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Record W2954912820 · doi:10.1139/cjce-2018-0498

Finite-element block shear failure deformation-to-fracture failure analysis

2019· article· en· W2954912820 on OpenAlexvenueno aff
Kazeem K. Adewole, Oladejo O. Joy

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceUltimate tensile strengthShear (geology)Deformation (meteorology)Composite materialFinite element methodStructural engineeringShear stressEngineering

Abstract

fetched live from OpenAlex

This paper presents the finite-element (FE) block shear failure (BSF) deformation-to-fracture analysis. FE analysis reveals the following: BSF begins with bolt – bolt hole contact point compressive yielding and not the tensile or shear yielding reported in the literature. BSF does not result from the combination of the gauge tensile plane tensile deformation and the shear plane pure shear deformation alone as reported in the literature and codes. BSF results from compressive deformation of the bolt – bolt hole contact points, tensile deformation of bolt hole portions not in contact with the bolts, gauge tensile plane and edge distance tensile plane deformations in combination with pure shear deformation and a combined shear and tensile bending deformation of the portions of the shear planes near to and remote from the bolt – bolt hole contact points, respectively. This study provides a better understanding of the BSF mechanism, BSF total load-bearing areas, and various resistances to deformation that contribute to the block shear capacity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.010
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0080.002

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 head0.004
GPT teacher head0.173
Teacher spread0.169 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2019
Admission routes1
Has abstractyes

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