Incorporating region-variability of model bias into liquefaction-triggering procedures for sandy and gravelly soils through BUS-powered hierarchical Bayesian updating
Bibliographic record
Abstract
The accuracy of cyclic stress-based liquefaction-triggering assessment procedures can vary systematically from region to region, but it is challenging to regionalize models due to the lack of region-specific data. This paper presents a hierarchical Bayesian modeling (HBM)-based framework for incorporation of inter-region and intra-region variabilities of the bias factor in liquefaction-triggering procedures. A key feature is that the BUS approach (Bayesian Updating with Structural reliability methods) is combined with subset simulation to efficiently update high-dimensional statistics of bias factors. Another feature is a new four-hyperparameter HBM considering both region-specific means and variances of bias factors. This framework is utilized to develop three sets of region-specific liquefaction probability models for practical applications, covering liquefaction-susceptible sandy and gravelly soils. The results show that the four-hyperparameter HBM generally matches better with liquefaction observations and produces larger total variance, compared to the lumped-region modeling and the HBM with only region-specific means. Meanwhile, the population-level distribution and the weighting factor of liquefaction/non-liquefaction occurrence can considerably affect model performance. Furthermore, a discrete integration-based probabilistic method is suggested for liquefaction-triggering hazard assessment. Illustrative examples indicate that different HBM configurations can yield notably different liquefaction hazard results while neglecting the region-variability tends to be unconservative.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".