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Record W3118018645 · doi:10.5539/mas.v15n1p53

Using Fine Silica Sand and Granite Powder Waste to Control Free Swelling Behavior of High Expansive Soil

2020· article· en· W3118018645 on OpenAlexvenueno aff
Manal O. Suliman, Abdulrazzaq Jawish Alkherret

Bibliographic record

VenueModern Applied Science · 2020
Typearticle
Languageen
FieldEngineering
TopicGrouting, Rheology, and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsExpansive clayAtterberg limitsSubgradeGeotechnical engineeringSwellingCuring (chemistry)SwellExpansiveSoil waterEnvironmental scienceMaterials scienceSoil testSoil scienceGeologyCompressive strengthComposite material

Abstract

fetched live from OpenAlex

Many researchers have been interested in studying the effect of adding local natural materials or construction waste on the properties of poor subgrade soil. However, changes in size and strength of expansive soils can cause extensive damage to the geotechnical infrastructure. This damage is often repeatable and latent in the long term, and is a critical issue in highway subgrade engineering. This paper examines the effect of adding both Fine Silica Sand (FSS) and Granite Cutting Powder Waste (GPW) materials on the welling characteristics of expansive soils. Atterberg limits, free swell index, and rate of swell of the mixtures were used as a key to assess properties of a group of expansive soil samples after adding different percentages of the mentioned materials. The rates of additions were 10%, 20%, 30%, 40%, 50%, 60 and 70% of the weight of the soil samples. The test results showed that FSS and GPW significantly affect the expansive soil properties. However, adding 70% of both FSS and GPW reduced the swelling index from 58.3% to 6.6% and from 58.3% to 11% after 7 days of curing, respectively. This study suggests that the Fine Silica Sand and Granite Powder Waste can be used as stabilizers for expansive highly plastic soils.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.019
GPT teacher head0.217
Teacher spread0.198 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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