MétaCan
Menu
Back to cohort
Record W3194576438 · doi:10.5772/intechopen.99650

Bearing Capacity of Concrete Filled Steel Tube Columns

2021· book-chapter· en· W3194576438 on OpenAlexaboutno aff
А Л Кришан

Bibliographic record

VenueIntechOpen eBooks · 2021
Typebook-chapter
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsStructural engineeringBearing capacityConstructiveLoad bearingCompression (physics)EngineeringDeformation (meteorology)Geotechnical engineeringComputer scienceMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Concrete filled steel tubes columns of circular cross section (CFST) have significant constructive, technological, economic advantages. Therefore, CFST are increasingly used in construction practice. Due to the complex nature of CFST load resistance, regulations of the Europe, Australia, Brazil, India, Canada, China, the USA, Japan, and of a number of other countries recommend using empirical formulas for calculating their bearing capacity. Despite the large number of the experiments, serving as a basis for these formulas, they do not always allow to obtain valid results. Besides, these methods, as a rule, do not allow the calculations of compressed CFST elements, which have any differences from a “classical” design, for example, the presence of a high-strength rod and (or) spiral reinforcement, various types of concrete, the effect of preliminary lateral compression of a concrete core, etc. The purpose of this monograph is to propose the method of deformation calculation of the bearing capacity of compressed CFST elements under short-term load action based on the phenomenological approach and the theoretical positions of reinforced concrete mechanics.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.021
GPT teacher head0.209
Teacher spread0.188 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueIntechOpen eBooksSame topicStructural Load-Bearing AnalysisFrench-language works237,207