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Record W2342740217 · doi:10.5539/mer.v6n1p75

Dynamic Simulation of Adaptive Truss Consisting of Various Types of Truss Members

2016· article· en· W2342740217 on OpenAlexvenueno aff
Kazuyuki HANAHARA, Xuan Zhang, Yukio Tada

Bibliographic record

VenueMechanical Engineering Research · 2016
Typearticle
Languageen
FieldEngineering
TopicStructural Analysis and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsTrussRelation (database)Structural engineeringComputer scienceDynamic simulationEngineeringSimulation

Abstract

fetched live from OpenAlex

An adaptive truss is a truss structural system equipped with functional truss members, such as length-adjustable members, shape memory alloy (SMA) members, spring-dashpot members, and so on. This is a representative example of so-called adaptive structure. In this study, we deal with the dynamic simulation of various types of adaptive trusses. The geometrical relation is given in a universal form applicable to all planar and spatial trusses. We develop descriptions of dynamic behavior of various types of functional truss members and give a general form of the descriptions. The equation of motion is formulated based on the geometrical relation and the description of member characteristics. Dynamics simulation procedure based on the Newmark Beta method is also developed. Dynamic behaviors of several types of adaptive trusses are simulated. The feasibility of the proposed dynamics calculation is confirmed; some characteristic dynamic behaviors of adaptive trusses are demonstrated.

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)0.000
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: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.026
GPT teacher head0.288
Teacher spread0.262 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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