Evaluating seismic liquefaction potential using shear wave velocity using machine learning
Bibliographic record
Abstract
Liquefaction investigations use a variety of approaches based on field and laboratory tests.The paper describes a study designed to determine the probability of soil liquefaction in a region covering Turkey and Iraq.We used machine learning approaches, particularly Random Forest (RF) models, to build and test models to estimate the chance of liquefaction, with shear wave velocity playing a critical role.In addition, earthquake magnitude and peak acceleration were considered important variables.The dataset includes soil attributes such as effective vertical stress (v0), soil type, shear wave velocity (Vs), and earthquake parameters including peak horizontal acceleration (PGA) and magnitude (M), allowing for the computation of liquefaction risk as actual values.The results indicate that Random Forest predicted soil liquefaction potential with a remarkable 92.5% accuracy using only 20% of the dataset.This study adds to the progress of risk assessment approaches in earthquake-prone locations, hence improving infrastructure resilience and catastrophe protection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".