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Record W3134729558 · doi:10.1139/cjce-2019-0570

Seismic performance of concrete walls reinforced by high-strength bars: cyclic loading test and numerical simulation

2021· article· en· W3134729558 on OpenAlexaffvenue
Xiangyong Ni, Shuangyin Cao, Hassan Aoude

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsUniversity of Ottawa
FundersNational Key Research and Development Program of ChinaGraduate Research and Innovation Projects of Jiangsu ProvinceMajor State Basic Research Development Program of ChinaNatural Science Foundation of Jiangsu ProvinceGovernment of Jiangsu Province
KeywordsStructural engineeringDissipationDuctility (Earth science)Shear wallFinite element methodFlangeStiffnessBar (unit)Materials scienceReinforcementSeismic analysisGeotechnical engineeringComposite materialEngineeringGeologyCreep

Abstract

fetched live from OpenAlex

This study examines the influence of cross-section shape on the seismic behaviour of high-strength steel reinforced concrete shear walls (HSS-RC) designed with Grade HRB 600 MPa reinforcement. As part of the study, two flexure-dominant walls with rectangular and T-shaped cross-sections, are tested under reversed cyclic loading. Seismic performance is evaluated by studying the failure characteristics, hysteretic curves, energy dissipation, ductility, and reinforcing bar strains in the two walls. As part of the numerical study, two-dimensional (2D) and three-dimensional (3D) finite element modelling (FEM) are used to predict the seismic response of the rectangular and T-shaped walls, respectively. The test results show that compared to the rectangular wall, the flange in the T-shaped HSS-RC wall increased strength, energy dissipation and stiffness, but decreased ductility. The analytical hysteretic curves calculated using 2D and 3D FEM analyses show good agreement with the experimental test 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 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 score0.025
Threshold uncertainty score0.977

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 science0.0000.000
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 head0.005
GPT teacher head0.182
Teacher spread0.177 · 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

Citations14
Published2021
Admission routes2
Has abstractyes

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