Determination of cohesive properties for mode I fracture from compacted clay beams
Bibliographic record
Abstract
Tensile fracture in mode I occurs in many geotechnical applications such as in slope stability, desiccation cracking, borehole pressuremeter testing, etc. The cohesive crack model is a powerful and versatile tool that can be used to numerically analyse mode I fracture, that has had very limited usage in geomechanics to date. This research reports findings from testing on single-edge notched beams manufactured from compacted clay fractured in three-point bending. Specimens were tested at a range of moisture contents to determine several fracture parameters including the parameters defining cohesive cracks. The properties for the cohesive crack were back-calculated by matching the numerically modelled load–load point displacement curve obtained using a hybrid continuum distinct element program with the ones obtained experimentally. It was found that the cohesive crack method could be successfully used in matching the load–load point displacement curves for a range of consistencies of the clay from soft to very hard. It is tentatively suggested that linear softening curves may be sufficient for modelling clay fracture, unlike for concrete, which typically displays distinctly bi-linear softening behaviour. Further research and testing along the discussion presented in this paper could be beneficial in numerically analysing geotechnical applications with mode I fracture.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".