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Record W7083204552 · doi:10.23977/jemm.2025.100202

Mechanical Performance of Yurt Assemblies: A Finite Element Analysis Approach

2025· article· en· W7083204552 on OpenAlexvenueno aff

Bibliographic record

VenueJournal of Engineering Mechanics and Machinery · 2025
Typearticle
Languageen
FieldComputer Science
TopicSoftware-Defined Networks and 5G
Canadian institutionsnot available
Fundersnot available
KeywordsFinite element methodStiffnessUltimate tensile strengthDisplacement (psychology)ComputationDirect stiffness methodStiffness matrixBoundary (topology)

Abstract

fetched live from OpenAlex

This study presents a comprehensive finite element analysis (FEA) of the structural behavior of yurt assemblies subjected to extreme loading conditions, including a gravity load of 180 N and a wind load of 1984.5 N. The modeling process encompasses the geometric definition of nodes and elements, formulation of element stiffness matrices, coordinate transformations, global stiffness matrix assembly, imposition of loads and boundary conditions, and computation of nodal displacements and internal forces. The analysis reveals that the yurt structure exhibits stable mechanical performance under the specified loading conditions, with a maximum apex displacement of only 3.5 mm. Internal force distribution aligns with fundamental principles of structural mechanics: vertical support members sustain the highest compressive force (268.3 N), while bottom horizontal members primarily undergo tensile loading (142.1 N). The maximum compressive and tensile stresses are found to be 0.89 MPa and 0.47 MPa, respectively. When compared to the yield strength of steel (235 MPa), the corresponding safety factors are approximately 264 and 500, significantly surpassing standard design criteria. These findings underscore the structural efficiency and high safety factor of yurt configurations under combined loading scenarios.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.876
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.007
GPT teacher head0.211
Teacher spread0.204 · 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
GenreMethods

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
Published2025
Admission routes1
Has abstractyes

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