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Record W4406018844 · doi:10.7764/ric.00129.21

The influence of Interground limestone fines and metakaolin on the electrical resistivity of portland-limestone concrete

2024· article· en· W4406018844 on OpenAlexaff
Zichun Xia, Leandro Sanchez

Bibliographic record

VenueRevista Ingeniería de Construcción · 2024
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPortland cementMetakaolinCementitiousElectrical resistivity and conductivityCementMaterials scienceClinker (cement)Compressive strengthComposite materialMetallurgyCorrosionEngineering

Abstract

fetched live from OpenAlex

The critical climate change has raised concern about the decarbonization of the cement and concrete industry, which is responsible for 8% of global CO2 emissions. Portland limestone cement (PLC), which is made by partially replacing the clinker with up to 15% interground limestone fines (LFs), has been recognized as a viable solution for its feasibility to match the engineering properties of ordinary portland cement (OPC). However, with the necessity of further increasing the LFs contents to meet the desired eco-efficiency, the dilution effect brought by less ultimate hydration products may be detrimental to the long-term performance of reinforced concrete structures, such as chloride-induced corrosion. Thus, this research explores the potential of combining PLC and alumina-rich supplementary cementitious materials (SCMs) to improve the resistivity of the concrete. Concrete specimens were fabricated with PLCs from two sources of three LFs replacement ratios (15, 20, and 25%). MK (8%) is used as the source of alumina. The bulk and surface resistivity results showed that combining PLC with MK can notably improve concrete resistivity even in mixtures with lower amounts of cement. Additionally, compressive strength demonstrated poor correlation with electrical resistivity, which highlights the significance of performance-based design.

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 score0.069
Threshold uncertainty score0.403

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)0.0000.000

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 head0.011
GPT teacher head0.254
Teacher spread0.243 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueRevista Ingeniería de ConstrucciónSame topicConcrete and Cement Materials ResearchFrench-language works237,207