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Record W2984253369 · doi:10.1139/cgj-2019-0215

Systematic approach to assessing the applicability of fly-ash-based geopolymer for clay stabilization

2019· article· en· W2984253369 on OpenAlexvenueno aff
Hayder H. Abdullah, Mohamed A. Shahin, Megan L. Walske, Ali Karrech

Bibliographic record

VenueCanadian Geotechnical Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
Fundersnot available
KeywordsGeopolymerFly ashLimeSoil stabilizationCompactionDurabilityCompressive strengthGeotechnical engineeringCuring (chemistry)Soil waterEnvironmental scienceClay soilMaterials scienceGeologySoil scienceComposite materialMetallurgy

Abstract

fetched live from OpenAlex

Traditional soil stabilization by chemical additives such as cement and lime is a well-established technique for ground improvement of problematic soils. However, with the advantage of lower carbon emission and energy consumption, fly-ash-based geopolymer has recently become an attractive alternative to traditional stabilizers. Nevertheless, the literature lacks systemic approaches that assist engineers to apply this promising binder for soil stabilization, including the proper dosages required for an effective treatment. This paper introduces a systematic approach to assess the applicability of fly-ash-based geopolymer for stabilization of clay soils, through a comprehensive experimental program where engineered and natural clays were examined and evaluated, including soil compaction, plasticity, compressive strength, durability, pH level, and impact of pulverization. The results revealed several factors that influence the level of enhancement of geopolymer-treated clays, including the soil mineralogy, plasticity–activity properties, geopolymer concentration, curing time, and pulverization.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score0.311

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 head0.017
GPT teacher head0.259
Teacher spread0.242 · 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 designSimulation or modeling
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

Citations78
Published2019
Admission routes1
Has abstractyes

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