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Record W3135384754 · doi:10.2749/newyork.2019.1711

Multi-story truss moment frames equipped with friction dampers and self-centering system for enhanced seismic performance

2019· article· en· W3135384754 on OpenAlexaffabout
Keyhan Faraji, Robert Tremblay

Bibliographic record

VenueReport · 2019
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsTrussStructural engineeringDamperMoment (physics)DissipationChord (peer-to-peer)ResidualNonlinear systemStructural systemEngineeringComputer sciencePhysics

Abstract

fetched live from OpenAlex

<p>In this article, two new truss moment frame (TMF) systems exhibiting enhanced seismic performance are examined: truss moment frames with friction energy dissipation dampers between the truss bottom chord and the columns (F-TMFs) and F-TMFs with tendons added to achieve self-centering response (FT-TMFs). In both cases, all steel components of the systems are expected to behave essentially elastically to eliminate structural damage. The second system is also expected to have negligible residual lateral deformations. To compare and investigate the seismic performance of the proposed TMF systems, a 5-story commercial steel building located in Vancouver, BC, is designed in accordance with the National Building Code of Canada 2015 (NBCC) and it is subjected to a series of nonlinear static and dynamic time history analyses. The earthquake records, employed in non-linear time history analyses, are scaled for a hazard level corresponding to a probability of 2% in 50 years. The analytical results show that structural damage does not occur in neither of the two proposed systems . Meanwhile, FT-TMF system showed notably better seismic response and negligible residual deformations due to its self-centering capacity provided by the tendons.</p>

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 score0.433
Threshold uncertainty score0.540

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)0.0000.000

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 head0.007
GPT teacher head0.204
Teacher spread0.197 · 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

Citations0
Published2019
Admission routes2
Has abstractyes

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