Assessment of Remaining Useful Life of Pipelines Using Different Artificial Neural Networks Models
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
Water distribution networks have a significant effect on public health and safety. Recent reports state that the 21st century is estimated to be the end of effective life for most water distribution networks in the United States. It is essential to implement accurate and cost-effective models that can predict deterioration rates along with estimates of remaining useful life (RUL) of the pipelines, to perform necessary intervention plans that can prevent disastrous failures. This study presents a computational model that predicts the RUL of water pipelines using an artificial neural network (ANN) model that has been developed using the Levenberg-Marquardt backpropagation algorithm. The model is implemented, tested, and trained using data collected from the city of Montreal. Results show that pipeline age, condition, length, diameter, material, and breakage rate are the most important factors in the prediction of RUL. Because the model shows robustness and accuracy in estimating the RUL of water pipelines in the case study, it can be used to support the municipality of Montréal, Quebec, Canada, in future planning.
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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) | 0.001 | 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