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Record W2299691413

ANALYSIS AND DESIGN OF FLARE BRIDGE

2013· article· en· W2299691413 on OpenAlexaboutno aff
R.A. Ganorkar, Ashtashil Bhambulkar, P.I. Rode

Bibliographic record

VenueIJITR International Journal of Innovative Technology and Research - IJITR International Journal of Innovative Technology and Research · 2013
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsBridge (graph theory)Submarine pipelineEngineeringMarine engineeringStructural engineeringOffshore wind powerWind engineeringLift (data mining)Abu dhabiGeotechnical engineeringComputer scienceWind powerGeography
DOInot available

Abstract

fetched live from OpenAlex

Offshore flare bridge is a connecting bridge, it connects the process platform and flare platforms. The bridge is used for transport men, material and unused crude oil. Offshore design is slightly complicated due to harsher environment, also construction and installation of structures to suit offshore environment makes design challenging due to heavier weights. These structures are analyzed by, In place analysis , Lift analysis , and Load-out analysis . At offshore, tubular construction recommended due to the shape, circular cross section which attracts fare less wind loads, hence majority of the offshore structures are of steel tubular construction. In this paper analysis and design of an offshore flare bridge of length 132.76m, width 5.5m and height 8.5m, done for gravity load like self-weight of the bridge and live load from men, materials and unused crude oil weights and wind load. The behavior of the bridge is analyzed for 4 different wind speeds, similar procedure is followed by, China, Abu Dhabi, Canada and India.

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: none
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.051
GPT teacher head0.387
Teacher spread0.335 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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Same venueIJITR International Journal of Innovative Technology and Research - IJITR International Journal of Innovative Technology and ResearchSame topicStructural Integrity and Reliability AnalysisFrench-language works237,207