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Record W4394748100 · doi:10.3390/buildings14041079

Corrosion Performance of Buried Corrugated Galvanized Steel under Accelerated Wetting/Drying Cyclic Corrosion Test

2024· article· en· W4394748100 on OpenAlexaff
Islam Ezzeldin, Hany El Naggar, John Newhook

Bibliographic record

VenueBuildings · 2024
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsDalhousie University
Fundersnot available
KeywordsGalvanizationCorrosionMaterials scienceWettingMetallurgyDuctility (Earth science)Ultimate tensile strengthZincCoatingCrackingComposite materialLayer (electronics)Creep

Abstract

fetched live from OpenAlex

Rehabilitation of corroded buried galvanized steel structures, including corrugated metal culverts (CMCs) and pipes (CMPs), requires a deep understanding of the corrosion process and the corresponding deterioration. The current paper describes an accelerated laboratory corrosion test of corrugated galvanized steel coupons exposed to sequenced wetting/drying cycles ranging from 50 and up to 1600 cycles. The analysis demonstrates the influence of applying an increased number of wetting/drying cycles on the acceleration of the developed corrosion in the buried galvanized steel coupons. The study examines changes in the steel geometry represented by thickness loss and the accompanied deterioration of the mechanical properties such as tensile strength, hardness, and ductility over relatively short periods of time. It was observed that corrosion was insignificant as long as the zinc coating of the galvanized steel lasted. However, when the zinc was almost fully depleted, the bare steel was directly subjected to the surrounding corrosive environment, causing greater corrosion damage during subsequent wetting/drying cycles. Based on four standard mathematical models, the paper also presents approximate average corrosion predictions for bare steel in the galvanized coupons, to assess the impact of potential damage due to corrosion and determine essential rehabilitation measures.

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 categoriesMeta-epidemiology (narrow)
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 score0.397
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
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.241
Teacher spread0.223 · 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 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

Citations5
Published2024
Admission routes1
Has abstractyes

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