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Record W2955191606 · doi:10.29173/mocs142

Numerical Study of the Geometry Effect of Notched Connections in Mass Timber Panel-Concrete Composite Floors

2019· article· en· W2955191606 on OpenAlexaffvenue
Lei Zhang, Ying Hei Chui

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2019
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsStructural engineeringFinite element methodStiffnessInterlockingComposite numberBendingUltimate tensile strengthShear (geology)Beam (structure)Materials scienceEngineeringComposite material

Abstract

fetched live from OpenAlex

A mass timber panel-concrete (MTPC) composite floor system consists of a timber panel in the tensile zone, a concrete layer in the compression zone, and shear connectors between timber and concrete. The notched connections that are cut in timber and connected with concrete by the interlocking effect are often classified as the best type of connection system in terms of stiffness and load-carrying capacity. To study the effect of notch geometry to the performance of notched connections and composite beams, 2D finite element models are built in ABAQUS in this study. The concrete portion is modelled with concrete damaged plasticity model while the timber portion is modelled with Hashin’s failure criteria. The effective bending stiffness and ultimate bending capacity of the composite beam under uniformly distributed load are obtained from the finite element models and are compared with the well-known Gamma method in Eurocode 5 and strut-and-tie model. Good agreement between finite element model in the elastic range and strut-and-tie model was achieved. However, due to the assumptions made in the Gamma method, it was found that this simplified design method is not capable of describing MTPC composite floors with discrete notched connections.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score0.621

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 head0.006
GPT teacher head0.183
Teacher spread0.177 · 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 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

Citations1
Published2019
Admission routes2
Has abstractyes

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Same venueModular and Offsite Construction (MOC) Summit ProceedingsSame topicWood Treatment and PropertiesFrench-language works237,207