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Record W4410904271 · doi:10.18280/acsm.490211

Optimization of Bearing Capacity of Sandy Soil Using Geosynthetic: An Experimental Study on Sand Types and Reinforcement Material Variations

2025· article· en· W4410904271 on OpenAlexvenueno aff
Fitridawati Soehardi, Abdul Hakam, Rendy Thamrin, Mas Mera

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2025
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
Fundersnot available
KeywordsReinforcementGeotechnical engineeringBearing capacityBearing (navigation)GeologyGeosyntheticsEnvironmental scienceSoil scienceMaterials scienceComposite materialComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Sand is commonly used as a base material in construction; however, its non-cohesive nature and high deformability make it less ideal for directly supporting structural loads.One of the solutions developed to address this limitation is the use of geosynthetic materials for soil reinforcement.This study aims to analyze the influence of sand gradation (coarse, medium, fine) and types of geosynthetics (woven geotextile, nonwoven geotextile, and geogrid) on the bearing capacity of sand, with geosynthetics placed at a fixed depth of 5 cm.The testing was conducted using a plate load test within a smallscale laboratory model box.Results indicate that the combination of coarse sand and geogrid produced the highest bearing capacity, increasing from 161.46 kg (unreinforced) to 261.63 kg.Woven and non-woven geotextiles also improved bearing capacity, albeit with lower effectiveness.The stress-strain graph shows that the use of geosynthetics enhances soil stiffness and reduces deformation.The ultimate bearing capacity versus settlement graph confirms that geogrid and woven geotextile are effective in maintaining structural performance up to a 1-inch settlement.Overall, geosynthetics proved to be effective, particularly in coarse sand, with a bearing capacity increase of up to 61.33%.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.342

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 head0.033
GPT teacher head0.271
Teacher spread0.239 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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