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Strengthening Square and Circular Low-Strength Concrete Columns with Fiber-Reinforced Cementitious Matrix: Experimental Investigation

2022· article· en· W4210660160 on OpenAlexaff
Farid Abed, Ahmed El Refai, Tamer El‐Maaddawy, Noor Tello, Yazan Alhoubi

Bibliographic record

VenuePractice Periodical on Structural Design and Construction · 2022
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsDuctility (Earth science)Materials scienceSquare (algebra)Structural engineeringComposite materialColumn (typography)CementitiousCross section (physics)Compressive strengthReinforcementCementGeometryMathematicsCreepEngineering

Abstract

fetched live from OpenAlex

This study investigated the efficiency of strengthening low-strength RC short columns with fiber-reinforced cementitious matrix (FRCM). Twelve columns were cast with concrete with a compressive strength of 18 MPa. All columns had a reinforcement ratio of 1.5%. The investigated parameters were the column cross section (square or circular), the spacing between the ties (90 and 180 mm) selected based on the columns’ dimensions, and the number of FRCM layers used in wrapping the columns [zero, two, and four layers of paraphenylene-ben-zobisoxazole (PBO) FRCM]. All columns had a clear height of 800 mm and were tested monotonically until failure. Results showed that for columns wrapped with two PBO-FRCM layers, using a tie spacing of 90 mm eliminated the effect of varying the cross section. However, circular columns showed a higher increase in capacity than square columns for a tie spacing of 180 mm, where the increase was 40%. For all columns wrapped with four PBO-FRCM layers, the cross-section shape was the sole influence on ultimate capacity, where circular columns noticeably showed a more improved capacity. Also, column load–strain relationships were only influenced by the tie spacing. All strengthened columns showed improved ductility with the increase in PBO-FRCM layers. Using existing design provisions, the theoretical capacity of the columns was calculated, and results showed that the code underestimates ultimate capacity, where the theoretical capacities were lower than the experimental ones by 5%–20%.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 source (direct Gemma or distilled Codex), 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

Citations13
Published2022
Admission routes1
Has abstractyes

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