Acoustic guided wave detection of grounding rod corrosion: equivalent circuit model and implementation
Bibliographic record
Abstract
Abstract Acoustic pulse-echo systems, are effective for corrosion damage detection in buried individual ground rods without any need for excavation. In electric power distribution systems there are large numbers buried ground rods are used in substations to form a grounding networks that is critical for the protection of equipment and personnel working in the vicinity of the substation. Over time, these grounding rods will corrode and cease to provide adequate protection. In this work, we present an equivalent circuit model (ECM) for acoustic pulse-echo corrosion detection systems. The circuit model was used to develop a transducer configuration that effectively launches longitudinal acoustic waves that can accurately detect position and damage severity. We show that our model correctly predicts the temporal response from both undamaged rods and rods with machined simulated corrosion pits. The circuit model also accounts for loss, and the results show strong agreement with simulated soil-loss in laboratory experiments. Evidence of wave mode conversion is found in experimental data that is not captured by the model. Preliminary field measurements of copper-clad steel grounding rods are presented that show strong similarities to modeled results. This ECM is a promising basis for further development of pulse-echo systems for corrosion detection and may be useful for future tomographic analysis of field measurements.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".