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Record W2136428486 · doi:10.1002/tal.1107

Finite element modeling and parametric analysis of timber–steel hybrid structures

2013· article· en· W2136428486 on OpenAlex
Zheng Li, Minjuan He, Frank Lam, Minghao Li, Renle Ma, Zhong Ma

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Structural Design of Tall and Special Buildings · 2013
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInfillStructural engineeringFinite element methodShear wallParametric statisticsStiffnessShear (geology)EngineeringMaterials scienceComposite materialMathematics

Abstract

fetched live from OpenAlex

SUMMARY This paper presents finite element modeling and a parametric analysis of prototype timber–steel hybrid structures, which are composed of steel moment‐resisting frames and infill wood‐frame shear walls. A user‐defined element was developed to model the behavior of the infill wood shear walls based on the concept of pseudo‐nail model. The element was implemented as a subroutine in a finite element software package abaqus . The model was verified by reversed cyclic test results and further used in a parametric analysis to investigate the lateral performance of timber–steel hybrid shear walls with various structural configurations. The results showed that the infill wall was quite effective within small drift ratios, and the elastic lateral stiffness of the hybrid shear wall increased when a stronger infill wall was used. In order to ensure the structural efficiency of the hybrid shear wall system, it is beneficial to use relatively strong timber–steel bolted connections to make sure the shear force can be transferred effectively between the steel frame and the infill wall. Copyright © 2013 John Wiley & Sons, Ltd.

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.

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 score0.010
Threshold uncertainty score0.404

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.016
GPT teacher head0.203
Teacher spread0.187 · 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