Evaluation of flexural fracture toughness for quasi-brittle structural materials using a simple test method
Bibliographic record
Abstract
As new structural concepts such as partial prestressing and steel-free bridge decks are more widely accepted and used, there is an increasing need for a reliable and reproducible fracture performance criterion that can describe resistance to crack growth. The required criterion should also be easy to determine experimentally so that it can be incorporated in structural specifications. The nonlinear behaviour of concrete and masonry materials suggested that quasi-brittle fracture mechanics approaches may be the most suitable for determining their fracture performance. The effective elastic crack model originally developed by Karihaloo and Nallathambi (1989) was modified to evaluate the critical crack depth under pure flexural stresses. A computer program was developed to calculate this depth iteratively from the experimental results. An experimental programme examining the fracture performance of four different structural materials (high performance concrete, mortar, fibre reinforced concrete, and masonry units) was carried out to examine the applicability of the model. As no post-peak data are required for the analysis, the model allows the use of a simple test setup to evaluate the fracture performance of quasi-brittle materials experimentally.Key words: fracture toughness, linear elastic fracture mechanics (LEFM), elastoplastic fracture mechanics (EPFM), quasi-brittle fracture mechanics, effective elastic crack, high performance concrete, masonry, fibre reinforced concrete.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".