Seismic vulnerability of offshore wind turbines to pulse and non‐pulse records
Bibliographic record
Abstract
Summary The increasing number of wind turbines in active tectonic regions has attracted scientific interest to evaluate the seismic vulnerability of offshore wind turbines (OWTs). This study aims at assessing the deformation and collapse susceptibility of 2MW and 5MW OWTs subjected to shallow‐crustal pulse‐like ground motions, which has not been particularly addressed to date. A cloud‐based fragility assessment is performed to quantify the seismic response for a given intensity measure and to assess the failure probabilities for pulse‐like and non‐pulse‐like ground motions. The first‐mode spectral acceleration Sa(T1) is found to be an efficient response predictor for OWTs, exhibiting prominent higher‐mode behavior, at the serviceability and ultimate conditions. Regardless of earthquake type, it is shown that records with strong vertical components may induce nonlinearity in the supporting tower, leading to potential failure by buckling in three different patterns: (i) at tower base near platform level, (ii) close to tower top, and (iii) between the upper half of the main tower and its top. Type and extent of the damage are related to the coupled excitation of vertical and lateral higher modes, for which tower top acceleration response spectra Sa,i(Top) is an effective identifier. It is also observed that tower's slenderness ratio (l/d), the diameter‐to‐thickness ratio (d/t), and the rotor‐nacelle‐assembly mass (mRNA) are precursors for evaluating the damage mode and vulnerability of OWTs under both pulse‐like and non‐pulse‐like ground motion records.
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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) | 0.000 | 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".