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Record W3118227520 · doi:10.21608/erjeng.2020.131488

Improvement of Expansive Soil by Using Micro Silica Fume

2020· article· en· W3118227520 on OpenAlexaff
Mohamed Mugahed Sakr, Waseim Azzam, Mohamed A. Meguid, Hebatalla Ghoneim

Bibliographic record

VenueJournal of Engineering Research - Egypt/Journal of Engineering Research · 2020
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsMcGill University
Fundersnot available
KeywordsExpansive claySilica fumeExpansiveGeotechnical engineeringMaterials scienceEnvironmental scienceComposite materialGeologyCementSoil waterSoil scienceCompressive strength

Abstract

fetched live from OpenAlex

Expansive soil shows frequent volume changes with the changes in the moisture content, causing severe problems to the civil engineering structures. Consequently, the measurements of swelling properties including free swell index and swelling pressure are extremely important. Several attempts are being made all over the world to control the swell-shrink behavior of expansive soils. Many researches have investigated how to overcome the problems of such soils by means of using different additives such as cement, lime, steel fibers, stone dust and fly ash. This study is directed towards the improvement of expansive soil with a new, inexpensive and environmentally friendly additive. In this study, the effect of using micro silica fume to stabilize the soil was investigated through a laboratory study. Test results showed that, the micro silica fume can considerably decrease the free swell index value by 69% at 25% micro silica fume content. Also, the swelling pressure is reduced from 410 kN/m2 to nearly 330 kN/m2 and 302 kN/m2 at micro silica fume content of 5% and 25%, respectively. This demonstrates the effectiveness of the proposed addition in the expansive soils improvement. This improvement technique can be used in different civil engineering construction projects including slope stabilization and road embankments.

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)0.006
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
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 score0.167
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.005
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 head0.057
GPT teacher head0.323
Teacher spread0.266 · 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.

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

Citations12
Published2020
Admission routes1
Has abstractyes

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