Detecting the Defects in Concrete Components with Impact-Echo Method
Bibliographic record
Abstract
With the large-scale application of the prestressed concrete structure, the quality of the concrete component defects and pipeline grouting has increasingly become the focus of attention. The impact-echo scanner uses the nature of wave, which pass though different media at different velocities, to distinguish internal defects of concrete, pipe filling density and so on. In this paper, using the impact-echo method to detect the concrete block with prefabricated defects of shape, location, and size explores the effect of defect properties, parameter settings and detection environment to impact-echo preliminarily and also explores the relationship of pipeline filling status and impact-echo image. Based on this study, the article raised the problem met during this non-destructive testing methods applied to engineering, and accumulated a certain amount of available engineering data. The experiment results show that using the impact-echo method to identify the defects of concrete components and to test the quality of pipeline grouting is a more convenient and effective non-destructive testing method. Especially, with the radar method in the pipeline grouting quality inspection which complement each other to make up for the shortcomings the lightning wave in case of the metal medium total reflection phenomenon, cannot detect metal pipe grouting plumpness.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".