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Record W3191167999 · doi:10.1139/cjce-2020-0345

Seismic performance assessment of RC columns with interlocking hoops

2021· article· en· W3191167999 on OpenAlexvenueno aff
Taehoon Kim

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsnot available
FundersKorea Railroad Research Institute
KeywordsInterlockingStructural engineeringReinforced concreteColumn (typography)Seismic loadingTransverse planeEngineeringGeotechnical engineeringConnection (principal bundle)

Abstract

fetched live from OpenAlex

The aim of this study is to analytically assess the seismic performance of reinforced concrete (RC) columns with interlocking hoops using a novel damage index, and to provide data for developing next generation seismic design criteria. Interlocking hoops are used in rectangular or non-circular columns, the splices shall be located in the column interior. Seismic performance of RC columns is controlled by the level of confinement provided by transverse steel. Interlocking hoops are commonly used in RC columns because they can provide more effective confinement than rectangular hoops. Three RC interlocking columns were tested under a constant axial load and a cyclically reversed horizontal load. A computer program, Reinforced Concrete Analysis in Higher Evaluation System Technology (RCAHEST), is used to analyze RC structures. Novel damage indices aim to provide a means of numerically quantifying the performance level in RC columns with interlocking hoops sustained under earthquake loading. The proposed numerical method for the seismic performance assessment of interlocking columns is verified by comparison with the experimental results.

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 score0.004

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)0.0010.000

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 head0.006
GPT teacher head0.187
Teacher spread0.181 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Civil EngineeringSame topicStructural Behavior of Reinforced ConcreteFrench-language works237,207