A Constitutive Model Controlling Damping for 2D and 3D Site Response
Bibliographic record
Abstract
Modulus reduction and damping curves are commonly used as input parameters in site response analyses. Until very recently, constitutive models for nonlinear site response analysis could not accurately capture both the desired modulus reduction and damping behavior due to flaws in the assumed functional form for the backbone curve, and/or flaws in the unload-reload relationship. New relationships that solve these issues have recently been proposed, however, they are restricted to defining the in-plane stress-strain relationship for a single plane of shear, and are therefore only appropriate for 1D site response. This paper presents a multi-axial generalization of one of these 1D models, enabling its use in 2D and 3D site response. The 1D model is first briefly described, followed by the multi-axial generalization in terms of stress and strain invariants. The modulus reduction and damping curves are defined in terms of stress ratios rather than shear strains, which allows the model to better capture changes in confining pressure during undrained loading. An example is presented to illustrate fundamental differences in multi-axial versus 1D loading.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".