Detecting the Defects in Concrete Components with Impact-Echo Method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it