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Record W4392193517 · doi:10.56042/ijems.v30i6.502

Regression and Cluster Analysis of GGBS based geopolymer composite at different proportion of Ceramic Dust

2023· article· en· W4392193517 on OpenAlexaff
Arun Kumar Parashar, Ajay Kumar, Nakul Gupta, Kuldeep K. Saxena, Rakesh Chandrashekar, Vinayak Malik, Dilsora Abduvalieva

Bibliographic record

VenueIndian Journal of Engineering and Materials Sciences · 2023
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced ceramic materials synthesis
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsGeopolymerComposite numberCluster (spacecraft)Regression analysisCeramicGround granulated blast-furnace slagMathematicsComposite materialMaterials scienceStatisticsFly ashComputer science

Abstract

fetched live from OpenAlex

In compare to Portland cement, geopolymer have much lower CO2 emissions, which led to growing interest in their use as an environmentally sustainable binder. The investigation of the strength in compression and durability characteristics of geopolymer composite produced at various calcined clay and ground granulated blast furnace slag (GGBS) proportions (upto 50:50) with 12M of sodium hydroxide and ratio of sodium silicate to sodium hydroxide as 2. The strength of the produced composites was evaluated after 7, 28, 56 and 90 days of ambient air curing. The durability characteristics were evaluated using Rapid Chloride Permeability Test (RCPT), acid and sulfate attack using 5% MgSO4 and 5% H2SO4 solutions respectively and for the integrity using ultrasonic pulse velocity (UPV) test. Test results showed that the developed GPC (Geopolymer composite) has several advantages over standard concrete. The strength in compression (MPa) of the SC100 (standard concrete) as compared to Geopolymer Concrete in Compressive strength was increased by replacing GGBS with calcined clay up to 10%. At 56 and 90 days, the compressive strength of 10% calcined clay samples were improved by 20.15% and 21.60% respectively as compared to the SC100. Correlations showed that strength is having a strong relationship with the chloride ion permeability and the pulse velocities.

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 score0.023
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.243
Teacher spread0.232 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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