Combining RMU, Annual Survey, and Groundbed System Design Data to Predict Capital Replacement Needs
Bibliographic record
Abstract
Abstract We present a procedure to estimate the remaining service life of an impressed current groundbed using data analytics techniques to combine the various manually and remotely collected measurements, and rectifier groundbed metadata. Multiple data sources, including commissioning data, annual survey data, remote monitoring data were combined to create a consolidated dataset spanning over ten years for each impressed current cathodic protection (ICCP) rectifier groundbed. Using advanced analytics algorithms applied to ICCP rectifier groundbed sites, weighting factors have been calculated to determine the influence of each dataset on the estimated groundbed remaining service life. This work builds on previous presentations at AMPP Corrosion 2021 and 2022, where seasonality and historic trends in rectifier resistance measurements were modelled. By including additional datasets describing the location at the time of installation and the historic survey measurements, a more robust prediction can be made.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".