A non-destructive crack detection method for steel jacket offshore platforms based on global and local responses
Bibliographic record
Abstract
A crack detection method for steel jacket offshore platforms based on global (displacement) and local (strain) responses is presented in this paper. One side panel of an idealized three-dimensional space frame structure was used in the numerical analysis; substructuring technique in finite element method was utilized as the numerical analysis procedure. Isoparametric thin shell elements, with reduced Gaussian integration, were used to discretize the whole structure. ABAQUS finite element software was used to solve the problem and process all information relating to the above-mentioned global and local responses. Numerical results obtained in the analysis have been compared with those obtained using other types of elements available in ABAQUS. The global and local parameters (displacements and strains) have been normalized using those obtained from the uncracked structure. It has been observed that the largest changes (in strain and stress) occur around the crack, but significant changes occur even away from the cracking weld toe. For cracks occurring at "one or both" saddle point(s), significant differences in strain exist to identify whether the cracking is on one side or on both sides. Significant change in slope of normalized global displacements, obtained around the crack, indicates the location of crack.Key words: crack detection, global and local responses, substructuring.
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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) | 0.001 | 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 | 0.001 | 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".