Simplified Uniform Hazard Liquefaction Analysis for Bridges
Bibliographic record
Abstract
Many bridge designers currently rely on the design specifications provided by AASHTO to evaluate liquefaction hazards for foundations and embankments. These guide specifications recommend a pseudo-probabilistic approach (i.e., an approach in which probabilistic estimates of seismic loading are incorporated into a deterministic liquefaction hazard analysis) for most conventional projects. A more consistent approach for the assessment of liquefaction hazards would involve a performance-based or uniform hazard evaluation of liquefaction potential. However, a performance-based liquefaction analysis is complex and difficult to implement in practice. This paper describes a simplified procedure for uniform hazard liquefaction analysis that uses the standard penetration test to closely approximate the results of a performance-based liquefaction analysis at a desired uniform hazard level. The simplified procedure is compatible with current AASHTO load and resistance factor bridge design specifications and is intended for use on bridges and other transportation-related structures. The procedure is summarized and shown to produce more consistent results for liquefaction hazards across different seismic environments than the existing AASHTO pseudo-probabilistic procedure. An example application of the simplified procedure is presented and discussed, and limitations for the procedure's proper use are provided.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.004 | 0.001 |
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".