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Record W4221071625 · doi:10.18280/ijsdp.170116

Using High Reactivity Attapulgite for Stabilizing Collapsible Gypseous Soil

2022· article· en· W4221071625 on OpenAlexvenueno aff
Safaa A. Mohamad, Hadeel Ammar Mohammed, Hanan Adnan Hassan, Mohammed Y. Fattah

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2022
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
FundersMustansiriyah University
KeywordsGypsumCompactionAtterberg limitsPozzolanCompressive strengthGeotechnical engineeringMaterials scienceConsolidation (business)Environmental scienceComposite materialWater contentGeologyCement

Abstract

fetched live from OpenAlex

The presence of gypsum in soil has a high effect on the mechanical and physical properties of soil. This effect depends fundamentally on the type and quantity of gypsum existent in the soil. The present study attempts to improve the physical and mechanical properties of gypseous soil using a new type of pozzolanic material discovered as a pozzolanic material from attapulgite clay. It is called “High Reactivity Attapulgite” HRA. The objective of this study is to investigate the ability of this material to be a moderate stabilizer material used for treating soil. The soil was obtained from one location at depth 1 m with 28% gypsum content. High Reactivity Attapulgite (HRA) was used as a replacement with soil in different percentage (5%, 10%, 20%, and 40%). Experimental tests included Atterberg limits, modified compaction, strength, unconfined compression and consolidation. Then all the tests were conducted on specimens with HRA content that was varied in the range 5-40%. The results showed the important effect of HRA on gypseous soil, as was seen from the reduction in the collapse potential by about 13-90%, and the increase in the unconfined compressive strength from 88 kPa to 271 kPa upon the addition of HRA by 5-40%.

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 score0.266
Threshold uncertainty score0.449

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 head0.021
GPT teacher head0.247
Teacher spread0.227 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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