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Record W4402292997 · doi:10.1139/cjce-2023-0565

Study on seismic performance of the CFST-framed aeolian sand concrete composite shear wall

2024· article· en· W4402292997 on OpenAlexvenueno aff
Kangjie Chen, Chi Wang, Yaohong Wang, Zhiqiang Li, Wei Dong, Xiaokai Lu

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
FundersNatural Science Foundation of Inner MongoliaNational Natural Science Foundation of China
KeywordsGeotechnical engineeringComposite numberGeologyAeolian processesShear (geology)Structural engineeringEngineeringMaterials scienceGeomorphologyComposite material

Abstract

fetched live from OpenAlex

This study examined the seismic performance of composite shear walls framed with concrete-filled steel tubes (CFST) utilizing aeolian sand concrete. Six specimens were designed and tested under low cyclic load with a constant axial compression ratio. The research aimed to analyze how the CFST frame affects the seismic behavior of shear walls made from aeolian sand concrete and understand the underlying influence mechanism. The results showed significant differences in failure modes between CFST-framed and conventional aeolian sand concrete shear walls, with the former exhibiting a lower degree of failure and instances of plastic hinge failure. The hysteretic curve of CFST-framed walls displayed noticeable pinching, indicating enhanced seismic energy dissipation capacity and ductility. The damage analysis model based on the energy damage principle was found to be suitable for conducting seismic damage analysis of these walls. A shear capacity model was also established to accurately depict the variation in bearing capacity under low cyclic loading, providing valuable insights for engineering applications.

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)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.179
Teacher spread0.173 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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