A novel clustering based curvature method with wavelet transform for detecting progressive damage of simply supported ultra-high performance fiber-reinforced concrete beams using laser scanner vibrometer
Bibliographic record
Abstract
Monitoring the structural performance of Ultra-High-Performance Fiber-Reinforced Concrete (UHPFRC) beams is essential for understanding damage progression and improving long-term durability in advanced structural elements. This study presents a novel reference-free clustering-based algorithm that leverages high spatial resolution and higher vibrational modes obtained using a laser scanning vibrometer (LSV) during a progressive damage test on UHPFRC. In the experimental phase, the vibrational behavior of a 2-meter one-span simply-supported UHPFRC beam was measured by laser scanning vibrometry under progressively increasing damage levels. Two different reference-free damage detection methods are developed and compared. In the first method, the curvature is calculated using the central difference approximation, and a clustering-based algorithm combining LoOP outlier detection with locally weighted nonparametric regression fitting using a second-order polynomial (LOESS) is applied to construct an intact baseline directly from the damaged data. In the second method, a wavelet-based curvature formulation is introduced to overcome the noise sensitivity and boundary effects inherent in central difference schemes. Both approaches are evaluated for their ability to identify damage zone characteristics, such as position and severity, and are validated through finite element simulations where strain energy variation is used as a numerical severity index.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".