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Record W2186881942

Comparison of Dual System of Steel Moment frame and Thin steel Plate shear walls with Dual system of Steel moment frame and cross Bracing or Chevron with a Design Method based on Performance levels

2013· article· en· W2186881942 on OpenAlexaboutno aff
Yousef Zandi, Tabriz Branch

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsStructural engineeringMoment (physics)BracingShear wallFrame (networking)Steel frameStructural systemShear (geology)Dual (grammatical number)EngineeringMaterials scienceMechanical engineeringPhysicsBraceComposite materialClassical mechanics
DOInot available

Abstract

fetched live from OpenAlex

Various systems are applied to counteract lateral forces and especially the earthquake force, including: dual systems that include combined system of moment frame and other resistant systems. In 2800 code system, dual systems of steel moment frame include moment frame and the variety of braces and steel shear wall 4 is not mentioned. Only in the code system of the law of Canada (CAS, 1994) a part is explicitly assigned to this element bearing. Recently, among the methods for designing structures against earthquakes, method of design based on performance levels is taken into consideration, due to taking the inelastic behaviour of structures in the instruction of FEMA274, FEMA273, and ATC40 into account. In this paper, dual systems including intermediate moment frames and thin steel moment frame and intermediate moment frame and convergent braces are used in different frames and performance point of them is obtained using Capacity spectrum method and is compared to each other. Also coefficient of thin steel shear walls and the rate of energy loss of the dual systems are investigated. Soft-waresSAP2000 and ANSYS are used for modeling and analysis.

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)0.001
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 score0.177
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
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 head0.028
GPT teacher head0.269
Teacher spread0.241 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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