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Record W2004610130 · doi:10.3390/polym6051381

Cost and Ductility Effectiveness of Concrete Columns Strengthened with CFRP and SFRP Sheets

2014· article· en· W2004610130 on OpenAlexafffund
Khaled S. Abd‐Elrahman, Raafat El‐Hacha

Bibliographic record

VenuePolymers · 2014
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsUniversity of Calgary
FundersUniversity of Calgary
KeywordsDuctility (Earth science)Parametric statisticsMaterials scienceStructural engineeringReinforced concreteComposite materialEngineeringCreepMathematics

Abstract

fetched live from OpenAlex

Recently, steel fibre reinforced polymers (SFRP) sheets have been introduced for the repair and rehabilitation of concrete structures. Few researchers studied the behaviour of the concrete columns wrapped with SFRP sheets; however, several critical parameters such as the cost and ductility effectiveness of the SFRP wrapped concrete columns have been lightly addressed. Thus, the main objective of this paper is to study the cost and ductility effectiveness of SFRP wrapped concrete columns and compare the results with the conventionally used carbon FRP (CFRP) wrapped concrete columns. In addition, an analytical procedure to predict the cost effectiveness of SFRP wrapped concrete columns is also suggested, from which, a parametric study was conducted. The parametric study investigated the effect of the concrete strength, the number of SFRP layers, and the size and slenderness effects on the cost effectiveness of the concrete columns wrapped with SFRP sheets. The results from the cost and ductility effectiveness study indicated that the SFRP wrapped concrete columns showed enhanced performance over the CFRP wrapped concrete columns. The suggested analytical procedure proved to be a reliable and accurate method to predict the cost effectiveness parameter of SFRP wrapped concrete columns. The parametric study showed the significant impact of the investigated parameters on the cost effectiveness of concrete columns wrapped with SFRP sheets.

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 score0.185
Threshold uncertainty score0.582

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 head0.008
GPT teacher head0.212
Teacher spread0.204 · 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

Citations21
Published2014
Admission routes2
Has abstractyes

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