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Record W3209421188 · doi:10.1139/cjce-2021-0309

Shaking table test of combination isolation plate-shell integrated concrete liquid-storage structure

2021· article· en· W3209421188 on OpenAlexvenueno aff
Lei Qi, Xuansheng Cheng, Shanglong Zhang, Fuqiang Wang, Peiyan Xia

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsSlosh dynamicsEarthquake shaking tableAccelerationDisplacement (psychology)Structural engineeringMaterials scienceStrain gaugeGeotechnical engineeringSplashResidualMechanicsEngineeringPhysicsComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

To obtain the seismic performance of plate-shell integrated concrete liquid-storage structures (PSICLSS), a scale test model was built for shaking table testing. A combination isolation layer is proposed, and the dynamic responses of non-isolation and combination isolation PSICLSS under unidirectional and bidirectional earthquakes were tested. The results show that the combination isolation layer can change the spectral characteristics of the input acceleration, reducing the acceleration response and displacement of the structure, but it will increase the hydrodynamic pressure and liquid sloshing height. Under earthquake action, the liquid sloshing of combination isolation PSICLSS clearly appears as a nonlinear splash phenomenon. When considering the vertical earthquake, the horizontal acceleration response of PSICLSS and the liquid sloshing height change little, the peak hydrodynamic pressure changes significantly, and the peak position also changes. The maximum displacement of the isolation layer increases, but the residual displacement decreases.

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 score0.178
Threshold uncertainty score0.789

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 head0.180
Teacher spread0.175 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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