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Reinforced Concrete Shear Walls Detailed with Innovative Materials: Seismic Performance

2018· article· en· W2978317469 on OpenAlexaff
Mohammad Javad Tolou Kian, Carlos Cruz-Noguez

Bibliographic record

VenueJournal of Composites for Construction · 2018
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceComposite materialShear wallDuctility (Earth science)DissipationStructural engineeringShear (geology)CementitiousFiber-reinforced concreteFibre-reinforced plasticComposite numberReinforced concreteCementCreepEngineering

Abstract

fetched live from OpenAlex

This paper presents the experimental results of a pilot study performed on the seismic performance of three types of damage-resistant, slender reinforced concrete (RC) shear walls. The study explores three innovative schemes for the mitigation of postearthquake damage, including permanent lateral deformation and concrete damage, in RC shear walls. Each innovative shear wall had an aspect ratio of 2.0 and was reinforced with a hybrid reinforcing system consisting of mild steel and a type of self-centering reinforcement such as shape memory alloy bars, glass fiber reinforced polymer bars, or high-strength steel strands. To mitigate concrete damage, the walls were detailed with fiber reinforced cementitious composites, either engineered cementitious composite or steel fiber reinforced concrete. The specimens were supported as cantilevers, and then were tested up to failure under pseudo-static, cyclic loads. As test results showed, the innovative shear walls had smaller residual drift ratios and mitigated damage with respect to a conventional RC shear wall. The innovative walls also showed significant levels of energy dissipation and ductility throughout testing.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.743

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 communication0.0000.001
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 head0.008
GPT teacher head0.215
Teacher spread0.207 · 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

Citations40
Published2018
Admission routes1
Has abstractyes

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