Calibration of strain-softening constitutive model parameters from full-field deformation measurements
Bibliographic record
Abstract
Many offshore geotechnical problems—such as cyclic T-bar penetration and lateral buckling of pipelines—are affected by the tendency of fine-grained soils to strain-soften due to remoulding. Careful calibration of the constitutive model parameters that control strain softening is essential for accurate simulation of these processes. This is usually achieved by matching constitutive model response with standard element test data, which implicitly assumes that homogeneous stress/strain fields exist within the sample. However, popular element test protocols (e.g., triaxial, simple shear) cannot apply sufficient deformation—at least as measured at the boundaries—to achieve fully remoulded conditions. This work explores the potential to determine strain-softening parameters experimentally, directly from image-based full-field deformation measurements and external loading data. Artificial data generated from finite element simulations of biaxial compression and T-bar penetration tests, using a non-locally regularised strain-softening constitutive model based on Modified Cam Clay, are then used to demonstrate the potential of the proposed technique. The results demonstrate significant potential for the application of the technique to identify constitutive parameters from full-field measurements even when polluted with modest measurement noise.
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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 | 0.001 |
| 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".