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Record W96792631

Evaluation of impact factors of straight and horizontally curved composite concrete deck-steel cellular bridges.

2001· article· en· W96792631 on OpenAlexaboutno aff
Zhang Xue-sheng

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2001
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsDeckComposite numberStructural engineeringBridge deckEngineeringForensic engineeringMaterials scienceComposite material
DOInot available

Abstract

fetched live from OpenAlex

A theoretical investigation of the dynamic impact factors for straight and curved composite cellular bridges is performed in this thesis. The bridges are modelled as three-dimensional solid structures using commercially available software "ABAQUS" to simulate the bridge geometry and vehicle loading. The vehicle loads are modelled as a pair of two concentrated forces moving along in circumferential paths. Extensive parametric study is conducted, in which 120 composite multi-cell bridge prototypes are analyzed to: (1) evaluate their first natural frequencies; (2) evaluate their impact factors for moment, reaction, and deflection under truck loading conditions. The key parameters considered in this study are: number and area of cross-bracing and top-chord systems, number of cells, number of lanes, degree of curvature, span length, and loading conditions. Based on the data generated from the parametric study, expressions for dynamic impact factors for moment, reaction, and deflection are proposed.* (Abstract shortened by UMI.) *This dissertation includes a CD that is compound (contains both a paper copy and a CD as part of the dissertation). The CD requires the following application: Microsoft Office. Source: Masters Abstracts International, Volume: 41-01, page: 0279. Adviser: John B. Kennedy. Thesis (M.A.Sc.)--University of Windsor (Canada), 2001.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.230
Teacher spread0.204 · 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.

Study designObservational
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
Published2001
Admission routes1
Has abstractyes

Explore more

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