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Record W4390538389 · doi:10.21203/rs.3.rs-3733622/v1

Influence of residual strength on progressive collapse of GFRP composite panels under transverse loading

2024· preprint· en· W4390538389 on OpenAlexaff
Ali Reza Nazari, Farid Taheri‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Mehdi Khanzadeh Moradllo

Bibliographic record

VenueResearch Square · 2024
Typepreprint
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsDalhousie University
Fundersnot available
KeywordsFlexural strengthComposite numberFibre-reinforced plasticMaterials scienceResidual strengthComposite materialStructural engineeringResidualSofteningCompressive strengthTransverse planeProgressive collapseComposite laminatesReinforced concreteEngineeringComputer science

Abstract

fetched live from OpenAlex

<title>Abstract</title> Collapse of panels under transverse loads has been reported dependent on formation of some unstable subregions surrounded by failure lines (hinges) within the panels, however such a collapse mechanism has been less investigated for the composite panels. In this paper, the significance of residual strength in the flexural failure lines around the unstable subregions through the collapse of GFRP panels was investigated. In the experimental program, the panels made of E-glass/vinylester composite laminates with various layups and aspect ratios were examined. To observe progression of failure lines in the panels due to large deflections, by application of a support composed of tubular components, the restraining influence of support on the panels was minimized. Based on well-known criteria, initiation and evolution of damage in the composite panels were simulated using the FE models. By a good estimation from the compressive residual strength in the failure lines based on the results of uniaxial compressive and three-point-bending tests, the FE models could simulate softening of the composite panels prior to collapse and temporary stability prior to complete loss of load carrying capacity. Using the FE models, contribution of various strain components in the flexural failure of the laminates and the influence of residual strength on the energy absorption capacity was probed for various configurations of the 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), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.442
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
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
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 head0.051
GPT teacher head0.365
Teacher spread0.314 · 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 designBench or experimental
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
Published2024
Admission routes1
Has abstractyes

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