with a view to validating of the frame limit state equations that were developed for the recently developed Unified Requirements for Polar Ships. The program was then expanded to include small grillage tests, this with the aid of support from the US Coast Guard. The program was further expanded to include large grillage tests as part of a Ship Structures Committee project (SR1442) (managed by TC and DRDC). As a sequence of related experiments, we are able to see the influence of increasingly realistic boundary conditions on the plastic structural behaviour of a frame. The paper describes the experimental test arrangements, the data collection strategy and presents some of the results. Load, strains and large scale distortions are the main items being measured. Extensive ANSYS finite element analyses of frames/grillages have been conducted, which extend the range of the investigation. The work is still underway and results are only partial. Nevertheless, several new insights have been found. Proper consideration of plastic behaviour can help produce ship frames with both an enhanced linear range and large stable plastic reserve.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".