Methodology for the Early Detection of Damage Using CEEMDAN-Hilbert Spectral Analysis of Ultrasonic Wave Attenuation
Bibliographic record
Abstract
Current non-destructive testing (NDT) methods, such as those based on wave velocity measurements, lack the sensitivity necessary to detect early-stage damage in concrete structures. Similarly, common signal processing techniques often assume linearity and stationarity among the signal data. By analyzing wave attenuation measurements using advanced signal processing techniques, mainly Hilbert-Huang transform (HHT), this work aims to enhance the early detection of damage in concrete. This study presents a novel energy-based technique that integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and Hilbert spectrum analysis (HSA), to accurately capture nonlinear and nonstationary signal behaviors. Ultrasonic non-destructive testing was performed in this study on manufactured concrete specimens subjected to micro-damage characterized by internal microcracks smaller than 0.5 mm, induced through controlled freeze-thaw cycles. The recorded signals were decomposed from the time domain using CEEMDAN into frequency-ordered intrinsic mode functions (IMFs). A multi-criteria selection strategy, including damage index evaluation, was employed to identify the most effective IMFs while distinguishing true damage-induced energy loss from spurious nonlinear artifacts or noise. Localized damage was then analyzed in the frequency domain using HSA, achieving an up to 88% reduction in wave energy via Marginal Hilbert Spectrum analysis, compared to 68% using Fourier-based techniques, demonstrating a 20% improvement in sensitivity. The results indicate that the proposed technique enhances early damage detection through wave attenuation analysis and offers a superior ability to handle nonlinear, nonstationary signals. The Hilbert Spectrum provided a higher time-frequency resolution, enabling clearer identification of damage-related features. These findings highlight the potential of CEEMDAN-HSA as a practical, sensitive tool for early-stage microcrack detection in 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".