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Record W2909768503

Comparison of chloride-induced corrosion between alkali-activated slag concretes and Portland cement concretes

2018· article· en· W2909768503 on OpenAlexfundno aff
Muhammed Basheer, Keun Hyeok Yang, Qianmin Ma, Sreejith Nanukuttan, Chuanxi Yang, Y Bai

Bibliographic record

VenueWhite Rose Research Online (University of Leeds, The University of Sheffield, University of York) · 2018
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
FundersShenzhen UniversityKunming University of Science and TechnologyNational Natural Science Foundation of ChinaChongqing UniversityUniversity College LondonQueen's University BelfastEngineering and Physical Sciences Research CouncilUniversity of LeedsQueen's UniversityState Key Laboratory of High Performance Civil Engineering Materials
KeywordsPortland cementCorrosionChlorideCementitiousMaterials scienceCementMetallurgySlag (welding)Composite material
DOInot available

Abstract

fetched live from OpenAlex

It is reported that the diffusion of chlorides in Alkali-activated slag (AAS) concretes is lower than that in Portland cement (PC) counterparts and is comparable to concretes containing high volumes of supplementary cementitious materials. This is considered to be due to its dense calcium silicate hydrate structure and relatively better chloride binding capacity due to its high alumina content. However, a critical review of the literature indicated that both the resistance to chloride ingress and chloride-induced corrosion of steel in AAS concretes are not found uniformly in all publications. Further, less is known about the effect of mix proportions, including binder content, water-binder ratio, role of activator, etc. on the rate of chloride transport through AAS concretes. As a consequence, there is conflicting information on the ability of AAS concretes to delay both the onset and the rate of corrosion of embedded steel in such concretes. Therefore, a thorough investigation was carried out focusing on their permeation properties and the corrosion behaviour in them. The results obtained from this research has illustrated that AAS concretes could achieve lower non-steady state diffusion coefficient and higher degree of chloride binding, resulting in improved corrosion resistance. However, there is a need to optimise mix proportions as there was a significant influence and interaction between both Na2O % and Ms of water glass used as activator for the AAS binder.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.070
GPT teacher head0.297
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2018
Admission routes1
Has abstractyes

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