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Record W2095117339 · doi:10.2478/sgem-2014-0033

Modeling of Fracture Propagation in Concrete Structures Using a Constitutive Relation with Embedded Discontinuity

2015· article· en· W2095117339 on OpenAlexaff
S. Pietruszczak, Ehsan Haghighat

Bibliographic record

VenueStudia Geotechnica et Mechanica · 2015
Typearticle
Languageen
FieldEngineering
TopicNumerical methods in engineering
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDiscontinuity (linguistics)Constitutive equationFinite element methodContext (archaeology)Extended finite element methodStructural engineeringBendingCrackingDeformation (meteorology)MechanicsComputer scienceMaterials scienceEngineeringGeologyPhysicsMathematical analysisMathematicsComposite material

Abstract

fetched live from OpenAlex

Abstract In this paper, the problem of modeling of mixed mode cracking in concrete structures is addressed within the context of a constitutive law with embedded discontinuity (CLED). This approach, which was originally developed for describing the propagation of localized deformation in a “smeared” sense, is enhanced here to model a discrete nature of crack propagation. The latter is achieved by coupling the CLED approach with the level-set method, which is commonly used within the framework of Extended Finite Element (XFEM). Numerical simulations of experimental tests conducted at Delft University, which involve four-point bending of a notched concrete beam under the action of two independent actuators, are presented. The results based on enhanced CLED approach are directly compared with XFEM simulations. The predictions from both these methodologies are quite consistent with the experimental data, thereby giving advantage to CLED scheme in view of its simplicity in the numerical implementation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.286
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2015
Admission routes1
Has abstractyes

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