Probabilistic Liquefaction Potential of Kathmandu Valley Soil Based on Standard Penetration Tests
Bibliographic record
Abstract
Abstract Despite being a liquefaction susceptible zone, Kathmandu Valley soil in Nepal has limited studies on liquefaction potential and most of them are based on the deterministic approach. Although this method is widely used, it ignores the uncertainties of seismic parameters such as peak ground acceleration,amax,and earthquake magnitude,Mwas well as the inherent variabilities of soil layers, in-situ testing procedures, and geotechnical properties. On the other hand, the probabilistic approach helps assess the liquefaction potential by considering all these uncertainties. In this study, we assess the liquefaction hazard in the Kathmandu Valley using the first-order second-moment (FOSM) method as a probabilistic approach for liquefaction hazard assessment. The assessment is done for three likely-to-recur scenario earthquakes utilizing the geotechnical data of 1510 boreholes. The soils are characterized geotechnically to further assess susceptibility criteria of liquefaction in the valley. The assessment reveals that the central part of the valley is more vulnerable to liquefaction than other parts and the liquefaction probability increases with increasing depth up to 9 m, after which has geared down the value. Moreover, a relationship between the probability of liquefaction (PL) and the factor of safety (FS) against liquefaction is established. The hazard maps prepared for different earthquake scenarios can be useful for future infrastructure planning in Kathmandu Valley.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| 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".