New Analysis Methodology for Dynamic Soil Characterization Using Free-Decay Response in Resonant-Column Testing
Bibliographic record
Abstract
The resonant-column test is widely used for dynamic characterization of soils under different confinement and shear-strain conditions. The free-decay response of a soil specimen in resonant-column testing shows a nonlinear behavior when the strain levels exceed a threshold shear strain. However, the dynamic parameters are commonly determined assuming a linear single-degree-of-freedom (SDOF) model. This paper presents a new analysis methodology based on the complex exponential method (CEM) for the nonlinear dynamic characterization of soil specimens using the free-decay response in torsional fixed-free resonant-column testing. This analysis methodology represents a solution to the chronic problem of evaluating the dynamic properties of soils while avoiding the negative effects of imposing many loading cycles at strain levels greater than the threshold shear strain, which, in turn, change the original dynamic properties of soil specimens. The effectiveness of the proposed analysis methodology is demonstrated on a dry-sand specimen tested under isotropic loading and unloading conditions, two confining pressures (40 and 100 kPa), and different shear-strain levels (1.34×10−5≤γ≤1.52×10−3). The results from the CEM are compared with the results from traditional linear SDOF methods (transfer function and free-decay response). Damping ratios obtained from the CEM are also compared with values obtained using the empirical-mode-decomposition–Hilbert-transform (EMD-HT) method. The results show that the damping ratio can be underestimated up to 80% at strain levels greater than the threshold shear strain when computed using linear SDOF models. In a nonlinear free-decay response in resonant-column testing analyzed using the CEM, it is possible to observe that the dry-sand specimen becomes denser (stiffer). Thus the shear-wave velocity (resonant frequency) increases at the low shear-strain levels imposed during the same test.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".