Effect of Lateral Spacing of Structures on Tsunami-Induced Scour
Bibliographic record
Abstract
April Le Quéré, P.; Nistor, I., and Mohammadian, M., 2021. Effect of lateral spacing of structures on tsunami-induced scour. Journal of Coastal Research, 37(4), 813–826. Coconut Creek (Florida), ISSN 0749-0208.Tsunamis are a devastating force, as seen by the extreme tsunamis that struck the Indian and Pacific Oceans over the past two decades: the 2004 Indian Ocean Tsunami, the 2010 Chile Tsunami, and the 2011 Tohoku Japan Tsunami. In those events, flood-induced scouring was found to be an important phenomenon that often led to infrastructure damage. Past research has focused on scouring around single columns and other isolated horizontal structures to attempt to understand the complex processes characterizing this phenomenon. However, given that coastal cities often comprise complex arrangements of multiple structures, often crammed, tsunami inundation flow often finds concentrated paths in between such structures; these flow concentrations increase the resulting scouring. This research is an attempt to inform the scientific community on the potential of augmented scour attributable to flow concentration that is due to the lateral spacing of structures and to compare it with nonconcentrated flow-induced scour. The authors employed the FLOW-3D numerical model for this study. This model was first calibrated using a physical model experiment performed in the dam break flume of the University of Ottawa, Canada. Results from this study demonstrate that flow concentration due to the lateral spacing of structures leads to greater scouring than for the case of an isolated structure and that the magnitude of the scour, for most cases studied, was larger than what is prescribed by the latest building codes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".