Challenges in NorSand to model CSD stress paths and proposed modifications
Bibliographic record
Abstract
NorSand is a widely used constitutive model in geotechnical engineering. This study identifies challenges in simulating stress-relief stress paths, such as constant shear drained (CSD) loading, using NorSand, and proposes modifications to address them. These stress paths are particularly relevant in the assessment of dams and tailings storage facilities, as evidenced by case history failures. An experimental dataset of triaxial and CSD tests on a mine tailings material is used to highlight stress-relief mechanisms and contextualize the challenges associated with the flow rule, hardening rule, and the planar inner cap geometry in the standard NorSand model. The proposed modifications, informed by experimental observations on instability onset and strain evolution during CSD tests, introduce a new inner cap geometry, a modified flow rule, and a refined hardening rule. Their effectiveness is evaluated through comparisons between experimental and numerical responses, demonstrating that the updated model reproduces the experimentally observed patterns. Additionally, the performance of the updated NorSand model in a system-level simulation of a dam subjected to a rising water table, a stress-relief scenario, is also illustrated, further highlighting the role of the proposed modifications. More broadly, this study contributes to performance-based assessments of dam systems, aligning with modern engineering standards.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 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".