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Record W4416313938 · doi:10.4028/p-ok5mqt

The Application of a Hybrid Damage Modeling and Simulation Methodology to Composite Laminate Ultimate Strength Predictions

2025· article· W4416313938 on OpenAlexaff
Gang Qi, Heng Hannah Liu, Il Yong Kim, Diane Wowk

Bibliographic record

VenueKey engineering materials · 2025
Typearticle
Language
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsRoyal Military College of CanadaQueen's UniversityNational Research Council Canada
Fundersnot available
KeywordsComposite numberFinite element methodCompression (physics)Tension (geology)Composite laminatesModeling and simulationResidual strengthMatrix (chemical analysis)Fibre-reinforced plastic

Abstract

fetched live from OpenAlex

A reliable hybrid modeling and simulation methodology is developed to predict the progressive damage evolution and ultimate strength in multidirectional fiber-reinforced polymer (FRP) composite laminates. The integrated modeling approach combines continuum damage modeling (CDM), the extended finite element method (X-FEM), and the cohesive zone modeling (CZM) technique, to capture fiber breakage, polymer matrix major cracking, composite ply interlaminar delamination, and the interactions of these failure modes. The Schapery theory is incorporated into the finite element model to accurately simulate the pre-peak nonlinearity of the load-bearing response caused by matrix micro-cracking. Multidirectional composite laminates with open-hole tension (OHT), open-hole compression (OHC), filled-hole tension (FHT), and filled-hole compression (FHC) configurations are examined as case studies. It is demonstrated that this hybrid modeling framework and methodology can effectively and efficiently capture the complex composite damage progression and properly predict the residual strengths of damaged composite laminates.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.470
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.289
Teacher spread0.268 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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