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Record W2181027180 · doi:10.1139/cjce-2012-0318

Assessment of current design procedures for conical tanks under seismic loading

2013· article· en· W2181027180 on OpenAlexaffvenue
M. Jolie, M.M. Hassan, Ashraf A. El Damatty

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsWestern University
Fundersnot available
KeywordsConical surfaceStructural engineeringCylinderStorage tankWater tanksEngineeringSeismic analysisFlexibility (engineering)Geotechnical engineeringMarine engineeringMathematicsMechanical engineering

Abstract

fetched live from OpenAlex

This study is motivated by the fact that no design provisions currently exist specifically for conical tanks under seismic excitations. The design of liquid storage structures is governed by the American Water Works Association (AWWA), American Petroleum Institute (API), or Eurocode 8. An approximate method based on replacing the conical tank with an equivalent cylinder is given in these design codes. The state of stresses in conical tanks is different than that of cylindrical tanks. A previously established equivalent mechanical model is used to determine the response of a number of conical tanks under horizontal excitations. The models incorporate the different components of the hydrodynamic pressure and account for flexibility of the tank walls. Using an assortment of seismic hazard areas, the maximum base shear force and overturning moment are evaluated for a number of conical tanks. Those are compared to the corresponding values predicted by the design codes using the equivalent cylinder approach. The results reveal that this approximate approach is not adequate for designing conical tanks to resist seismic excitations.

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)0.004
metaresearch head score (Gemma)0.007
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 score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
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 head0.019
GPT teacher head0.255
Teacher spread0.236 · 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

Citations11
Published2013
Admission routes2
Has abstractyes

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