Liquefaction potential index: A critical assessment using probability concept
Bibliographic record
Abstract
Liquefaction potential index (IL) was developed by Iwasaki et al. in 1978 to predict the potential of liquefaction to cause foundation damage at a site. The index attempted to provide a measure of the severity of liquefaction, and according to its developer, liquefaction risk is very high if IL > 15, and liquefaction risk is low if IL ≤ 5. Whereas the simplified procedure originated by Seed and Idriss in 1971 predicts what will happen to a soil element, the IL predicts the performance of the whole soil column and the consequence of liquefaction at the ground surface. Several applications of the IL have been reported by engineers in Japan, Taiwan, and the United States, although the index has not been evaluated extensively. In this paper, the IL is critically assessed for its use in conjunction with a cone penetration test (CPT)-based simplified method for liquefaction evaluation. Emphasis of the paper is placed on the appropriateness of the formulation of the index IL and the calibration of this index with a database of case histories. To this end, the framework of IL by Iwasaki et al. is maintained but the effect of using different models of a key component in the formulation is explored. The results of the calibration of IL are presented. Moreover, use of IL is extended by introducing an empirical formula for assessing the probability of liquefaction-induced ground failure.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".