UNDERSTANDING TSUNAMI RISK TO STRUCTURES: A CANADIAN PERSPECTIVE
Bibliographic record
Abstract
The potential catastrophic effects of tsunami-induced loading on built infrastructure in the vicinity of shorelines have been brought to the fore by recent global events. However, state- of-the-art building codes remain silent or provide conflicting guidance on designing near- shoreline structures in tsunami-prone areas. This paper focuses on tsunami-induced loading and its effect on structures within the Canadian context. The mechanics of tsunami-induced loading is described based on knowledge gained during reconnaissance visits after the 2004 south-east Asia Tsunami, as well as post-construction visits to countries significantly affected by the destructive forces of the tsunami. To gain an appreciation of the magnitude of tsunami-induced bores for a given seismic event along the western coastal region of Canada, structural analysis of a simple near-shoreline structure was performed considering a proposed loading protocol for tsunami-induced hydraulic bores. These loads were further compared to seismic loading in order to provide an estimation of the tsunami risk and its impact. The work was complemented by experimental results from a large-scale testing program conducted with the purpose of estimating the forces experienced on structural components. Square-, rectangular-, and diamond-shaped columns were used to study the influence of shape. Furthermore, results from debris impact testing are also discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".