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Record W4387986746 · doi:10.7712/120123.10474.21197

PREDICTION OF REINFORCED CONCRETE COLUMNS LIMIT STATES USING MACHINE LEARNING ALGORITHM

2023· article· en· W4387986746 on OpenAlexaff
Anthos Ioannou, Nikolaos Bakas, George Markou, Ashley Megan van der Westhuizen, S. J. Pantazopoulou, M. Petrou, Dimos C. Charmpis

Bibliographic record

VenueCOMPDYN Proceedings · 2023
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsBerger (Canada)York University
Fundersnot available
KeywordsBrittlenessStructural engineeringDuctility (Earth science)Nonlinear systemComputer scienceExperimental dataShear strength (soil)Shear (geology)Strength of materialsReinforcementMaterials scienceEngineeringGeologyMathematicsComposite materialPhysicsCreep

Abstract

fetched live from OpenAlex

PREDICTION OF REINFORCED CONCRETE COLUMNS LIMIT STATES USING MACHINE LEARNING ALGORITHM - 9th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering - 12-14 June 2023, Athens, Greece

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)0.002
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: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.026
GPT teacher head0.227
Teacher spread0.201 · 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
Published2023
Admission routes1
Has abstractyes

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