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Record W4236550846 · doi:10.29173/mocs164

Design and Specification Compilation of a Modularized Prefabricated High-rise Steel Frame Structure with Inclined Braces Part II: Elastic-plastic analysis and Joint Design

2015· article· en· W4236550846 on OpenAlexvenueno aff
Xuechun Liu, Ailin Zhang, Chen Tian, Xun Zhang, Yongqiang Tan

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2015
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsStructural engineeringFlangeJoint (building)BraceFinite element methodColumn (typography)Steel frameHigh riseEngineeringBeam (structure)Shear forceConnection (principal bundle)

Abstract

fetched live from OpenAlex

Modularized prefabricated steel structure has some obvious advantages, such as fast construction, industrial-scale production and environment-friendliness. Although it has been used for low-rise buildings, its applications in high-rise buildings are quite less. The elastic-plastic time-history analysis under rare earthquake conditions is performed on a 30-floor building. The changing law of the base shear force, the story drift angle, the stress, the damage characteristics, etc. are studied. According to the theoretical analysis, the finite element simulation and the model test, the design methods and the relevant formulas regarding the elastic and elastic-plastic properties of the beam-column connection joints, the column flange joints and the inclined brace joints are proposed in this paper. The control parameters for the structural design are also discussed. This paper provides an important reference for the research and design of the same type of modularized prefabricated high-rise steel structures, and the design method has been compiled into design specification.

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 score0.383
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 studies0.0000.000
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 head0.017
GPT teacher head0.189
Teacher spread0.172 · 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
Published2015
Admission routes1
Has abstractyes

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