Optimal intensity measures for predicting coseismic liquefaction triggering and lateral displacements in sloping ground
Bibliographic record
Abstract
This paper identifies optimal ground motion intensity measures (IMs) for predicting coseismic liquefaction triggering and liquefaction-induced lateral displacements in sloping ground. Fully coupled nonlinear dynamic analyses of mildly sloping liquefiable soil columns are conducted in OpenSees using the SANISAND-MSf v2 soil constitutive model. For the identification of optimal IM, eighty-one soil profiles, with varying depth, thickness, and relative density of an embedded liquefiable layer, as well as different slope angles, are subjected to 950 crustal ground motions. Based on the criteria of efficiency, sufficiency, and predictability, modified acceleration spectrum intensity, followed by characteristic intensity and Arias intensity, are identified as optimal IMs for predicting the peak depth-averaged excess pore water pressure ratio, while cumulative absolute velocity is the optimal IM for the end-of-motion surface lateral displacement. In addition to conclusions drawn from scalar-valued outcrop-motion-based and total motion time histories, further evaluations assess other potential efficiency improvements. Vector-valued and within-motion-based IMs offer slight improvements in predictive efficiency, but the added complexity limits their practical advantage. A timing-based framework for isolating the post-triggering portion of evolutionary IMs improves liquefaction response prediction by 12%. However, its reliance on identifying the exact triggering time and applicability only to evolutionary IMs limits its practical use in hazard evaluations. The findings provide practical guidance for selecting efficient and implementable intensity measures to predict coseismic liquefaction triggering and its induced displacements in sloping ground. • Impact of soil profile variables and ground motion intensity is studied via NDAs. • MASI best predicts liquefaction triggering, followed by I c and I A . • CAV is the optimal IM for surface lateral displacements. • Vector-valued and within-motion-based IMs modestly improve prediction efficiency. • Post-triggering CAV improves prediction of lateral displacements by 12%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".