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Record W4417221474 · doi:10.5267/j.esm.2025.11.003

An assessment of progressive damage in mechanical joint of GLASS/EPOXY composite under quasi-static loading

2025· article· W4417221474 on OpenAlexvenueno aff
Nabi Mehri Khansari, Mehdi Sepehrifar

Bibliographic record

VenueEngineering Solid Mechanics · 2025
Typearticle
Language
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsnot available
Fundersnot available
KeywordsDelamination (geology)Composite numberAerospaceDurabilityJoint (building)Damage toleranceUltimate tensile strengthComposite laminates

Abstract

fetched live from OpenAlex

The prediction of crack initiation and propagation of damage initiation and propagation in composite structures has gained significant attention due to the increasing use of these materials in the aerospace industry. In this context, estimating progressive damage in composite ply is crucial, as it refers to the gradual failure and deformation of the structure, which can lead to a reduction in the useful life and safety of the structure. By examining these damages, it is possible to identify the causes and factors contributing to their occurrence and to propose suitable solutions for preventing and repairing the damages. In the present study, an effort is made to develop numerical, analytical, and experimental approaches for modeling and estimating progressive damage at mechanical joints in composite aircraft structures, considering quasi-static loading, including tensile loading. The study incorporates an investigation of damage mechanisms such as fiber breakage, matrix cracking, and delamination that commonly occur in composite laminates under mechanical stress. Combining modeling and experimental results allows for a comprehensive understanding of damage evolution, enabling the formulation of strategies aimed at improving the durability and safety of composite structures in aerospace applications. Ultimately, based on the results of modeling and experiments, strategies will be proposed to enhance the lifespan of the structure.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.561
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.317
Teacher spread0.301 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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