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Record W4409740938 · doi:10.1139/cjce-2024-0278

Shaking table tests and numerical analysis on steel frame structure with various brace arrangements

2025· article· en· W4409740938 on OpenAlexvenueno aff
Qiujun Ning, Xiaosong Lu, Zihua Zhang, Jiawei Lu

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsBraceStructural engineeringSteel frameEarthquake shaking tableFrame (networking)EngineeringTable (database)Forensic engineeringComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

The steel frame-bracing system can increase the overall stiffness and lateral stiffness of the frame, significantly improving the seismic capacity of the structure. In this paper, shaking table tests were conducted on two 1/8 scaled steel frame structures, including a one-way brace arrangement scaled structure and a two-way brace arrangement scaled structure. A finite element (FE) model of the steel frame structure was established in Ansys, and its validity was verified against experimental results. Compared with experiments, an extended parameter analysis of the variable brace arrangement schemes of a multi-story space steel frame structure was conducted using the proposed FE model. The experimental and simulation results showed that the two-way brace arrangement in a steel frame structure significantly enhances the overall stiffness of the structure. The seismic performance of the brace arranged in a steel frame structure in the reverse wave direction is better than that of the brace arranged along the wave direction.

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 categoriesnone
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 score0.472
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.003
GPT teacher head0.181
Teacher spread0.178 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2025
Admission routes1
Has abstractyes

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