Influence of Grain Size Distribution of Sand Lenses in Evaluating the Liquefaction Potential
Bibliographic record
Abstract
Sand lenses are susceptible to ground motion due to dynamic loads like earthquakes.Infrastructure can be damaged by liquefaction that occurs in saturated sand lenses.A study on the safety factor (SF) of liquefaction potential and immediate settlement (ΔSe) was carried out at an area of infrastructure development in Langsa, Indonesia.This region features many alluvial deposits, which have multiple fine to coarse sand lenses from loose to medium conditions and varying in thickness from 2.0 to 3.0 m.This research aims to evaluate the effect of grain size distribution (GSD) on sand lenses susceptible to liquefaction, as well as the extent of immediate land subsidence following liquefaction.Laboratory analysis of GSD allows for the calculation of the uniformity coefficient (Cu), fines content (FC), and curvature coefficient (Cc).It also helps in assessing the likelihood of liquefaction potential and ΔSe within the sand lens layer.The laboratory analysis conducted by GSD revealed that at the vulnerable sand lens layers situated 2.0 to 10.0 m beneath the current groundwater table, Cu value of 13 and Cc value of 0.31 were recorded, while the FC value was approximately 2%.This indicates an increased liquefaction potential as the SF diminishes.Prediction of SF values existed in the range from 0.66 to 1.49 and ΔSe from 2.12 to 88.07 mm.Probability of liquefaction (P[L]) existed in the range from 0% to 30%, with pore water pressure (u) reaching until 35% from the hydrostatic pressure during liquefaction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".