Residual ultimate strength of a damaged deck grillage structure
Bibliographic record
Abstract
• Multi-cycle loading tests on a full-scale damaged grillage structure. • Nonlinear FEA modelling with parameterized material model. • Good agreement in nonlinear FEA predictions and test measurements. • 20.7 % reduction in ultimate strength in compression due to damage. • Modelling method applied to four previously tested undamaged grillages. A deck grillage structure was extracted from a decommissioned warship (ex-HMCS IROQUOIS) and damaged as the result of a dynamic pressure loading test, resulting in overall permanent multi-bay deformation of the plating and attached members. The damaged grillage was then re-configured for residual ultimate strength testing under longitudinal loading. The test article spanned three complete frame bays plus half-bays at each end and four continuous longitudinals of the original structure. In addition to thickness and material property measurements, Light Detection and Ranging (LiDAR) measurement of the damaged panel was carried out after re-configuration. The residual strength testing consisted of compressive loading to collapse and post-collapse, followed by two tension-compression cycles. Numerical assessments of the residual strength were performed using nonlinear finite element analysis (FEA) and material models based on measured material properties from material recovered from the ship. Excellent agreement is achieved between the measured and predicted load-shortening behaviour through progressive adjustment of the material modelling parameters. The deformation damage is estimated to result in a 20.7% loss of ultimate strength. The modelling approach developed here is then extended to the analysis of four previously-studied grillage structures recovered from the same vessel.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".