SCOUR AMPLIFICATION CAUSED BY STRUCTURE PROXIMITY IN EXTREME FLOWS
Bibliographic record
Abstract
Forensic engineering field surveys of recent tsunamis (Saatcioglu et al. 2005, Chock et al. 2013) highlighted the importance of scour-related damage to structures located in coastal communities. To date, only a limited number of studies have investigated the interaction of extreme hydrodynamic flows and groups of structures, and none have studied the scour around multiple structures interacting with each other. One field example discussed by Yeh et al. (2013) documented flow concentration in between two tsunami-resistant buildings, leading to a deep scour hole between them and infrastructure failures onshore of the gap between the two buildings. This field example shows that multiple buildings, often crammed, lead to complex flow-structure interactions, leading to flow and scour either amplification or reduction depending on the relative position of the buildings. Nouri et al. (2010) and Thomas et al. (2015) investigated the flow velocity amplification caused by structures proximity, which concentrated the flow onto a downstream monitored structure. Their results informed the ASCE7 Ch.6 “Tsunami Loads and Effect” standard on flow velocity amplification caused by nearby structures. However, in this standard, there is currently no link between flow velocity amplification factors and their effects on scour around structures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".