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Mechanical properties of glulam moment-resisting joints reinforced by inclined self-tapping screws

2024· article· en· W4392802297 on OpenAlexaff
Shuo Wang, Panpan Ma, Jingkang Lin, Tongyu Hou, Feibin Wang, Zeli Que, Meng Gong

Bibliographic record

VenueBioResources · 2024
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversity of New Brunswick
FundersGovernment of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsTappingMaterials scienceComposite materialStructural engineeringMoment (physics)EngineeringMechanical engineeringPhysicsClassical mechanics

Abstract

fetched live from OpenAlex

Self-tapping screws (STS) are an effective fastener to enhance wooden moment-resisting joints. However, the effects of the arrangement and insertion angle of STS on the mechanical properties of wooden joints are less studied. Therefore, this study investigated the influence of these two factors on the mechanical properties of wooden joints by conducting cyclic loading tests using glulam moment-resisting joints reinforced by STS with different arrangements (round and square) and insertion angles (45° and 90°). The failure modes, bearing performances, and energy dissipation capacities were considered. The results showed that the insertion angle affected the bearing and energy dissipation capacity of the joints significantly, while the effect of arrangement was slight. The anti-rotation bending moments of the joints reinforced by inclined STS were higher by 31.7% and 13.5% when the arrangement of STS was circular and rectangular respectively compared with the joints reinforced by vertical STS under compression state, and were lower by 17.5 % and 22.9 % under tensile state. The restoring force characteristics of the joints were similar when the insertion angle of STS was the same. Furthermore, the joints had optimal ductility and stiffness when the arrangement was rectangular, and the insertion angle was 45°.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.743

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 head0.016
GPT teacher head0.231
Teacher spread0.215 · 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 designBench or experimental
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
Published2024
Admission routes1
Has abstractyes

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