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Record W4380450764 · doi:10.52202/069179-0215

NUMERICAL MODELLING OF CONTEMPORARY MASS TIMBER CONNECTIONS IN FIRE

2023· article· en· W4380450764 on OpenAlexaff
Bronwyn Chorlton, Mathieu Létourneau-Gagnon, Christian Dagenais, Marc-André Langevin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFire dynamics and safety research
Canadian institutionsFPInnovationsQueen's University
Fundersnot available
KeywordsResidualArchitectural engineeringThermal massCross laminated timberFire resistanceLimitingFastenerEngineeringComputer scienceStructural engineeringCivil engineeringThermalMechanical engineeringMaterials science

Abstract

fetched live from OpenAlex

Timber buildings are becoming increasingly popular due to their sustainability and aesthetic appeal. There remains however, a need to fully understand the fire performance of mass timber construction, including the fire performance of the connection. Beam-column connections can be complex to assess in fire, due to the proprietary and custom nature of many connection designs. The purpose of this study is to create a numerical model that can assess the residual fire resistance capacity of mass timber connections exposed to fire. This endeavour was completed using finite element modelling considering heat transfer of mass timber connections. Two approaches were taken, first, the heat flux through the fastener's shank establishes the reduction factors that predict the residual thermal capacity of fasteners, and the second establishes the residual length of penetration that provide adequate structural capacity into timber elements. These thermal analyses work towards developing a simplified design method for evaluating mass timber connections exposed up to two hours of standard fire exposures. By providing additional information related to the expected temperatures and strengths of timber connections in fire, novel designs become more accessible for innovative timber structures.

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 score0.423
Threshold uncertainty score0.185

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 head0.048
GPT teacher head0.259
Teacher spread0.210 · 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
Published2023
Admission routes1
Has abstractyes

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