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Record W2034599703 · doi:10.5539/mas.v2n5p9

Redevelopment of ANSYS in Mechanics Analysis of Railway-highway Combined Bridge

2008· article· en· W2034599703 on OpenAlexvenueno aff
Yiping Yin, Weishi Liu

Bibliographic record

VenueModern Applied Science · 2008
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsBridge (graph theory)RedevelopmentFinite element methodWork (physics)Computer scienceStructural engineeringEngineeringCivil engineeringMechanical engineering

Abstract

fetched live from OpenAlex

The length of railway-highway combined bridge is large and it’s calculating model having many elements makes it need a great of manpower and time in the Finite Element Analysis (FEA). With redevelopment of ANSYS, a FEA program is empoldered so that complex load calculation and loads combination is worked out automatically in ANSYS. It reduces manpower, shortens time and improves work efficiency. the FEA program was used successfully in analysis of Zhengzhou Yellow River railway-highway combined bridge, and all the work of calculation and results processing can be finished in sixteen days, which saves much time in researching and designing bridge and obtains good social benefits and economic benefits. It also provides convenience for other analysis of railway-highway combined bridges and demonstrates idea for other similar questions of development of ANSYS.

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)0.000
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: none
Teacher disagreement score0.518
Threshold uncertainty score0.404

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
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 head0.016
GPT teacher head0.210
Teacher spread0.194 · 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
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
Published2008
Admission routes1
Has abstractyes

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