Extended finite-element analysis of fractures in concrete
Bibliographic record
Abstract
In this paper an extended finite-element method (X-FEM) that is fully compatible with standard FE program has been formulated based on a virtual node technique. A cohesive crack model that is appropriate for concrete fracture under mixed-mode loading has been integrated into the formulation. The proposed method was implemented into a commercial FE program as a user subroutine, and two benchmark experimental tests were successfully modelled. The numerical robustness and predictive power of the proposed method have been demonstrated by its excellent predictions on arbitrary crack evolution and the associated load–displacement curves. Detailed numerical investigation of the crack wake shielding effects on the fracture loading curves showed that: (a) crack wake shear shielding has little effect on the peak fracture load and its immediate neighbouring softening phase; (b) the initial shear cohesive stiffness has a significant influence on the descending slope of the softening part of the load–CMSD curve; and (c) the shear cohesive strength appears to make the softening phase more stable and to delay the abrupt fracture point.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".