Robustness of Resonant Frequency Test for Strength Estimation of Concrete
Bibliographic record
Abstract
Abstract The resonant frequency test (RFT) of molded concrete specimens is a nondestructive test (NDT) method for indirect strength estimation based on the fundamental mode characteristic resonance frequency. In this article, experimental, numerical, and analytical studies were performed to assess the sensitivity of test results to various factors involved in RFT. These factors included vibration modes of the specimen, sensor attachment techniques, contact time of the hammer impact, location of the sensor on the specimen, length to diameter ratio (L/D), and cross-sectional shape of the specimen. Experimental study included concrete specimen preparation in the lab and real-time RFT measurements for robustness of some of the aforementioned factors. Computation models developed using numerical simulations were verified by laboratory test and analytical results. The finite element method–based code, ABAQUS, was employed for computational modeling. Statistical analyses of the experimental results and parametric studies of the computational modeling were used to quantify the effect of the aforementioned factors on uncertainties of strength estimates using RFT measurements. The contact time of the hammer impact and the L/D of the specimen were found to have considerable effect on test results and hence on the concrete strength estimates.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".