MétaCan
Menu
Back to cohort
Record W3135881898 · doi:10.2749/newyork.2019.0460

Finite element modeling of concrete beams reinforced with basalt FRP bars

2019· article· en· W3135881898 on OpenAlex
Jordan Carter, Aikaterini S. Genikomsou

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

VenueReport · 2019
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsQueen's University
Fundersnot available
KeywordsFibre-reinforced plasticMaterials scienceCrackingParametric statisticsFinite element methodTension (geology)Structural engineeringCorrosionCalibrationBasalt fiberComposite materialCompression (physics)Nonlinear systemReinforced solidDuctility (Earth science)Reinforced concreteFiberEngineeringMathematics

Abstract

fetched live from OpenAlex

<p>Fiber-reinforced polymer (FRP) bars can replace conventional steel reinforcing rebars to prevent from corrosion in reinforced concrete structures exposed to highly corrosive environments. In this contribution, three tested concrete beams reinforced with BFRP (Basalt Fiber Reinforced Polymer) bars are analyzed using three-dimensional finite element methods. In the numerical analyses, concrete is modeled as nonlinear using plasticity and damage principles, while BFRP is modeled as linear elastic material. The main focus of this research is to present the calibration process that should take place prior to any parametric studies. This calibration suggests that the concrete model should be regularized using a characteristic length and material post-yield fracture energies in both tension and compression to provide mesh-size independent results. The numerical results are compared to the test results with regard to failure load and cracking.</p>

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.770

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.011
GPT teacher head0.218
Teacher spread0.208 · 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