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Record W2625992690 · doi:10.1139/cjce-2016-0297

Design procedure for liquid storage steel conical tanks under seismic loading

2017· article· en· W2625992690 on OpenAlexaffvenue
Ahmed Y. Musa, Ashraf A. El Damatty

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsWestern University
Fundersnot available
KeywordsConical surfaceStructural engineeringStorage tankCylinderSeismic analysisEngineeringFinite element methodNonlinear systemFocus (optics)Geotechnical engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Steel conical liquid tanks are widely used for the purpose of liquid storage. Despite the fact that some of these tanks have failed lately, most of the seismic design guidelines focus on the design of steel cylindrical tanks with the only design guidelines for conical tanks based on using an equivalent cylinder approach, which is not based on any theoretical or experimental basis. In this study, a simplified procedure is proposed to design steel conical tanks subjected to horizontal and vertical excitations. The proposed procedure is based on satisfying a design formula that combines the ratio of seismic demand to the tank resistance due to horizontal and vertical excitations including geometric imperfections. The design procedure is then validated by comparing its outcomes with those obtained using time history analysis. The study is carried out numerically using an in-house nonlinear finite element model.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.018
GPT teacher head0.228
Teacher spread0.210 · 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 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
Published2017
Admission routes2
Has abstractyes

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