Mesh Increment Methodology for Improving Concrete Ultrasonic Tomography
Bibliographic record
Abstract
Ensuring the longevity of concrete structures is crucial for increasing their lifespan, and nondestructive tests play a key role in this context. Ultrasonic testing, a widely used nondestructive method, is employed to evaluate the heterogeneity and stiffness of concrete elements. Through advanced ultrasonic signal analysis, ultrasonic tomography enables the internal visualization of a structure’s state. Despite the development of reliable image reconstruction techniques, improving image resolution remains a challenge, particularly when dealing with limited data and mesh density issues. This study presents a method named the mesh increment (MESINC) method to enhance image resolution by incrementally densifying the mesh, even with a small number of tests around a concrete element. Two image reconstruction techniques—the simultaneous iterative reconstruction technique and algebraic reconstruction technique—are applied for image generation. Numerical simulations and experimental concrete sections are used to validate the effectiveness of the proposed method compared to regular image generation methods. The results demonstrate that the proposed MESINC method reduces reconstruction errors, enhances image contrast, and improves the identification of internal inclusions with better-defined shapes and positions. The findings suggest that this methodology can contribute to more accurate diagnostics of concrete elements, offering potential for improved nondestructive testing in civil engineering applications.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".