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Record W374829675 · doi:10.1520/stp12589s

IBOLT: A Composite Bolted Joint Static Strength Prediction Tool

2004· book-chapter· en· W374829675 on OpenAlexaff
JR Eisenmann, CQ Rousseau

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsJoint (building)Composite numberBolted jointStructural engineeringMaterials scienceComposite materialEngineeringFinite element method

Abstract

fetched live from OpenAlex

The objective of this paper is to describe the bolted joint analysis method developed and used by LM Aeronautics (named IBOLT) including the theoretical basis, required input, and coupon-level strength-prediction validation. IBOLT performs a fracture-mechanics-based static strength prediction for a rectangular composite joint element subjected to any combination of biaxial membrane loads, shear loads, and an off- axis bolt load. Out-of-plane bending moments are also treated. Joint configuration effects (single- or double-shear) are modeled by a beam-on-elastic-foundation analysis to account for the thickness and stiffness of the joint members as well as the bolt bending and shear stiffness. Empirical equations are included to account for fastener head geometry, effects of filled versus open holes, fastener/hole clearance, and fastener torque. Required input, in addition to the usual geometry, loads, fastener, and substrate elastic properties, includes a variety of special laminate-level joint properties. Uni-axial notched, un-notched, and bolted-joint coupon tests are conducted to develop these properties. In addition to these uniaxial coupon tests, bearing/bypass tension and bearing/bypass compression tests have been performed to validate the accuracy of IBOLT strength predictions. An overview of these validation results is provided, along with a brief summary of IBOLT's strengths and weaknesses as a practical bolted joint stress analysis tool.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.488
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.204
Teacher spread0.188 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations9
Published2004
Admission routes1
Has abstractyes

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