EXTREME WAVE PRESSURES AND LOADS ON A PILE-SUPPORTED WHARF DECK - INFLUENCES OF AIR GAP AND WAVE DIRECTION
Bibliographic record
Abstract
Many deck-on-pile structures are located in shallow water depths at elevations low enough to be inundated by large waves during intense storms or tsunami. Many researchers have studied wave-in-deck loads over the past decade using a variety of theoretical, experimental, and numerical methods. Wave-in-deck loads on various pile supported coastal structures such as jetties, piers, wharves and bridges have been studied by Tirindelli et al. (2003), Cuomo et al. (2007, 2009), Murali et al. (2009), and Meng et al. (2010). All these authors analyzed data from scale model tests to investigate the pressures and loads on beam and deck elements subject to wave impact under various conditions. Wavein- deck loads on fixed offshore structures have been studied by Murray et al. (1997), Finnigan et al. (1997), Bea et al. (1999, 2001), Baarholm et al. (2004, 2009), and Raaij et al. (2007). These authors have studied both simplified and realistic deck structures using a mixture of theoretical analysis and model tests. Other researchers, including Kendon et al. (2010), Schellin et al. (2009), Lande et al. (2011) and Wemmenhove et al. (2011) have demonstrated that various CFD methods can be used to simulate the interaction of extreme waves with both simple and more realistic deck structures, and predict wave-in-deck pressures and loads.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".