Dynamic Characteristics of a Beam with a Shallow Crack Using Alternative Admissible Functions
Bibliographic record
Abstract
The presence of a crack in a beam leads to changes in its dynamic characteristics and hence changes in its natural frequencies and mode shapes. In this paper, Alternative Admissible Functions (AAF) with penalties for extracting the dynamic characteristics of a Euler–Bernoulli Beam with a shallow crack is proposed and validated. The proposed method has two key advantages. First, the alternative admissible function choice is independent of the boundary conditions, which are modelled via boundary penalty terms. Second, the crack is treated as a penalty function to account for the local stiffness reduction while ensuring beam continuity. The approach is validated with different crack depth ratios and locations. The mass, stiffness, and penalty function matrices for Simply Supported (SS), Clamped–Clamped (CC), and Clamped–Free (CF) are developed and are used in the analysis of a beam with a shallow crack. The proposed method demonstrates results in good agreement with published literature for shallow cracks. A significant advantage of the proposed method is the ease of applicability, eliminating the need for remodeling with changes in boundary conditions or crack parameters. The results show that the crack introduces asymmetry to the beam and may require changing the boundary penalty values, depending on the location and depth of the crack.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".