Evaluation of Seismic behavior of damaged offshore jacket platform under the environmental condition
Bibliographic record
Abstract
Most of the fixed offshore platforms in the Persian Gulf have survived more than 25-year design life and have suffered from significant damages in this period.Seismic acceleration modifications and changes in seismic criteria of API-2EQ-2014 increase the importance of seismic assessment of the offshore platforms in the Persian Gulf.This paper presents a case study for modeling and evaluating the seismic behavior of an existing damaged fixed offshore platform in the Persian Gulf with consideration of actual structural damages as per provided subsea inspection reports and comparing with the intact condition of the platform to obtain the effect of assessment initiators like; actual damages and increased spectral acceleration as per API2EQ 2014 in the structural integrity of the fixed offshore platforms under the seismic loads in the Persian gulf.Following the actual jacket inspection reports, Excessive corrosion, flooding of some members, marine growth, and anode wastage are the significant damages on this platform.Spectral nonlinear and static-dynamic analysis with SACS12.00 software considering the pile-soil interaction in the three following scenarios has been performed to verify structural seismic assessment.The first scenario contains a damaged platform with a lighter topside, the second scenario is a damaged platform with a heavier topside, and the third one includes the intact platform with initial design assumptions and criteria.The evaluation of the structure in three parts of the jacket members, joints, and piles has been done under the ALE & ELE earthquake levels.According to the results, jacket legs have a significant effect on the structural seismic strength.In the abnormal level earthquake, the first plasticization occurs in the deck legs which are connected to the topside and the piles below the seabed.The comparison of the RSR values indicates that the initial assumption in platform design criteria has been stringent and uneconomical in the past.Also, the actual presented damages do not have much effect on the seismic strength of the structure.A Comparison of the Joint and member capacity illustrates a more significant impact of uniform corrosion on joint capacity than member strength.Finally; buckling in the deck legs at the splash zone and yielding in the Piles near the sea bed causes the global collapse of the structure.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".