Predictive Analytics for Planning Inspections of Linear Water and Wastewater Infrastructure
Bibliographic record
Abstract
Techniques currently available for modeling the deterioration of aging water and wastewater linear infrastructure tend to focus on providing municipalities with generalized estimates of condition at the network-wide level. These models have often been found incapable of reliably predicting the condition of individual pipes within a larger network. The primary goal of this research was to utilize existing data mining tools to make predictions of individual pipe condition that could effectively direct the inspection, maintenance and rehabilitation of critical infrastructure on an asset-by-asset basis. The municipalities of Guelph, Ontario, Canada and Scarborough, Ontario, Canada are provided as case studies. Portions of the sanitary sewer and stormwater networks in Guelph were inspected from 2008 to 2011 using closed circuit television (CCTV) technology. A combined application of predictive and spatial analytics effectively leverages information contained within the existing inspection dataset so that the potential threat posed by uninspected pipes can be suitably assessed. Methods are described for using class-imbalanced inspection datasets to train, tune and test support vector machines, decision tree classifiers and random forests. Decision tree classifiers were found to be a useful first step for extracting information from existing datasets as they illustrate the influence of pipe-specific attributes (e.g. year of installation and length) on structural condition. Support vector machines were outperformed by random forests that achieved excellent levels of predictive accuracy for what is, in reality, a difficult classification task. Ultimately, the proposed modeling methodology has the potential to significantly reduce the time and money spent identifying bad condition, uninspected pipes. An analysis of historical water main failures within Scarborough, Ontario, Canada indicates the majority of failures occur during the very cold winter months. Extensive installation of cement mortar lining and cathodic protection has extended the lifespan of aging water mains. Artificial neural networks are found capable of predicting the time to failure for individual pipes. Simulated failure scenarios indicate a return to high failure rates if cement mortar lining and cathodic protection are not extended to all candidate pipes within the distribution network.
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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 | 0.000 | 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".