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Record W4392683554 · doi:10.1139/cgj-2022-0608

Flow failure assessment for dams and embankments

2024· article· en· W4392683554 on OpenAlexvenueno aff
Timothy D. Stark, Hyunil Jung, Jiale Lin

Bibliographic record

VenueCanadian Geotechnical Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringFlow (mathematics)GeologyEngineeringForensic engineeringEnvironmental scienceMechanics

Abstract

fetched live from OpenAlex

A procedure is proposed to assess whether a liquefied strength should be applied to a zone of non-plastic silt, silty sand, and (or) clean sand in a static or seismic stability analysis to assess the flow failure potential of dams and slopes. The procedure consists of the following five main steps to assess flow failure potential: (1) assess static liquefaction potential of segments along a potential failure surface; (2) assess seismic liquefaction potential by calculating the factor of safety against liquefaction (FoS Liquefaction ) for any amplitude of shaking in each segment; (3) if liquefaction is not triggered in any of these segments, assess the magnitude of shear-induced pore-water pressures due to seismic or vibratory events of any amplitude; (4) assign a liquefied strength to segment(s) that experience seismic liquefaction, i.e., FoS Liquefaction < 1 or significant pore-water pressure generation, i.e., total pore-water pressure ratio ≥ 0.7; and (5) conduct a post-triggering stability analysis to assess flow failure potential. This procedure is illustrated using the 1971 seismic permanent deformations of Upper San Fernando Dam and 2015 Fundão tailings dam failure.

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 score0.970
Threshold uncertainty score0.447

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 head0.006
GPT teacher head0.230
Teacher spread0.224 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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