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Innovative Repair of Severely Corroded T-Beams Using Fabric-Reinforced Cementitious Matrix

2015· article· en· W1866600415 on OpenAlexaff
Tamer El‐Maaddawy

Bibliographic record

VenueJournal of Composites for Construction · 2015
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsUniversité Laval
FundersUnited Arab Emirates University
KeywordsMaterials scienceCorrosionBeam (structure)Flexural strengthComposite materialDuctility (Earth science)Ultimate tensile strengthCementitiousBendingStructural engineeringCementCreep

Abstract

fetched live from OpenAlex

This paper offers an innovative technique for rehabilitation of severely corroded reinforced concrete (RC) T-beams using fabric-reinforced cementitious matrix (FRCM). Eight RC T-beam specimens were constructed and tested to failure under four-point load configuration. One beam was neither corroded nor repaired to act as a benchmark. Seven beams were presubjected to accelerated corrosion for 5 months that corresponded to an average tensile steel mass loss of 22%. Corrosion was restricted to the tensile steel located in the middle third of the beam span. Six corroded beams were repaired with either carbon or basalt FRCM system whereas one corroded beam was left unrepaired. The fabrics were internally embedded within the clear cover of the corroded-repaired region and/or externally bonded along the beam span. Corrosion damage significantly reduced the flexural capacity and ductility of the unrepaired beam. The basalt FRCM system could not restore the original flexural capacity of the beam whereas the carbon FRCM system fully restored the capacity. Doubling the amount of the internally embedded carbon FRCM layers slightly increased the strength gain but restored only 90% of the original beam ductility. The use of a combination of internally embedded and externally bonded carbon FRCM layers was more effective in improving the flexural response than the use of same amount of FRCM layers internally embedded within the corroded-repaired region.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.460

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 head0.025
GPT teacher head0.265
Teacher spread0.240 · 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

Citations57
Published2015
Admission routes1
Has abstractyes

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