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Record W4399756468 · doi:10.1177/87552930241246016

Response spectrum and modal dynamic analyses of gravity dams using ground motion accelerations modified to account for hydrodynamic effects

2024· article· en· W4399756468 on OpenAlexafffund
Ramtin Kouhdasti, Najib Bouaanani

Bibliographic record

VenueEarthquake Spectra · 2024
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGround motionModalResponse spectrumGeologySpectrum (functional analysis)PhysicsGeodesyStructural engineeringEngineeringSeismologyMaterials science

Abstract

fetched live from OpenAlex

This research proposes simplified methods for response spectrum and modal dynamic analyses of gravity dams using time‐history and spectral seismic accelerations modified to directly account for hydrodynamic effects. The developed methods waive the need for specialized fluid‐structure interaction software. They also lead to more accurate assessment of coupled structural flexibility and hydrodynamic effects due to each vibration mode than the static correction method commonly used in traditional simplified methods. The methodology utilizes analytical formulations of hydrodynamic pressure or simplified added masses to obtain the contributions of the selected vibration modes to the total seismic response. A hydrodynamic modification factor characterizing the amplification/de‐amplification of acceleration seismic demands due to earthquake‐induced hydrodynamic effects is also introduced. The application of the proposed methods is illustrated numerically through examples of two typical gravity dam‐reservoir systems subjected to four earthquakes. The obtained results are in excellent agreement with the classical reference solutions. Time‐history and spectral seismic acceleration demands modified by hydrodynamic effects as well as selected key response indicators, such as relative displacements and stresses within the studied gravity dams, are discussed.

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: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.934

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 head0.020
GPT teacher head0.295
Teacher spread0.275 · 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 routes2
Has abstractyes

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