Displacement Hazard Analysis of Earth Structures Affected by Subduction Zone and Shallow Crustal Earthquakes
Bibliographic record
Abstract
Common practice for evaluating the seismic performance of earth dams uses design ground motions selected to be consistent with a target design response spectrum, which are subsequently used in dynamic analyses that estimate seismically induced displacements as an index of performance. This approach involves selecting the probability of exceedance of a ground-motion parameter rather than the probability of exceeding the seismically induced displacements. This implicitly assumes that the different response spectra levels (e.g., different SA hazard levels or 50th percentile versus 84th percentile ground motions) is correlated to the different levels of seismically induced displacement. This may not always be the case and can be particularly problematic when evaluating earth structures located in a tectonic setting with multiple source types (e.g., subduction and shallow crustal earthquakes). This can lead to high variability in the estimated displacements, making the selection of a representative overall displacement computed from the different source types not immediately clear. In this study, we propose simplified approaches to select representative displacements for dams affected by earthquakes from multiple source types and we evaluate their performance by constructing displacement hazard curves that rely on the conditional scenario spectra (CSS) framework. We illustrate the application of the proposed procedures for a fictitious dam located in Vancouver, British Columbia, and offer recommendations for using the proposed procedures in practice. Finally, we propose a new procedure for selecting a subset of ground motions for use in complex dynamic analyses [e.g., FEM or finite-difference methods (FDM)] based on the deaggregation from displacement hazard curves rather than the deaggregation from elastic response spectra hazard curves.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".