Natural Frequency of Liquefaction Potential Based on Soil Investigation and Microtremor Observation Results
Bibliographic record
Abstract
This study aims to identify the natural frequency threshold for liquefaction potential by comparing four assessment methods at 54 identical sites in Padang, Indonesia. Methods include: (1) safety factor calculations from soil investigation results (CPT and SPT) applying the 2009 Padang earthquake's peak ground acceleration as input for cycling stress ratio; (2) natural frequency measurements at the surface using microtremor single observations; (3) liquefaction potential assessment through vulnerability index; and (4) analysis of historical liquefaction events from the September 30, 2009 Padang earthquake documented in two previous research papers. The analysis focused on soil depths ranging from 1-4 m. Findings reveal that sites with natural frequencies exceeding 0.40 Hertz remain safe from liquefaction, while sites with frequencies between 0.20-0.39 Hertz demonstrate significant liquefaction potential. This research contributes to the field by establishing a clear correlation between measurable natural frequency thresholds and liquefaction risk, providing engineers and urban planners with a more accessible parameter for preliminary risk assessment. Integrating multiple assessment methods at identical sites enhances the reliability of the identified frequency thresholds, offering a more comprehensive approach to liquefaction hazard mitigation in earthquake-prone regions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".