Importance of coherence between geophysical and geotechnical data in dynamic response analysis
Bibliographic record
Abstract
The demand for a precise evaluation of shear wave velocity Vs, is gaining interest in the field of geotechnical engineering due to its importance as a key parameter required to properly evaluate typical characteristics of soils. Nowadays, Vsmeasurements are performed on the field using different methods, such as SCPT tests and various geophysical methods. However, the effectiveness of these field measurements is not guaranteed and rather depends on how they are analyzed. Furthermore, a proper analysis is critical since the collected data may be used in liquefaction evaluation or earthquake ground response analyses. In these situations, it is recommended to verify the coherence between the obtained geophysical (Vs) and geotechnical (N-SPT, qc-CPT) measurements using alternative methods (e.g., Vs-correlations, H/V method, etc...). In some situations, the correlation between the different measurements makes it easier to unambiguously define seismic wave profiles. In other cases, geophysical and geotechnical tests would provide different resolutions for Vsmeasurements, an issue that complicates the decision of the practitioner. In this paper, we first demonstrate the importance of the shear-wave velocity in liquefaction potential analysis. A case study performed in eastern Canada is also presented where we show the importance of the method used to calculate Vsprofiles (MASW, MMASW).
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 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".