Residual ultimate strength of a damaged deck grillage structure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it