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Effect of Surface Roughness on Cyclic Ductility of Corroded Steel

2016· article· en· W2274994810 on OpenAlexaff
Steve Burke, Michel Bruneau

Bibliographic record

VenueJournal of Structural Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsProfessional Engineers Ontario
Fundersnot available
KeywordsDissipationDuctility (Earth science)Monotonic functionMaterials scienceStructural engineeringSurface finishSurface roughnessYield (engineering)Multilinear mapComposite materialEngineeringMathematics

Abstract

fetched live from OpenAlex

Stable cyclic hysteretic behavior is required from structural members to dissipate seismic energy. Limited knowledge exists on the hysteretic behavior of corroded steel, and a relationship that quantifies energy dissipation capacity as a function of section thickness and roughness does not exist. Monotonic and cyclic tests of corroded steel were conducted in this research in an attempt to provide such quantification. Results from coupons suggest that assessing the strength of rusted members by machining a smooth coupon from steel extracted from an existing corroded structure, and only using the resulting yield and ultimate strength values in otherwise standard multilinear monotonic models, may be an unconservative approach. Results from cycling tests show that: (1) rusted steel can exhibit a significant hysteretic energy capacity, (2) a linear relationship exists between the total dissipated energy normalized by mean thickness and the mean 10-point-height of irregularities, and (3) increases in roughness correspond to decreases in the magnitude of total normalized energy dissipated before complete failure.

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)0.001
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.220
Teacher spread0.215 · 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

Citations23
Published2016
Admission routes1
Has abstractyes

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