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Record W3118785552 · doi:10.3390/su13010398

Corrosion Evaluation of Geopolymer Concrete Made with Fly Ash and Bottom Ash

2021· article· en· W3118785552 on OpenAlexaff
Priyanka Morla, Rishi Gupta, Peiman Azarsa, Ashutosh Sharma

Bibliographic record

VenueSustainability · 2021
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsCorrosionFly ashMaterials scienceGeopolymerPortland cementMetallurgyPotassium hydroxideCementSilicateSodium silicateChloridePotassium silicateComposite materialChemical engineering

Abstract

fetched live from OpenAlex

Environmental pollution caused by CO2 releasing from the production of cement is a great challenge for the construction industry and has triggered exploration into more sustainable alternatives. Geopolymer Concrete (GPC) is a potential sustainable solution that does not involve the use of cement as a binder. GPC is produced by mixing the alumino-silicate source materials such as fly-ash with alkali activators such as potassium hydroxide (KOH) and potassium silicate (K2SiO3). Unlike Ordinary Portland Concrete (OPC), the characteristics of GPC depend on the precursor materials and therefore vary for different mixes. Consequently, corrosion behavior needs to be evaluated separately for individual mixes. This has narrowed the scope of existing published work on corrosion behavior of GPC. In this study, GPC and OPC specimens were prepared and exposed to accelerated corrosion exposure. Half-cell potential and linear polarization resistance were used to evaluate the corrosion rate in GPC and OPC. Under accelerated conditions, the corrosion rate of the GPC specimens was between 10 µm/year and 20 µm/year exhibiting a moderate to high rate of corrosion. Meanwhile, the corrosion rate of the OPC specimens was between 40 µm/year and 60 µm/year indicating a very high corrosion activity. It can be concluded that GPC has a higher resistance to chloride-induced corrosion; with a low corrosion rate and lower mass loss percentage, compared to OPC.

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)0.001
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 score0.037
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.013
GPT teacher head0.271
Teacher spread0.258 · 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

Citations50
Published2021
Admission routes1
Has abstractyes

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