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Research on Parametric Modeling and Computing of Multi-Tower Suspension Bridge Based on ANSYS

2012· article· en· W2080994844 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueApplied Mechanics and Materials · 2012
Typearticle
Languageen
FieldEngineering
TopicSimulation and Modeling Applications
Canadian institutionsMinistry of Transportation of Ontario
Fundersnot available
KeywordsBridge (graph theory)OpenGLSuspension (topology)TowerParametric statisticsFinite element methodGraphicsComputer scienceParametric modelInterface (matter)Parametric designStructural engineeringEngineeringEngineering drawingComputer graphics (images)Operating systemArtificial intelligenceVisualization

Abstract

fetched live from OpenAlex

Based on FEM program ANSYS and the OpenGL graphics, this paper develops the parametric modeling module and the computing module of the multi-tower suspension bridge, this module being embedded into the ANSYS system, and the parametric modeling module parameters can be entered by way of interface, which establish fast a multi-tower suspension bridge model. Calculation module can establish load conditions for the features of road bridge and specifications, in which multiple conditions can be defined and solved automatically. Post-processing part of the solution also for the results of the subtotals and select the output, so that the results of the output and finishing work has become more convenient and easier, and also the results can also be saved in word, excel and other different file types.

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.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.382
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.112
GPT teacher head0.338
Teacher spread0.226 · 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