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Record W2327156739 · doi:10.1061/41165(397)326

Seismic Deformation Analysis for Risk Assessment of Embankment Dams

2011· article· en· W2327156739 on OpenAlexaboutno aff
Vlad Perlea, David C. Serafini, Said Salah-Mars, Faiz I. Makdisi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsnot available
Fundersnot available
KeywordsLeveeGeologyGeotechnical engineeringLiquefactionSettlement (finance)CrestEmbankment damFoundation (evidence)AlluviumErosionSeismologyGeomorphology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.772
Threshold uncertainty score0.243

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.235
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

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