MétaCan
Menu
Back to cohort
Record W4409740960 · doi:10.1139/cjce-2024-0330

Seismic behaviour of slender concrete walls with high-strength reinforcement

2025· article· en· W4409740960 on OpenAlexvenueno aff
Pinle Zhang, Jia Yi

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsReinforcementStructural engineeringGeotechnical engineeringCompressive strengthEngineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Twelve concrete walls with 500 MPa reinforcement and aspect ratios of 2.8 and 2.15 were tested under cyclic loading. Seismic behaviour was evaluated through analysis of failure characteristics, hysteretic curves, energy dissipation, and ductility. Results demonstrated that walls with an aspect ratio of 2.8 exhibited flexure-dominant failure modes, while those with an aspect ratio of 2.15 exhibited bending-shear failure modes. All specimens exhibited exceptional ultimate deformation capacity, sustaining a lateral drift ratio of 2.0% without obvious loss of lateral strength. Flanged concrete walls exhibited higher load-bearing capacity when the web was in compression, whereas better displacement ductility was achieved when the web was in tension. Compared with rectangular walls, flanged walls significantly enhanced strength and stiffness, but exhibited reduced ductility when the web was in compression. To ensure displacement ductility coefficients exceeding 3.0 for flanged walls with axial load ratios greater than 0.50 under web compression, the transverse reinforcement ratio at the free web boundary should surpass GB 50011-2010 requirements by more than 30%; however, the flange concrete remained intact without spalling, suggesting that the special confined reinforcement specified in GB 50011-2010 for flange ends was unnecessary.

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: none
Teacher disagreement score0.529
Threshold uncertainty score0.700

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 head0.004
GPT teacher head0.166
Teacher spread0.162 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Civil EngineeringSame topicGeotechnical Engineering and Soil StabilizationFrench-language works237,207