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Record W4385781702 · doi:10.1142/s1758825123500898

Developing Multi-Scale Model for Graphene Cement Nanocomposite: Study of Damage Initiation

2023· article· en· W4385781702 on OpenAlexaff
Hamik Haghverdian, D. Pourbandari, Abolfazl Alizadeh Sahraei, Hamidreza Nasersaeed, Majid Baniassadi, Mostafa Baghani

Bibliographic record

VenueInternational Journal of Applied Mechanics · 2023
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMaterials scienceGrapheneVolume fractionNanocompositeComposite materialAspect ratio (aeronautics)CementNanotechnology

Abstract

fetched live from OpenAlex

Damage initiation due to the interfacial debonding plays a vital role in the mechanical properties of graphene-reinforced concrete. In this research, multi-scale modeling is exploited to study the effect of volume fraction, aspect ratio, and interaction properties of the multi-layer graphene nanoplatelets (GNPs) on the mechanical properties of reinforced concrete, assuming perfectly bonded and cohesively bonded interaction between the contact surface of the matrix and the GNPs. The cohesive zone model has been used to observe the debonding behavior and damage initiation between the concrete matrix and nanocomposites for cohesively bonded interaction. The required cohesive zone parameters were estimated based on the previously calculated information on graphene–graphene interactions. The results show that by increasing the volume fraction and aspect ratio of GNP, nanofiller improves the mechanical properties of the nanocomposite. In addition, results reveal that interaction properties significantly affect the mechanical properties of graphene-reinforced concrete.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.547
Threshold uncertainty score0.481

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 head0.051
GPT teacher head0.302
Teacher spread0.251 · 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.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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