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Record W3194049385 · doi:10.1139/cjce-2020-0416

Behaviour of bolted lap joints in above-ground liquid-filled steel cylindrical tanks

2021· article· en· W3194049385 on OpenAlexaffvenue
Mehdi Moslemi, M. Reza Kianoush

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsStructural engineeringServiceability (structure)Bolted jointFinite element methodEngineeringParametric statistics

Abstract

fetched live from OpenAlex

Leakage is the main concern in steel bolted liquid tanks and past experience has indicated serviceability problems due to leakage, mainly from the joint locations, even in situations where the tank has only been subjected to an internal hydrostatic pressure. In this study, the performance of this type of construction was investigated using a detailed finite element (FE) analysis technique. Moreover, the validity of the American Water Works Association design practice in terms of the safe design of this type of structure at a wide range of service loads is evaluated through a parametric study. The FE analysis was performed in two stages using shell and solid elements in a three-dimensional ANSYS environment including the effect of geometric nonlinearity. The proposed FE model could estimating the behaviors such as capacity curve, yield/failure pattern, and the occurrence of ovalling and curling in bolted connections. In addition, the FE results were compared with experimental ones and found to be generally in good agreement. This study shows that a bolt arrangement chosen by design can efficiently improve the joint performance. Further, the use of a limited deformation criterion in the leak-resistant design of bolted lap joints in steel liquid containers is recommended.

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)0.002
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.202
Teacher spread0.193 · 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
Published2021
Admission routes2
Has abstractyes

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