Estimating liquefaction-induced ground settlements from CPT for level ground
Bibliographic record
Abstract
An integrated approach to estimate liquefaction-induced ground settlements using CPT data for sites with level ground is presented. The approach combines an existing CPT-based method to estimate liquefaction resistance with laboratory test results on clean sand to evaluate the liquefaction-induced volumetric strains for sandy and silty soils. The proposed method was used to estimate the settlements at both the Marina District and Treasure Island sites damaged by liquefaction during the Loma Prieta, California, earthquake on 17 October 1989. Good agreement between the calculated and measured liquefaction-induced ground settlements was found. The major factors that affect the estimation of liquefaction-induced ground settlements are also discussed in detail. The recommendations for taking the effects of these factors into account in estimating liquefaction-induced ground settlements using the proposed CPT-based approach are presented. It is suggested that the proposed method may be used to estimate liquefaction-induced settlements for low- to medium-risk projects and also to provide preliminary estimates for higher risk projects.Key words: liquefaction, settlements, earthquake, sand, in situ testing.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".