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Combined Effects of Freeze-Thaw and Corrosion on Performance of RC Structures: State-of-the-Art Review

2021· article· en· W3190956891 on OpenAlex

Why this work is in the frame

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

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Performance of Constructed Facilities · 2021
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsNational Research Council CanadaUniversity of Ottawa
Fundersnot available
KeywordsCorrosionFrost (temperature)Reinforced concreteStructural engineeringMaterials scienceEnvironmental scienceForensic engineeringGeotechnical engineeringEngineeringComposite material

Abstract

fetched live from OpenAlex

Freezing and thawing cycles (FTC) on RC columns are a significant problem for vulnerable infrastructure exposed to extreme climate conditions. This problem is exacerbated by the presence of deicing agents that lead to reinforcement corrosion and overall concrete deterioration. Current research has mainly focused on studying the mechanical properties of concrete when exposed to cyclic conditions of freezing and thawing. Few studies have analyzed FTC’s influence or the dual action of FTC and steel corrosion on the structural performance of RC. This paper surveys available literature on the synergistic effects of one or multiple environmental exposures on RC columns and methodologies for inducing frost damage according to current standards. The literature survey is organized as follows: (1) frost damage mechanism; (2) test methods to evaluate frost damage; (3) effect of FTC on concrete mechanical properties; (4) effect of FTC on the structural performance of RC columns; and (5) effect of dual action of FTC and steel corrosion on RC columns. Finally, this paper draws a series of conclusions and recommendations for future work.

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.

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 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 score0.338
Threshold uncertainty score0.586

Codex and Gemma teacher scores by category

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

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 head0.188
Teacher spread0.183 · 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