Monitoring pH Level Using High-Resolution Microwave Sensor for Mitigation of Stress Corrosion Cracking in Steel Pipelines
Bibliographic record
Abstract
Here, the feasibility of using a non-contact high-resolution microwave sensor for the detection and mitigation of High pH Stress Corrosion Cracking (HpHSCC) through monitoring the pH range beneath the dsibonded coating in buried pipeline steel has been investigated. A modelling method and microwave dielectric sensing for aqueous solution has been studied in order to relate relative permittivity and loss to the pH level using the concentration as a bridge. The experimental results showed the potential of the high quality-factor sensor for monitoring the pH level variation. The resonant frequency of the microwave sensor was the main variable considered in the characterization of the sensor's response to pH level changes or concentration variations in the defect beneath the pipe coating. Additionally, the extracted experimental results for near-neutral and acidic pH environment detection demonstrated the significance of non-contact sensing performance and the sensor's potential to detect and study the pH variation. The results demonstrate significantly distinct frequency shift of 174 kHz as the pH increases from 7 to 11 which is the range of the pH created in HpHSCC. This method, as an example, could be employed in the third stage of Stress Corrosion Cracking Direct Assessment (SSCDA), Direct examination step, to determine whether breeding ground for HpHSCC is created or not.
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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 | 0.000 |
| 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 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".