A critical review on the tsunami-induced scour around structures
Bibliographic record
Abstract
Recent catastrophic tsunamis, such as the 2004 Indian Ocean and 2011 Tohoku events, have underscored the need for resilient coastal infrastructure. Field investigations in the aftermath of these tsunamis have consistently identified local scour around structures as a critical factor contributing to their failure. Local scour refers to the erosion or removal of sediment in the vicinity of a structure, triggered by the unsteady water flow associated with tsunami inundation. The recognition of the significance of this phenomenon, juxtaposed with the current deficiency of detailed and systematic design guidelines that incorporate the ramification of local scour, has spurred advancements in this area of research. This study has meticulously reviewed state-of-the-art studies on tsunami-induced local scour around structures. The existing design guidelines have been critically assessed, pinpointing their limitations and areas that require further elucidation. Moreover, this critical analysis delineates the current knowledge gaps and outlines prospective directions for future research endeavors aimed at enhancing the resilience of coastal infrastructure, with a particular focus on mitigating the effects of local scour. This comprehensive review aims to deepen the understanding of tsunami-induced local scour and promote more effective, evidence-based design strategies to mitigate the risk of structural failure in high-risk coastal areas.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".