Large‐Scale Physical Modeling of Wave Generation and Runup on Slopes From the Collapse of Partially and Fully Submerged Granular Columns
Bibliographic record
Abstract
Abstract Climate change is making coastal regions increasingly vulnerable to hazards, including rapid subaerial and submarine landslides, which can result in catastrophic tsunamis. Due to the complex geomechanics of failure, limited physical modeling studies have been conducted that encompass the entire problem including the triggering of granular landslides, the waves generated by partially and fully submerged mass failures, and the runup of these waves on local and distal slopes. In the present study, for the first time, waves in both the seaward direction (in the direction of failure for both submarine and partially submerged slides) and the landward direction (opposing the direction of failure only for submarine slides) are investigated during morphological evolution of the granular material. A series of large‐scale experiments are conducted under varying levels of submergence in water by releasing columns of gravel‐sized material into a range of different reservoir depths. This is accomplished using a pneumatically actuated vertical lift gate designed specifically for these experiments. The wave amplitudes measured in the seaward direction agree with empirical relationships developed in a previous study using smaller‐scale models, and a new analytical solution relating the column submergence to the trough‐led wave amplitudes in landward direction is presented. These novel predictions of wave amplitude and runup in both the seaward and landward directions indicate strong dependence of the wave behavior on the submergence depth and granular properties, improving our understanding of tsunamis generated by mass failures in coastal regions.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".