1g Modelling of Lateral Deformation of 2×2 Short Pile Group Foundations in Liquefied Sand
Bibliographic record
Abstract
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 2×2 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".