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Record W4390123093 · doi:10.2140/jomms.2024.19.19

Analytical evaluation of laminated composite DCB test data for achieving validated modelling analysis

2023· article· en· W4390123093 on OpenAlex
Gang Li, Guillaume Renaud, Chun Li

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of mechanics of materials and structures · 2023
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsComposite numberMaterials scienceComposite materialTest (biology)Structural engineeringReliability engineeringEngineeringGeology

Abstract

fetched live from OpenAlex

An analytical solution was developed to study mode I delamination in a laminated composite double cantilever beam (DCB) based on an augmented beam model considering lateral shear. Using the measured DCB compliance, the proposed analytical solution was employed to determine the initial delamination length and its propagation profile. Also, a finite element (FE) correction method was presented to establish a correlation between the delamination length and the DCB opening compliance. Similar delamination lengths were obtained from the analytical and the numerical methods. Consequently, the problematic delamination lengths generated from in-situ optical measurement were corrected using the two methods. The fracture resistance curves of the DCB specimen were also updated. Accordingly, the subsequent DCB FE modelling analyses, integrated with cohesive zone modelling or virtual crack closure technique, were able to generate practical predictions. The study shows that the developed analytical solution could also improve the DCB test efficiency without in-situ optical measurements.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.075
GPT teacher head0.321
Teacher spread0.246 · 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