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Record W2955916540 · doi:10.29173/mocs122

Numerical Modelling Analysis of Angle Bracket Connections Used in Cross Laminated Timber Constructions

2019· article· en· W2955916540 on OpenAlexaffvenue
Saeed Rezvani, Lina Zhou

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2019
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsBracketCross laminated timberFastenerStructural engineeringFinite element methodDissipative systemPhase angle (astronomy)Computer scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

Connections are arguably one of the most critical components controlling the structural performance and failure modes of mass timber structures. Over the last two decades, demands for stronger and energy dissipative connections have been raised with increased application of mass timber products in larger and taller buildings. This paper presents numerical analyses of novel mass timber connections used in cross laminated timber structures. The connections are developed by MyTiCon with BB Stanz- und Umformtechnik GmbH angle bracket. Despite being relatively thin, these angle brackets could show comparable load resistance with thicker ones due to the reinforced web and folded edges. The commercially available finite element software ABAQUS was used to develop three dimensional (3D) numerical models to simulate the performance of angle bracket connections under different load combinations. The modelling analysis involves two phases: (1) to determine the most efficient fastener type and setup for the angle brackets connected to CLT wall and floor panels, and (2) to evaluate the capacity of angle brackets connected to CLT wall and floor panels in various loading scenarios. The findings of this study provide an insight into the behaviour of this new angle bracket connections and will be used in the design of the experimental tests in the next phase.

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 score0.711
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.010
GPT teacher head0.206
Teacher spread0.196 · 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
Published2019
Admission routes2
Has abstractyes

Explore more

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