Prediction of Sewer Pipelines Using Machine Learning Techniques: A Case Study on the City of Hamilton Sewer Network
Bibliographic record
Abstract
Several physical, environmental, and operational factors contribute to the deterioration of wastewater pipelines during their service lives. Failure of this critical infrastructure would have repercussions on the economy, society, and the environment. Assessing sewers is most commonly performed by the use of closed-circuit television (CCTV) that helps in gaining knowledge of the structural and operational state of the pipelines. Identifying pipelines to be inspected annually is considered a main step toward a successful and cost-effective CCTV program. Generally, the initial process follows a desktop risk management approach that combines the condition of sewers and its criticality that cluster pipelines in short-, medium-, and long-term inspection intervals. Predicting sewers’ conditions would help select critical sewers that are most likely to fail, where these sewers will be prioritized for CCTV inspections to discern their conditions and plan for required restorations, if needed. Accordingly, the current study employs data mining algorithms, specifically Extreme Gradient Boosting (xgboost) and logistic regression (LR), to predict each pipeline’s condition based on the Pipelines Assessment Certification Program (PACP) and Water Research Centre (WRc) ratings. The models are implemented on the city of Hamilton sewer network that consists of combined and separate systems. This study attempted to address the prevalent class imbalance in pipe inspection data sets by employing a diverse range of hyperparameters. Overall, XGBoost produced more satisfying results while still falling short of acceptable average performance. Finally, the results were imported into ArcGIS to better depict the expected sewer condition. Comparing these algorithms shows that these techniques can be further improved by utilizing additional data sets collected from various municipalities across North America.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".