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Record W2617114310 · doi:10.1504/ijhvs.2017.10005337

Experimental validation of the U<SUP align="right">*</SUP> index theory for load transfer analysis

2017· article· en· W2617114310 on OpenAlexaff
Khashayar Pejhan, Igor Telichev, Christine Wu, Qingguo Wang

Bibliographic record

VenueInternational Journal of Heavy Vehicle Systems · 2017
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsTransfer (computing)Index (typography)PhysicsAtomic physicsComputer science

Abstract

fetched live from OpenAlex

Engineering structures are designed to transfer external loads to their supports. Therefore, it is necessary to study the pattern of load transfer. The U* index method has been introduced to follow the load path in the structure. The U* value at each point corresponds to its significance in the load carrying process. The U* index theory has been used as a new design approach in lightweight vehicle structures but there has not been any experimental validation of this theory. In this study, two experiments are presented to show that the U*index is a true indicator of load transfer in the structure and areas with higher U* index values indeed carry more load. The results also prove the insensitivity of the U* index to stress concentrations. This study presents the first experimental validation of the U* index theory which is important to this new approach for light-weight vehicle structural design.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.019
GPT teacher head0.292
Teacher spread0.273 · 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

Citations9
Published2017
Admission routes1
Has abstractyes

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