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Record W2108036432 · doi:10.1139/l09-056

Simplified analysis of a low-rise building seismically isolated with stable unbonded fiber reinforced elastomeric isolators

2009· article· en· W2108036432 on OpenAlexafffundvenue
Hamid Toopchi‐Nezhad, Michael J. Tait, Robert G. Drysdale

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaMcMaster University
KeywordsStructural engineeringIsolatorHysteresisEarthquake shaking tableDisplacement (psychology)Nonlinear systemShear (geology)Materials scienceBearing (navigation)Compression (physics)Base isolationSeismic loadingComputer scienceEngineeringReduction (mathematics)MathematicsComposite materialPhysicsGeometry

Abstract

fetched live from OpenAlex

The seismic response of an ordinary low-rise base isolated (BI) structure, employing stable unbonded-fiber reinforced elastomeric isolator (SU-FREI) bearings, is predicted by using two different simplified analytical models. Subsequently, the accuracy of the two models is evaluated by using measured test results from a shake table study. Two models simulate the nonlinear experimental lateral load–displacement hysteresis loops of these bearings. The experimental hysteresis loops were obtained from cyclic shear tests on prototype bearings under a constant compression load. Because of the nonlinear lateral response behavior of the SU-FREIs, these models are employed in an iterative time-history analysis approach, enabling the model variables and the calculated peak lateral displacement of the bearings to converge to their unique values. Analysis results show that the presented simplified models may be used effectively in seismic response prediction of ordinary low-rise buildings that are seismically isolated by SU-FREI bearings.

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 categoriesMeta-epidemiology (narrow)
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 score0.089
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
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 head0.003
GPT teacher head0.172
Teacher spread0.169 · 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.

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

Citations30
Published2009
Admission routes3
Has abstractyes

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