Probabilistic evaluation of earthquake-induced liquefaction using Bayesian network based on a side-by-side SPT–CPT database
Bibliographic record
Abstract
In situ tests such as standard penetration test (SPT) and cone penetration test (CPT) are often conducted to evaluate the probability of earthquake-induced liquefaction. However, the models adopted have seldom attempted to utilize SPT and CPT data simultaneously. In this study, a side-by-side SPT–CPT database at historical earthquake sites is established; then, a Bayesian network model is constructed to predict the probability of soil liquefaction based on this database, with which the SPT and CPT data are utilized simultaneously. Next, comparative studies are undertaken to illustrate the superiority of the Bayesian network-based probabilistic soil liquefaction model developed over other models, in terms of six SPT- and CPT-based conventional liquefaction models in the literature and two Bayesian network-based models. It should be noted that the liquefaction sites with two in situ tests are scarce and side-by-side SPT–CPT data can be incomplete, which leads to challenges in applying the Bayesian network model developed. To address this problem, correlations between SPT and CPT data are analyzed, and these correlations are further included in the Bayesian network model; as a result, a modified Bayesian network model is reached. Finally, the influence of the proportion of missing data in the incomplete SPT–CPT data on the liquefaction prediction accuracy is discussed.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".