Using crack face displacement to measure stress intensity: A practical approach
Bibliographic record
Abstract
This study presents a novel method for estimating the stress intensity factor (K) using direct measurements of crack face displacements. Starting from Westergaard’s analytical solutions, modifications were derived from adapting these equations for finite-width bodies in five different geometries, including centre crack plates, edge crack plates (with both single and double cracks), plates containing angled cracks, and cracks in three-point bend specimens. Finite Element Analysis (FEA) was used to determine the profile of crack face displacement at different points along each crack. It was found that for centre-cracked plates, Westergaard’s equation worked well with only slight correction needed, whilst for edge-cracked geometries, a different equation was needed to describe the displacement profile. Unlike conventional methods that require applied load or local stress–strain data, this approach provides a simple and practical means of estimating K using optical measurements of crack face displacement. The proposed equations correctly predicted K with errors less than 10% for all geometries considered. To demonstrate the practical use of this method, an experimental study was conducted to estimate the fracture toughness (K IC ) of leaf specimens using crack face displacement measurements. The results were within 3% of those obtained from conventional laboratory tests, confirming the feasibility of this approach for real-world applications. Other potential applications to different materials and structures were also proposed. These findings establish crack face displacement as a reliable parameter for fracture analysis, offering potential applications in material testing, non-destructive evaluation, and structural health monitoring.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".