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Record W2937079130 · doi:10.29173/mocs56

Testing and numerical analysis on cold-formed steel shear walls using corrugated steel sheathing

2017· article· en· W2937079130 on OpenAlexvenueno aff
Wenying Zhang, Yuanqi Li, Cheng Yu

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2017
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsnot available
FundersDivision of Civil, Mechanical and Manufacturing InnovationNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsStructural engineeringShear wallCold-formed steelShear (geology)Parametric statisticsStiffnessFinite element methodSteel plate shear wallMaterials scienceEngineeringGeotechnical engineeringComposite materialMathematics

Abstract

fetched live from OpenAlex

Cold-formed steel framed shear wall sheathed with corrugated steel sheets is a promising shear wall system for low- and mid-rise constructions at high wind and seismic zones due to its advantages of non-combustibility, high shear strength, and high shear stiffness. Monotonic and cyclic tests on full-scale wall assemblies using corrugated steel sheathing was conducted. To investigate the effect of vertical/gravity loading, shear wall specimens were tested under two different loading conditions: lateral loading, and a combined lateral and vertical/gravity loading. The test results are presented and discussed in this paper. Besides, finite element model of the proposed shear wall was created in Abaqus software. The validity of the numerical model was verified based on the test results. A series of parametric analysis were conducted, including the thickness of framing members, the cross section of stud members, yield strength of the frame members, stud spacing, and the influence of gravity loads. The detailed modeling information, relevant parametric analysis and recommendations for practical application of this type of shear resisting system are also presented.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.749
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
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.019
GPT teacher head0.231
Teacher spread0.212 · 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 designObservational
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

Citations4
Published2017
Admission routes1
Has abstractyes

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