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Record W4406365864 · doi:10.1139/cjce-2024-0322

Static behavior of CFRP-strengthened RC beams subjected to freeze–thaw cycles

2025· article· en· W4406365864 on OpenAlexafffundvenue
Mohamed Ahmed, Slimane Métiche, Radhouane Masmoudi, Richard Gagné, Jean‐Philippe Charron

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsPolytechnique MontréalUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStructural engineeringMaterials scienceReinforced concreteComposite materialGeotechnical engineeringEngineering

Abstract

fetched live from OpenAlex

Highway bridge durability, particularly in northern climates, presents a significant challenge for civil engineers. With numerous bridges exhibiting structural deficiencies, the importance of bridge rehabilitation cannot be overstated. This study aimed at determining the structural performance of 25 RC beams that were strengthened with carbon fiber-reinforced polymers (CFRP) sheets. These beams were exposed to different freeze–thaw cycles (FTCs) (100, 200, and 300 cycles) then were tested monotonically up to failure under four-point load test setup. The findings indicate that unanchored strengthened beams experienced a significant decrease of 12.5% in flexural capacity after 300 FTCs. Additionally, there is a notable shift in debonding characteristics, transitioning to adhesive failure at the fiber-reinforced polymers/concrete interface or a mixed mode, deviating from the cohesive failure in the concrete cover. The study concludes that FTC negatively impact the flexural performance of CFRP-strengthened beams, resulting in a reduced load-carrying capacity over time.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.005
GPT teacher head0.188
Teacher spread0.183 · 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
Published2025
Admission routes3
Has abstractyes

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