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Record W4309920147 · doi:10.1139/cjce-2020-0392

Fire residual stress–strain curves of corroded thermo-mechanically treated (TMT) reinforcing steel bars

2022· article· en· W4309920147 on OpenAlexvenueno aff
Faraz Tariq, Pradeep Bhargava

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsnot available
Fundersnot available
KeywordsRebarMaterials scienceCorrosionMartensiteComposite materialStructural engineeringBainitePearliteStirrupMetallurgyResidual stressUltimate tensile strengthAusteniteMicrostructureEngineering

Abstract

fetched live from OpenAlex

Thermo-mechanically treated (TMT) reinforcing steel bars are extensively employed in RC buildings these days because of their superior thermal- and seismic-resisting characteristics, which enable them to sustain most of their strengths during earthquakes. The distinctive cross-sectional phase distribution (CSPD) of martensite, bainite, and pearlite in TMT rebars makes them outstanding. The current study examines the extent to which TMT reinforcing steel bars' mechanical properties deteriorate as a result of exposure to a combination of corrosion-induced CSPD damage and high temperatures. The research attempted to model an accidental fire in an aging structure with corroded members as a result of exposure to extreme environmental conditions. Heated residual tensile tests on rebar specimens subjected to various combinations of corrosion-induced damage and high temperatures were used in the experiment. The results show a significant loss in the key mechanical characteristics of reinforcing steel due to the superimposition of elevated temperature exposure on corrosion-induced damage to CSPD.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.078
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.009
GPT teacher head0.176
Teacher spread0.167 · 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 teacher head, not a consensus.

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

Citations5
Published2022
Admission routes1
Has abstractyes

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