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Record W2482968112 · doi:10.5539/mas.v10n12p75

Amending and Changing the Seismic Behavior of K-bracing by Yielding Damped Braced Frame (YDBF)

2016· article· en· W2482968112 on OpenAlexvenueno aff
Vajdian Mehdi, Ali Parvari, Alireza Habibi

Bibliographic record

VenueModern Applied Science · 2016
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsnot available
FundersIslamic Azad University
KeywordsBracingDamperStructural engineeringBraced frameDuctility (Earth science)Frame (networking)Earthquake resistanceReduction (mathematics)Finite element methodComputer scienceSeismic energyBraceMaterials scienceEngineeringComposite materialMathematics

Abstract

fetched live from OpenAlex

An inactive control method is to prepare the kinds of dampers as inactive energy wasting factor for metallic structure buildings. Yielding metallic dampers (or excurrent) are metallic devices which can waste energy in an earthquake by the effect of non-elastic changes of metals, also in the braced systems of structure buildings, they can improve resistance against earthquake and their damages control potential significantly. With regard to the geometry of K-bracing and the weaknesses which are by the effect of a resultant of two compressive and tensile forces of Bersnon, Iran's earthquake regulation in 2008 has exerted some limitations for using of this system. Therefore, in this research we tried to eliminate these limitations to some extent by using of metallic dampers. For doing this research, by using of two software of finite components (sap2000, Abaqus) with static non-linear analysis, we achieved the same purpose. This kind of dampers due to the simplicity of their installation and low cost of them can be applied both in new and existing buildings.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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)0.0030.000

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 head0.212
Teacher spread0.203 · 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 designBench or experimental
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
Published2016
Admission routes1
Has abstractyes

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