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Record W4214819249 · doi:10.5267/j.esm.2022.2.002

Comparison between structural configurations designed by steel shear wall, moment resistant frame and X shape bracing systems

2022· article· en· W4214819249 on OpenAlexvenueno aff
Mohammad Gholami, Mehrdad Dorj, Peyman Beiranvand, Pegah Jafari Haghighatpour, Aref Azamigila

Bibliographic record

VenueEngineering Solid Mechanics · 2022
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsShear wallStructural engineeringBracingStructural loadShear (geology)Structural systemStiffnessRoofDuctility (Earth science)Shear forceMaterials scienceGeotechnical engineeringGeologyEngineeringBraceComposite material

Abstract

fetched live from OpenAlex

Nowadays, in order to increase construction of tall structures, the importance of choosing optimum systems, with a huge energy absorption capacity against wind and earthquake loads, has been widely considered. Since four decades ago, steel shear walls had been used as a stiff and high performance lateral system. This study is about the effect of concrete filled steel tubes (CFT) columns as vertical boundary elements of steel shear wall on seismic behavior of steel structures. Due to do this, three 10-storey steel structures, with similar plans and lateral load career systems of steel shear wall, coinciding X-bracing, and moderate steel frame were analyzed by means of non-linear, time-history method through SAP2000 software, and the results of roof displacement of them were compared with each other. Also after validating a two-storey, single-span frame sample with steel shear walls and CFT columns, 3 single-storey structures were analyzed by means of hysteresis and pushover, through ABAQUS software. The results of this study showed that a shear wall system presents suitable stiffness, resistance and ductility in comparison with other lateral bearing systems.

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 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 score0.287
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.237
Teacher spread0.224 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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