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Record W2967584386 · doi:10.1139/cjce-2019-0030

Shear analysis and static load test of single-box and multicell composite girders with corrugated steel webs: a case study

2019· article· en· W2967584386 on OpenAlexvenueno aff
Jie Li, Guopeng Du, Guanjie Feng, Yuanlin Feng, Xin Yan, Yan Liang

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsStructural engineeringShear (geology)GirderTransverse planeMaterials scienceShear forceDiaphragm (acoustics)Geotechnical engineeringComposite materialEngineering

Abstract

fetched live from OpenAlex

The shear design of a corrugated steel web box girder is important for structural reliability. Presently, corrugated steel webs bear all the shear forces, and the top and bottom concrete plates bear no shear force. Each steel web has equal force. This design is conservative and uneconomical and may sometimes be unsafe. The shear ratio and transfer efficiency of each corrugated steel web are investigated in this study through numerical analysis. Then, the distribution of shear stress in corrugated steel webs is verified through a static load test. Results show that the shear is shared by the top and bottom concrete plates and corrugated steel webs. The shear ratio presents a close relation with boundary, loading, and section position. The influence of a transverse diaphragm on the shear ratio is minimal, and this influence demonstrates a certain effect only on the steel webs located at the area of the transverse diaphragm.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
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 head0.005
GPT teacher head0.172
Teacher spread0.167 · 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 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

Citations3
Published2019
Admission routes1
Has abstractyes

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