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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".