Wave-in-Deck Loads for an Intricate Pile-Supported Pier and Variation With Deck Clearance
Bibliographic record
Abstract
Many deck structures are located at elevations low enough to be impacted by large waves. However, due to the highly complex and impulsive nature of the interactions between wave crests and intricate deck structures, establishing reliable estimates of extreme pressures and forces for use in design remains challenging. In this paper, results from an extensive set of three-dimensional scale model tests conducted to support the design of a large pile-supported pier (or jetty) are presented and discussed. Relationships between maximum wave-in-deck loads and the deck clearance (air gap) are presented and discussed. Results from numerical simulations of the wave-structure interaction process obtained using the three-dimensional CFD software FLOW-3D® are also presented and discussed. Finally, some initial comparisons between the numerical and physical modelling are also included. This paper provides new insights concerning the character and magnitude of the hydrodynamic pressures and loads exerted on intricate pile-supported deck structures due to impact by non-linear shallow-water waves, and the relationships between the hydrodynamic forcing and the deck clearance or air gap.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".