Investigation of the Performance of Simplified Constitutive Models in Nonlinear 1D Effective Stress Ground Response Analysis
Bibliographic record
Abstract
ABSTRACT Several building codes, such as the National Building Code of Canada, recommend that an effective stress ground response analysis be performed if a liquefiable stratum is identified within a soil profile. Although, constitutive models for total stress ground response analysis have been well verified against earthquake recordings, existing models for effective stress ground response analysis have yet to be thoroughly validated. This article investigates the predictions of five pore pressure models derived for effective stress ground response analysis. First, a dataset of five downhole arrays and two centrifuge experiments in which a potential of liquefaction was identified is presented. The profiles and ground-motion recordings are selected to represent a broad range of soil properties, ground-motion intensities, and excess-pore pressure generation levels. The differences between predictions of the effective stress models against commonly used 1D ground response total stress equivalent-linear and nonlinear analyses are evaluated. The predicted and measured motions are compared in terms of spectral response and amplification factor. The pore pressure response of all models is evaluated as a function of shear strain and found to agree well with published correlations representing the expected behavior of liquefiable soils. Although, the models show the ability to capture liquefaction triggering, the results indicate that for the selected dataset, total stress simulations were found to be, at least, as precise and accurate as the effective stress simulations. Therefore, simplified models for effective stress ground analysis should be used with caution by practicing engineers to predict surface spectra but can be used confidently to assess the potential for liquefaction triggering.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".