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Record W4400235033 · doi:10.11159/iccste24.122

Increase in the axial resistance of concrete walls for buildings through the application of Carbon Fiber Polymers

2024· article· en· W4400235033 on OpenAlexvenueno aff
LENIN MIGUEL BENDEZU ROMERO, ELENA ESPINOZA MUÑOZ, LUIS SANTA CRUZ DELGADO

Bibliographic record

VenueProceedings of the International Conference on Civil, Structural and Transportation Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials sciencePolymerComposite materialCarbon fiber reinforced polymerFiberCarbon fibersStructural engineeringReinforced concreteEngineeringComposite number

Abstract

fetched live from OpenAlex

This experimental work hopes to provide a potential solution by proposing techniques to improve the axial resistance capacity of reinforced concrete walls, using high-strength carbon filaments, and proposing a practical methodology for reinforcing existing reinforced concrete columns and walls. The methodology used to improve this work consists of the exploration and experimentation of two explicit parts, the first being a progression of material tests carried out in the laboratories of the Pontificia Universidad Católica del Perú, with the purpose of determining the direct mechanical properties. and not direct. The second part of the work included the analysis of the reinforced concrete walls through the non-linear concept modeled with the finite element method, verifying the non-linear attributes of the concrete by increasing the high-resistance carbon fiber. The results found have been obtained using the Cypecad software, and show that, in the case of carbon fiber reinforced specimens, a moment of 20.50 (tonf-m) is reached with respect to an axial load of 125.13 (tonf). On the other hand, the specimen without reinforcement reaches a moment of 18.20 (tonf-m) with respect to an axial load of 104.76 (tonf).

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.429
Threshold uncertainty score0.338

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 head0.010
GPT teacher head0.231
Teacher spread0.221 · 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 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

Explore more

Same venueProceedings of the International Conference on Civil, Structural and Transportation EngineeringSame topicStructural Behavior of Reinforced ConcreteFrench-language works237,207