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Record W2473872939 · doi:10.1016/j.proeng.2016.06.269

An Assessment of Bicycle Frame Behaviour under Various Load Conditions Using Numerical Simulations

2016· article· en· W2473872939 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

VenueProcedia Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsStructural engineeringContext (archaeology)Finite element methodSaddleFrame (networking)EngineeringComputer scienceMechanical engineeringGeology

Abstract

fetched live from OpenAlex

This paper outlines the use of a finite element model to simulate the behaviour for a standard steel bicycle frames under a range of measured load cases. These load cases include those measured both in the laboratory setting and also in the field, and include loads transmitted at key areas such as the dropouts and hub, the bottom bracket and drive, the headset and handlebars, and the seat post and saddle. The load cases analysed include static representations of dynamic bump situations which occur sporadically and also those which occur constantly or regularly such as those generated at the drive and handlebars during climbing or cruising. The resulting stresses within the bicycle are analysed in the context of frame performance relating to static and fatigue strengths and are also compared to similar load cases presented in the literature. Further research is required to understand the influence of tube profiles on frame strength, and to analyse the modes of failure for various bicycle designs and materials used under typical and extreme usage in order to understand the implications of design on safety.

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)0.000
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 score0.664
Threshold uncertainty score0.661

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.022
GPT teacher head0.341
Teacher spread0.319 · 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