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Glulam connections assembled with screws in different installation angles

2021· article· es· W3195104164 on OpenAlexaff
D.C. Hanna, Thomas Tannert

Bibliographic record

VenueMaderas Ciencia y tecnología · 2021
Typearticle
Languagees
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsStiffnessFastenerTappingStructural engineeringDuctility (Earth science)Joint (building)Deformation (meteorology)Materials scienceComposite materialEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Self-tapping screws are the recognized state-of-the-art in fastener technology for timber structures. Combining fasteners of different stiffness, such as self-tapping screws with different installation angles, can be advantageous to simultaneously achieve high connection stiffness and ductility. In this paper, experimental investigations on a total of 65 glued-laminated timber joints assembled with a variety of installation angles including several combinations of self-tapping screws acting axially in withdrawal with self-tapping screws acting laterally are presented. The connection performance was analyzed in terms of the load-carrying capacity, the deformation capacity, the stiffness, and the ductility. The findings demonstrated that joint assemblies with self-tapping screws loaded primarily laterally exhibit low stiffness but high ductility, whereas joint assemblies with self-tapping screws loaded primarily in withdrawal are very stiff but exhibit low ductility. Combining screws in different installation angles created glued-laminated timber connections that combine high stiffness with high ductility. Existing analytical expressions were deemed suitable to estimate load-carrying capacity through simple summation of the different screws' individual resistances.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.210
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.

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

Citations5
Published2021
Admission routes1
Has abstractyes

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