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Deterioration Modeling of Steel Components in Support of Collapse Prediction of Steel Moment Frames under Earthquake Loading

2010· article· en· 997 citations· W1963777170 on OpenAlex· 10.1061/(asce)st.1943-541x.0000376

Why is this work in the frame?

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

Canadian affiliationAn author listed a Canadian institution. This is the only route the usual frame has.

Full frame distilled prediction

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.

Candidate categories
none
Consensus categories
none
Domain
Candidate signal: noneConsensus signal: none
Study design
Candidate signal: Simulation or modelingConsensus signal: Simulation or modeling
Genre
Candidate signal: EmpiricalConsensus signal: Empirical
Teacher disagreement score
0.444
Threshold uncertainty score
0.788
Validation status
machine_predicted_unvalidated · codex-gemma-dda1882f352a

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

Machine scores (provisional)

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

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.

Opus teacher head0.013
GPT teacher head0.217
Teacher spread
0.204 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Abstract

Reliable collapse assessment of structural systems under earthquake loading requires analytical models that are able to capture component deterioration in strength and stiffness. For calibration and validation of these models, a large set of experimental data is needed. This paper discusses the development of a database of experimental data of steel components and the use of this database for quantification of important parameters that affect the cyclic moment-rotation relationship at plastic hinge regions in beams. On the basis of information deduced from the steel component database, empirical relationships for modeling of precapping plastic rotation, postcapping rotation, and cyclic deterioration for beams with reduced beam section (RBS) and other-than-RBS beams are proposed. Quantitative information is also provided for modeling of the effective yield strength, postyield strength ratio, residual strength, and ductile tearing of steel components subjected to cyclic loading. © 2011 American Society of Civil Engineers.

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.

The record

Venue
Journal of Structural Engineering
Topic
Structural Response to Dynamic Loads
Field
Engineering
Canadian institutions
McGill University
Funders
National Science Foundation
Keywords
StiffnessStructural engineeringTearingPlastic hingeMoment (physics)CalibrationResidualResidual strengthRotation (mathematics)Materials scienceBeam (structure)Cold-formed steelComponent (thermodynamics)Yield (engineering)Computer scienceComposite materialEngineeringFinite element methodMathematics
Has abstract in OpenAlex
yes