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Record W4225162785 · doi:10.11159/icgre22.226

Seismic Slope Stability and Liquefaction Potential of Large Existing Local Dams

2022· article· en· W4225162785 on OpenAlexvenueno aff
Nguyen Hong Nam, Abid AbuTair

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsLiquefactionStability (learning theory)GeologyGeotechnical engineeringSlope stabilitySeismologyComputer science

Abstract

fetched live from OpenAlex

In order to increase the local material dam capacity to sustain from the big floods as the effect of climate change recently, the existing dams in Vietnam are frequently considered the two remedial design proposals either increasing the spillway discharge capacity or raising the dam height to increase the storage volume of the reservoir during the flood season.In addition, regarding the dam constructed on the earthquake prone area, the seismic slope stability and liquefaction potential caused by strong earthquake need to be carefully examined.Numerical study was implemented on the Phu Vinh large dam based on the site geotechnical data with many scenario loading cases.The analysis results showed that the static slope stability factors of safety could be satisfied by the existing standards; however, seismic factors of safety decreased significantly under unity with three cases of earthquake (T=475 years, 145 years and 10,000 years).In addition, liquefaction potential could be possible at the sandy layer under downstream rockfill.The safety measures need to be implemented in the design and construction of the existing and /or the new large dams.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.184
Teacher spread0.179 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2022
Admission routes1
Has abstractyes

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