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Record W2804331610 · doi:10.1193/050318eqs108m

Seismic Safety Assessment of Base‐Isolated Buildings Using Lead‐Rubber Bearings

2019· article· en· W2804331610 on OpenAlexaff
T.Y. Yang

Bibliographic record

VenueEarthquake Spectra · 2019
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsStructural engineeringShear (geology)Natural rubberBase isolationEngineeringFinite element methodSafety factorDisplacement (psychology)Amplification factorSeismic loadingCoupling (piping)BucklingGeotechnical engineeringBase (topology)Nonlinear systemMaterials scienceComposite materialMathematicsMechanical engineering

Abstract

fetched live from OpenAlex

Lead‐rubber bearing (LRB) is a well developed and implemented isolation technology. One of the design challenges is to prevent LRBs from buckling during strong earthquake shaking. Although detailed component behavior of LRB under combined axial and shear loads has been well investigated, the seismic performance of base‐isolated buildings with LRBs has not been systematically examined. In this study, the robust finite element model of the LRB, which accounts for the axial and shear coupling, has been used to examine the seismic performances of two prototype buildings, each with different LRB geometric properties, structural periods, and axial loads. The results of nonlinear dynamic analyses show that the axial and shear coupling response of the LRB play an important role in the safety of base‐isolated buildings. A simple amplification factor of 2.5 is proposed to increase the axial capacity of the LRB when the shear deformation reaches the maximum total displacement. The results show that such a simple amplification factor can produce low probability of failure of LRB buildings during strong earthquake shaking.

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 score0.163
Threshold uncertainty score0.926

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)0.0010.000

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 head0.008
GPT teacher head0.228
Teacher spread0.219 · 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

Citations12
Published2019
Admission routes1
Has abstractyes

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