Condition Assessment of Cementitious Materials Using Surface Waves in Ultrasonic Frequency Range
Bibliographic record
Abstract
Abstract Surface waves propagating in a medium provide information about the mechanical properties and condition of the material. Variations in the material condition can be inferred from changes in the surface wave characteristics. Multichannel analysis of surface waves (MASW) is a well-established surface wave method used for determination of the shear-wave profile of the soil layers near the surface. The MASW test configuration is also applicable to assess the condition of construction materials using appropriate frequency range. Previous studies on the detection of surface-breaking cracks in concrete elements, using the dispersion and attenuation of ultrasonic waves, were successful; however, a complete damage assessment of the whole element was not in the scope of these studies. In this study, different wave characteristics, such as Rayleigh wave velocity, wave attenuation, and phase velocity dispersion, are investigated to evaluate their sensitivity to the damage in a medium. The condition of a test specimen, which is a half-space medium made of cement and sand, is evaluated using ultrasonic transducers for different damage cases. The recorded signals are processed using the Fourier and wavelet transforms to determine the surface wave characteristics. A new dispersion index (DI) is introduced, which represents the global correlation between the dispersion of phase velocity and damage level. All features are found to be capable of reflecting the damage in the test medium with different levels of sensitivity. Among the investigated parameters, the proposed dispersion index shows high sensitivity and linear correlation with the damage.
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 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".