Seismic Deformation Analysis for Risk Assessment of Embankment Dams
Bibliographic record
Abstract
Several failure modes, usually included in the risk analysis of an embankment dam, are related to seismic loading and include: overtopping due to embankment settlement, above crest erosion, seepage erosion through transverse cracks, and piping into a rupture of the outlet works system. All these failure modes are aggravated when the foundation soil is potentially liquefiable under possible earthquake loading. Success Dam and the Auxiliary Dam of the Isabella Lake, both in California have recently been evaluated for seismic loading and seismic risk. The dams are founded on liquefiable alluvium deposits and in one case the site has a seismically active fault that transects the dam. Seismic deformation analyses for the risk assessment were performed using the computer program FLAC and the liquefaction model UBCSAND (developed at the University of British Columbia, Canada and modified for better modeling of the liquefiable dam foundations by Dr. Michael Beaty). Correlation relationships were determined between the intensity of shaking (defined by the peak ground acceleration) and embankment deformations, in particular the crest settlement and the horizontal displacement of the slopes. The results were presented in a format adequate for easy implementation into the risk evaluation model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.002 | 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".