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Record W2139102295 · doi:10.1139/cjce-2013-0445

Evaluation of reinforced concrete and reinforced engineered cementitious composite (ECC) members and structures using small-scale testing

2015· article· en· W2139102295 on OpenAlexvenueno aff
Bora Gencturk, Farshid Hosseini

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceCementitiousDuctility (Earth science)StiffnessStructural engineeringComposite materialComposite numberDeformation (meteorology)CementEngineering

Abstract

fetched live from OpenAlex

The behavior of reinforced concrete (RC) and reinforced engineered cementitious composites (ECC) was comparatively investigated at the component and system levels through a small-scale (1/8 scale factor) experimental program. The logistical and financial advantages of small-scale testing were utilized to investigate a range of parameters, including the effect of reinforcement ratio and material properties, on the response of reinforced concrete and reinforced ECC structures. The procedures pertaining to material preparation, specimen construction, and input motion development that were critical for enhancing the similarity between the scales are provided. Engineered cementitious composite mixtures with different cost and sustainability indices were evaluated. Under cyclic loading, the stiffness, strength, ductility, and energy absorption capacity of columns made of different ECC mixtures were found to be 110, 65, 45, and 100% higher, respectively, than those of the RC columns. The system level investigation through hybrid simulation showed that the ECC structures sustain less deformation under earthquake excitation due to high energy absorption capacity of the material. The differences in cost, sustainability, and structural performance of different ECC mixtures suggest that a careful selection of materials is required for optimal performance.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.075
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.036
GPT teacher head0.227
Teacher spread0.191 · 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.

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

Citations25
Published2015
Admission routes1
Has abstractyes

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