A damage zone detection method in concrete hydraulic structures based on multi-frequency ultrasonic characteristics
Bibliographic record
Abstract
To avoid severe threats to the safety of people's lives and property caused by the ultimate collapsing of damaged concrete hydraulic structures and to overcome the technical bottleneck related to the low precision level of conventional acoustic non-destructive testing methods in distinguishing between different structural characteristics of damaged areas, this article focuses on the shortcomings in accuracy and detection capability of current acoustic technologies for damage detection in concrete structures. Different damaged areas of concrete structures exhibit distinct characteristics of frequency acoustic signals, and these multi-frequency ultrasonic characteristics were studied in this research to improve the detection method of damaged areas in concrete hydraulic structures. First, a damage area detection model that can synchronously reflect the non-smooth surface and multi-layer structural characteristics of the damaged area was established based on the real-state characteristics of concrete damage areas, providing a theoretical basis for the fine detection of the internal characteristics of concrete structures. Subsequently, an acoustic response feature function for the damaged area was constructed based on the multi-frequency information of acoustic signals, constituting an acoustic response feature extraction method that can effectively distinguish among multiple reflected echo signals. At the same time, by introducing the concept of the damage area recognition feature quantity, a structural damage area recognition method was formed to effectively distinguish between non-damaged and damaged areas in concrete structures, altogether encircling a complete set of the hydraulic concrete structure damage area detection technology system. Finally, the feasibility and superiority of the proposed method were verified through local and global testing experiments. The results indicated that the method proposed in this paper can improve the accuracy and efficiency of detecting damaged areas in concrete structures. The theoretical error was below 10%. The proposed method exhibited stronger adaptability, providing more effective and accurate diagnostic methods for assessing the current state of damaged areas in concrete structures in practice.
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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".