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Record W4401954434 · doi:10.18280/mmep.110819

1g Modelling of Lateral Deformation of 2×2 Short Pile Group Foundations in Liquefied Sand

2024· article· en· W4401954434 on OpenAlexvenueno aff
Arief Alihudien, As’ad Munawir, Yulvi Zaika, Eko Andi Suryo

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2024
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersUniversitas Jember
KeywordsPileGroup (periodic table)Geotechnical engineeringDeformation (meteorology)GeologyChemistry

Abstract

fetched live from OpenAlex

The earthquake that occurred in Palu-Sulawesi Indonesia in 2018 has caused many problems to infrastructure buildings.One of the impacts of the earthquake was the reduction the level of hardness includes the level of stiffness saturated sandy and condition makes the foundation structure experience greater lateral deformation, which can lead to the collapse of the building above it.This phenomenon is called liquefaction.This article describes the results of laboratory simulations using a one-way shaking table.It aims to obtain the lateral resistance of a group of short pile foundations.The lateral resistance is investigated from the amount of lateral deformation of the pile cap.Laboratory modeling used field and laboratory comparisons at a scale of 1:10.Pile foundations are used in 22 pile groups.To obtain the lateral deformation of the pile, Optic Flow is used which is placed on top of the pile cap as high as 30cm.Meanwhile, to obtain the increase in pore water pressure, a PWP sensor was used which was inserted at a certain soil depth of 30cm from the ground surface.The test results show that the lateral deformation of the pile cap due to liquefaction can be observed well.The phenomenon of liquefaction can be observed the excess the pressure of pore water into the soil is caused by loading of seismic.Furthermore, observation results were compared through analysis using the Plaxis 3D program, which showed a good agreement.

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: none
Teacher disagreement score0.685
Threshold uncertainty score0.682

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 head0.022
GPT teacher head0.209
Teacher spread0.187 · 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
Published2024
Admission routes1
Has abstractyes

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