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Record W2119297172 · doi:10.1002/suco.201400081

Analysis of cracking in steel fibre‐reinforced concrete (SFRC) structures in bending using probabilistic modelling

2015· article· en· W2119297172 on OpenAlex

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

VenueStructural Concrete · 2015
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsCrackingStructural engineeringUltimate tensile strengthMaterials scienceProbabilistic logicComposite materialBridging (networking)BendingFinite element methodReinforcementComputer scienceEngineering

Abstract

fetched live from OpenAlex

Abstract An improvement to the probabilistic discrete cracking model for fibre‐reinforced concretes, originally developed by Rossi, is proposed in this paper. This new model features the following: – Crack formation and propagation in the concrete is taken into account by using special interface elements. These elements open once the normal tensile stress at their centre of gravity reaches the tensile strength of the element. The probabilistic aspect of the cracking process is taken into account by the fact that the tensile strength is randomly distributed throughout the mesh elements. – Immediately after the formation of cracks, the fibre bridging effect is considered by a damage model approach. The probabilistic aspect consists of randomly distributing the post‐cracking energy. The improved numerical model is used to analyse the bending behaviour of three SFRC beams made from the same material. The numerical simulations are compared with experimental results in terms of the global behaviour of and cracking processes in the beams.

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 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: Empirical
Teacher disagreement score0.053
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.042
GPT teacher head0.275
Teacher spread0.232 · 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