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Bond Behavior of Self-Consolidating Concrete with Mineral and Chemical Admixtures

2008· article· en· W2043923044 on OpenAlexaff
Khandaker M. Anwar Hossain, Mohamed Lachemi

Bibliographic record

VenueJournal of Materials in Civil Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBond strengthMaterials scienceConsolidation (business)BondComposite materialCastingSelf-consolidating concreteUltimate tensile strengthSlag (welding)Structural engineeringMetallurgyCompressive strengthAdhesiveEngineering

Abstract

fetched live from OpenAlex

Self-consolidating concrete (SCC) is known for its excellent deformability, high resistance to segregation, and use in congested reinforced concrete structures characterized by difficult casting conditions without applying vibration. Research has been conducted on the development of SCC using high volumes of supplementary cementing materials (SCM) (such as fly ash and slag) and viscosity modifying admixtures (VMA). The bond characteristics of such SCCs are very important for their application in practical construction. An extensive investigation was conducted to determine the bond strength between deformed reinforcing steel bar and SCM and VMA based SCC as well as normal concrete (NC). Bond tests were conducted using a specially developed pullout test. The SCC pullout specimens were cast without applying any consolidation, whereas the NC specimens were cast by conventional practice with consolidation and vibration. It was found that the reduction in bond strength due to bleeding and inhomogeneous nature was less in SCC compared to NC. Although the variation in bond strengths at different casting elevations was observed in SCC, the extent was less significant than that of NC. SCC also exhibited a less significant top-bar effect compared to NC. This can be attributed to the more consistent nature of SCC and its superior filling capability. Performance of various code based and other existing bond equations are validated through experimental results illustrating the influence of concrete types (either SCC of different types or NC).

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)0.000
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.196
Teacher spread0.189 · 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

Citations55
Published2008
Admission routes1
Has abstractyes

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