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Record W2914705512 · doi:10.1193/101117eqs201m

Effect of Ground Motion Duration on Reinforced Concrete Shear Wall Buildings

2019· article· en· W2914705512 on OpenAlexafffund
Michael Fairhurst, Armin Bebamzadeh, Carlos E. Ventura

Bibliographic record

VenueEarthquake Spectra · 2019
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaFederal Emergency Management Agency
KeywordsGround motionDuration (music)Structural engineeringReinforced concreteGeotechnical engineeringScalingShear (geology)GeologyEngineeringMathematicsPhysicsGeometry

Abstract

fetched live from OpenAlex

This paper investigates the effect of earthquake ground motion duration on the design and collapse risk of reinforced concrete shear wall buildings from 6 to 30 stories. Both design and collapse levels of shaking were considered through nonlinear incremental dynamic analysis. At the design level of shaking, it was found that (1) maximum interstory drifts were increased as ground motion duration increased (from <20 s to 35–150 s of strong shaking), though not enough to flag the designs as unacceptable; (2) story forces and moments were not significantly affected; and (3) energy demands were greatly increased by motion duration. When the records were scaled until collapse level, it was found that (1) the median collapse scaling level was greatly impacted by duration, and the median collapse shaking level was almost 20% higher on average when considering shorter records; and (2) long duration records produced both larger probabilities of collapse at the design shaking level and lower collapse margin ratios compared with spectrally equivalent short duration records.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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)0.0010.000

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 head0.004
GPT teacher head0.197
Teacher spread0.193 · 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 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

Citations50
Published2019
Admission routes2
Has abstractyes

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