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Record W4405015239 · doi:10.1139/cgj-2024-0097

Metal corrosion in partially saturated sands: pore fluid conductivity and water saturation

2024· article· en· W4405015239 on OpenAlexvenueno aff
Gloria M. Castro, Junghee Park, Abdelmounam Sherik, J. Carlos Santamarina

Bibliographic record

VenueCanadian Geotechnical Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsnot available
FundersSaudi Aramco
KeywordsSaturation (graph theory)Hydraulic conductivityGeotechnical engineeringWater saturationCorrosionPore water pressureGeologyMetalMaterials sciencePermeability (electromagnetism)PorositySoil waterComposite materialMetallurgySoil scienceChemistry

Abstract

fetched live from OpenAlex

Sediments can facilitate the electrochemical processes that drive the corrosion of buried metallic components. Common analyses and guidelines emphasize the effect of pore fluid conductivity on soil corrosivity and overlook the effect of partial saturation; yet, most buried metals are situated within the vadose zone. The detailed experimental study reported herein used mass loss measurements from passive corrosion tests, X-ray microtomography, and image analyses to examine the evolution of corrosion in C-steel coupons embedded in sand specimens mixed with de-ionized water and brine at various degrees of saturation. Experimental observations show that corroding cells preferentially form at contacting grains, and that the evolution of corrosion is biased by the variability in packing density and saturation, while fluid conductivity plays a lesser role. Above all, results highlight the critical importance of percolating gas and water phases, and show that water–grain–metal interfaces restrict the actively corroding area to a fraction of the entire metal surface. A complementary macroscale analysis anticipates asymptotic conditions based on mass and charge conservation, and the transport of corroding agents and residuals. Together, the measurements and model results highlight the significant impact of the degree of saturation on corrosion rates and mass loss.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.864
Threshold uncertainty score0.901

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)0.0000.000

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 head0.012
GPT teacher head0.214
Teacher spread0.202 · 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.

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

Citations5
Published2024
Admission routes1
Has abstractyes

Explore more

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