Optimizing Magnitude Scaling Relationships in Simplified Liquefaction Triggering Procedures Using Ground Motion Intensity Measures
Bibliographic record
Abstract
The magnitude scaling factor (MSF) accounts for the number of equivalent uniform loading cycles (Neq) in the simplified liquefaction triggering procedures from different magnitude earthquakes. Traditionally, MSF relationships have been developed based on earthquakes with magnitudes up to 8.0, primarily from shallow crustal sources. However, the effectiveness of these models for intra- and inter-plate subduction zone earthquakes has not been rigorously evaluated. This study critically examines existing magnitude versus number of uniform equivalent cycles (Mw–Neq) relationships for subduction zone earthquakes, utilizing data from the NGAWest2 and NGASub ground motion databases. It is shown that current Neq and resulting MSF relationships tend to overestimate the number of cycles for interplate subduction events in the moderate to high magnitude range, and intraplate earthquakes contain more loading cycles than current relationships predict. To address these discrepancies, an improved regression model for Neq that accounts for variability due to regional differences and earthquake source-type dependencies is introduced. This model integrates ground motion intensity measures of cumulative absolute velocity and peak ground velocity, each normalized by peak ground acceleration to enhance accuracy and offer a source-type agnostic framework for liquefaction triggering assessments. The proposed Neq model supports the development of the ground motion factor (GMF), which replaces the traditional MSF by extending beyond sole dependence on earthquake magnitude. The GMF, along with its associated Neq model, is designed to be compatible with the current triggering framework, and its adoption is recommended for complex earthquake source types.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".