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Performance of RC Beams Strengthened Using Prestressed NSM-CFRP Strips Subjected to Fatigue Loading

2011· article· en· W2162087088 on OpenAlexafffundabout
Fadi Oudah, Raafat El‐Hacha

Bibliographic record

VenueJournal of Composites for Construction · 2011
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceComposite materialSTRIPSStructural engineeringPrestressed concreteUltimate tensile strengthCarbon fiber reinforced polymerDeflection (physics)Beam (structure)Reinforced concrete

Abstract

fetched live from OpenAlex

The fatigue performance of Reinforced Concrete (RC) beams strengthened using prestressed Near Surface Mounted (NSM) Carbon Fiber Reinforced Polymer (CFRP) strips was evaluated in this paper. Five full-scale RC beams were tested under fatigue loading simulating in-service loading conditions: one control unstrengthened beam and four beams strengthened using NSM-CFRP strips prestressed to 0, 20, 40, and 60% of the CFRP ultimate tensile strength. All beams were subjected to fatigue loading for 3 million cycles at a frequency of 2.0 Hz such that the stress range induced in the tension steel, during the initial cycle, is 125 MPa as specified by the Canadian Highway Bridge Design Code. Experimental test results show that the percentage deflection increase at the end of fatigue loading was almost the same for all beams, which implies that damage accumulation is independent from the prestress level. CFRP strain variation at midspan indicated the occurrence of deboning during the initial cycling of beams prestressed to 0 and 20%, whereas no signs of bond degradation were observed in the other beams. Therefore, prestressing the CFRP strips was shown to enhance the bonding properties and results in an overall CFRP strain increase at the completion of the fatigue loading. For all beams, steel and concrete strains and crack widths at midspan increased rapidly in the initial 500 cycles, followed by stabilized linear increase trends for the rest of the fatigue loading.

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 score0.108
Threshold uncertainty score0.764

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 head0.033
GPT teacher head0.240
Teacher spread0.207 · 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

Citations38
Published2011
Admission routes3
Has abstractyes

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