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Record W4205831667 · doi:10.3390/su14020712

Seismic Performance of Hybrid Corrosion-Free Self-Centering Concrete Shear Walls

2022· article· en· W4205831667 on OpenAlexafffund
Emad Abraik, Maged A. Youssef, S.F. El-Fitiany

Bibliographic record

VenueSustainability · 2022
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDuctility (Earth science)Fibre-reinforced plasticStructural engineeringBrittlenessSMA*Materials scienceCorrosionDurabilityShape-memory alloyRetrofittingComposite numberShear wallShear (geology)Composite materialEngineeringComputer scienceCreep

Abstract

fetched live from OpenAlex

Reinforced concrete (RC) walls are extensively used in high-rise buildings to resist lateral loads, while ensuring an adequate level of ductility. Durability problems, including corrosion of conventional steel reinforcements, necessitate exploring alternative types of reinforcement. The use of glass fiber reinforced polymer (FRP) bars is a potential solution. However, these bars cannot be used in seismic applications because of their brittleness and inability to dissipate seismic energy. Superelastic shape memory alloy (SMA) is a corrosion-free material with high ductility and unique self-centering ability. Its high cost is a major barrier to use in construction projects. The clear advantage of utilizing both SMA and FRP to achieve durable self-centering structures has motivated the development of a composite SMA-FRP bar. This paper investigates the hybrid use of FRP bars and either SMA bars or composite SMA-FRP in concrete shear walls. An extensive parametric study was conducted to study the effect of different design parameters on the lateral performance of hybrid RC walls. The seismic behavior of the hybrid walls was then examined. The hybrid walls not only solved the durability problem but also significantly improved the seismic 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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.999

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 science0.0010.001
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 head0.005
GPT teacher head0.204
Teacher spread0.199 · 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 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

Citations10
Published2022
Admission routes2
Has abstractyes

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