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Hysteretic performance of angle steel connections in a timber-concrete composite system

2022· article· en· W4213361676 on OpenAlexaff
Lan Xie, Guojing He, Xiaodong Wang, Xiao Wen Tang, Roberto Crocetti

Bibliographic record

VenueBioResources · 2022
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversité Laval
FundersNational Natural Science Foundation of ChinaEducation Department of Hunan Province
KeywordsDissipationComposite numberDuctility (Earth science)Structural engineeringStiffnessSlabMaterials scienceStaticsYield (engineering)Composite materialEngineering

Abstract

fetched live from OpenAlex

Timber–concrete composite systems are widely used in Europe, North America, and Australasia, primarily due to their good mechanical performance in terms of statics, dynamics, and seismic response. In addition, the concrete slab provides excellent protection to the timber, making such systems suitable for outdoor application. The seismic performance of timber–concrete composites is normally governed by their ductility and energy dissipation capacity. However, few design codes address the ductility and energy dissipation capacity of timber–concrete composite systems, owing to a lack of reliable performance data. Therefore, further research on the hysteretic performance of timber–concrete composite systems is necessary. In this study, six timber–concrete composite specimens with an angle steel connection of the same size were investigated using reversed cyclic tests. The corresponding failure modes were observed, and the salient features of the connection, i.e., the stiffness, ductility, and energy dissipation, were computed from the test results. The force mechanism of the timber–concrete composite specimens under reversed cyclic load was analyzed. Equations were presented to calculate the yield force and negative force in the same load step. A comparison of the test results and the theoretical results indicated good agreement.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.464
Threshold uncertainty score0.311

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 head0.009
GPT teacher head0.168
Teacher spread0.159 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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