Seismic Vulnerability of a Tailings Dam Affected by Subduction Earthquakes
Bibliographic record
Abstract
The seismic response of tailings dams is highly dependent on the intensity measures (IMs) of the input ground motions.For this reason, several researchers have used seismic fragility functions to evaluate the seismic performance of geotechnical structures.Seismic stability analyses of tailings dams are further challenged by the uncertainty and variability of IMs for a given earthquake scenario and site conditions.This study presents the seismic performance of a tailings dam affected by subduction earthquakes by generating fragility functions and analysing the effectiveness of different IMs in predicting a damage measure (DM), such as horizontal displacements.Our analyses are based on finite-difference numerical simulations using advanced constitutive models.The selected ground motions are compatible with the Maximum Credible Earthquake (MCE), which is common in the practice in South America.The results show that the Arias intensity is the most efficient and optimal IM in predicting the horizontal crest displacements of the dam.Furthermore, the analytical fragility functions based on numerical results using peak ground acceleration (PGA), Arias intensity (AI), cumulative absolute velocity (CAV), and peak ground velocity (PGV) are presented.The fragility functions can be a useful tool to assess the probability of damage levels for designed tailings dams based on their design earthquake and acceptable risk.In addition, the obtained fragility functions could be used to define alert levels to be considered in the operation manual of the tailings storage facility (TSF).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".