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Record W2890562084 · doi:10.62913/engj.v54i3.1117

Effective Shear Plane Model for Tearout and Block Shear Failure of Bolted Connections

2017· article· en· W2890562084 on OpenAlexaboutno aff
Lip H. Teh, Gregory G. Deierlein

Bibliographic record

VenueEngineering Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsnot available
FundersDepartment of Education and TrainingAustralian Government
KeywordsShear (geology)Structural engineeringDirect shear testUltimate tensile strengthSteel designShear stressEngineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

In spite of many revisions to the block shear requirements of the AISC Specification, the model in the current Specification can result in calculated strengths and failure modes that are inconsistent with published test data. The inconsistencies are primarily related to the assumed interaction of tensile and shear resisting mechanisms, combined with the definition of net and gross shear planes that are unrealistic. Using recently published test results of single-bolt connections in mild and high-strength steel plates, the shear failure planes are observed to be neither the assumed net nor gross shear planes, which are the basis of the current design provision, but rather effective shear planes with a calculated area that is between the net and gross areas. Based on the tensile rupture and shear yielding mechanism, and assuming that the steel on the effective shear planes is fully strain hardened, a simpler and more accurate block shear design equation is proposed. The new equation is straightforward to implement as it requires a simple rearrangement of existing design variables to determine an effective shear failure area. Through verifications against 161 gusset plate specimens, tested by independent researchers around the world, the proposed equation is shown to be significantly more accurate than the current AISC, Canadian, European and Japanese block shear design provisions. A resistance factor of 0.85 is recommended for use with the proposed equation, based on the available statistics from tests and well-established LRFD reliability principles. An example is presented to illustrate the impact of the proposed design provision, which can result in significantly fewer bolts per connection and/or smaller gusset plates, leading to simpler and more economical designs.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0040.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.007
GPT teacher head0.214
Teacher spread0.207 · 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

Citations40
Published2017
Admission routes1
Has abstractyes

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