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Record W2133488171 · doi:10.1061/9780784413470.052

Performance Spectra-Based Methodology for the Implementation of Supplemental Dampers in Buildings Subjected to Earthquakes

2014· article· en· W2133488171 on OpenAlexaff
Jack Wen Wei Guo, Constantin Christopoulos

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDamperStructural engineeringFrame (networking)Reduction (mathematics)Nonlinear systemMoment (physics)Spectral lineComputer scienceBase (topology)Damping ratioDesign methodsEngineeringAcousticsMathematicsPhysicsVibrationMechanical engineering

Abstract

fetched live from OpenAlex

An overview of the performance spectra method is first provided. Using this method, a 3-story, special moment-resisting frame is then retrofitted with four different types of supplemental dampers to meet two different performance targets. The results of the nonlinear time-history analyses show that the trial damping designs obtained by the method were close to the initial performance targets without any design iterations. The performance spectra can underestimate the reduction in base shear due to higher mode effects. However, this can be corrected using the supplemental damping properties computed from the method. This design example demonstrates the effectiveness of the performance spectra procedure for producing trial performance-based designs using different types of supplemental damping devices.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.021
GPT teacher head0.292
Teacher spread0.272 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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