MétaCan
Menu
Back to cohort
Record W2956122367 · doi:10.29173/mocs127

Effect of Loading Speed on the Mechanical Properties of Nail Joints

2019· article· en· W2956122367 on OpenAlexaffvenue
Shervin Shameli Derakshan, Lina Zhou, Chun Ni

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2019
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsFPInnovationsUniversity of Victoria
Fundersnot available
KeywordsNail (fastener)Materials scienceStructural engineeringShear (geology)Composite materialEngineering

Abstract

fetched live from OpenAlex

Wood shear walls are the main lateral load resisting systems in light wood frame buildings to resist the wind and seismic loads. Sheathing to lumber nail connections are regarded as the key components that control the resistance and failure modes of wood shear walls. Considerable experimental tests have been conducted on performance of nail joints or wood shear walls under both static and reversed cyclic loads. However, these tests were usually conducted under different loading speed causing specimen failure in 1 min to an hour. It is unclear how the loading speed will affect the test results of nail joints or wood shear walls. Research on these topics is limited. This paper aims to evaluate the effect of loading speed on the mechanical characteristics of nail joints. 72 specimens have been tested under various loading speeds, ranging from 0.05 mm/s to 0.5 mm/s for monotonic tests and 1.5 mm/s to 15 mm/s for reversed cyclic tests. The range of loading speed was selected based on the total estimated loading time that both monotonic and reversed cyclic tests fail within around 1 min to 10 min. Two groups of nail joints, comprising two common nail sizes and two sheathing thicknesses, were assessed under both monotonic and reversed cyclic loads. From the test results, the effect of loading speed on the mechanical properties of nail joints was determined.

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 score0.045
Threshold uncertainty score0.513

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 head0.008
GPT teacher head0.173
Teacher spread0.165 · 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

Citations0
Published2019
Admission routes2
Has abstractyes

Explore more

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