Quantifying reliability of liquefaction severity map developed from sparse cone penetration tests
Bibliographic record
Abstract
The liquefaction potential index (LPI) is widely used for evaluating the severity of liquefaction manifestation at the ground surface (e.g., settlement, lateral spreading, sand boils, and crack) and for developing liquefaction severity maps. Over the last two decades, several methods, such as cumulative probability distribution of LPI and geostatistics-based LPI mapping, have been proposed to develop a liquefaction severity map from in situ tests (e.g., cone penetration tests, CPT), which are often sparsely performed in sites. These methods are either based on an assumption of statistical homogeneity within each geologic unit or a stationary Gaussian model. However, subsurface soils frequently show significant spatial variability and LPI data obtained at different geological units usually exhibit non-stationary characteristics. More importantly, existing methods offer little insight into the reliability level of the obtained liquefaction severity map. To address these issues, this study proposes a non-parametric and data-driven method for CPT-based liquefaction severity mapping and, for the first time ever, quantification of the liquefaction severity maps’ reliability level using the probability of mis-predicting liquefaction severity from the map. Both synthetic and real-life data are used to demonstrate and validate the proposed method. The illustration examples indicate that the proposed method can properly deal with non-stationary LPI data from different geological units and quantify the mis-prediction probability of liquefaction severity at each point of the map.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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) | 0.001 | 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".