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Record W4380677024 · doi:10.1177/87552930231180225

Data set from shaking table tests of a slender MDOF structure with base‐rocking mechanisms

2023· article· en· W4380677024 on OpenAlexafffund
Chiyun Zhong, Constantin Christopoulos

Bibliographic record

VenueEarthquake Spectra · 2023
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsHudbay Minerals (Canada)University of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEarthquake shaking tableStructural engineeringDissipationTowerStiffnessBase (topology)Benchmark (surveying)EngineeringMathematicsGeologyMathematical analysisPhysics

Abstract

fetched live from OpenAlex

Numerical models of seismic‐resistant structures are typically validated by comparing numerically predicted key seismic response parameters to those obtained from benchmark shake table experiments. However, studies have shown that for structures rocking on rigid foundations, the seismic response can be unrepeatable in experiments and deterministically unpredictable by numerical models. Therefore, researchers have proposed to validate rigid rocking models using statistical approaches, yet no experimental study has examined whether the same observations apply to slender multi‐degree‐of‐freedom (MDOF) free‐rocking structures or controlled rocking structures with positive post‐rocking stiffness and dedicated energy dissipation. This data paper describes the now publicly available data obtained from a series of 254 shaking table tests of a slender MDOF tower structure with two base‐rocking mechanisms, which can be used to examine the predictability of the seismic response of an MDOF rocking structure and to statistically validate different approaches aiming to model tall and slender structures that utilize base‐rocking mechanisms to mitigate the dynamic response due to higher‐mode effects.

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 score0.181
Threshold uncertainty score0.661

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.023
GPT teacher head0.231
Teacher spread0.207 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

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