A comparative soil liquefaction analysis with a Matlab® based algorithm: soiLique
Bibliographic record
Abstract
Soil liquefaction is one of the ground failures induced by earthquakes. Determining the safety factor and the settlements are the most common analyses to decrease liquefaction-induced failures and hazards. Scientists have suggested numerous empirical formulas to detect and mitigate liquefaction-based hazards, and they have been used over the decades. This study aims to present a user-friendly and interactive program for deterministic soil liquefaction analyses. The algorithm presented in this study, soiLique, is the first MATLAB® program, including a graphical user interface that provides the deterministic liquefaction analysis with the computation of parameters propounded with the formulas. One of the advantages of soiLique is that it allows picking the physical property of every layer (i.e., fine or coarse), which provides dealing with liquefaction prone layer(s) directly when necessary. Not only can one calculate parameters regarding soil liquefaction with the help of this program, but one also can see graphically supported results. The robustness of soiLique is checked with another soil liquefaction analysis program, SoilEngineering, which was introduced by Ozcep (2010). Calculations were done separately using real SPT data and synthetic data such as VS measurements and CPT data. The real SPT data and synthetic VS data were used to compare soiLique and SoilEngineering (Ozcep, 2010). The present study presents an example of CPT data analysis but could not be used for comparison. Comparisons reveal that outputs of soiLique and results of SoilEngineering 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".