Prediction of water distribution system pipes and isolation valves failure using Bayesian models with the consideration of soil corrosion and climate change
Bibliographic record
Abstract
Aging water distribution system pipes and valves are at stake due to progressive deterioration. Deterioration of the pipes and valves might be as a result of static, operational and dynamic (time-dependent) factors. The impact of these factors are manifested in the form of declined water quality, diminished hydraulic capacity, increased leakage rate and frequent pipe breaks. Reported literature shows that there are a limited number of comprehensive models which integrate characteristics of these factors. As a result, municipalities have been facing a challenge in predicting pipe failure and practicing proactive decision making. The primary objective of this research was to develop a comprehensive Bayesian (Bayesian Belief Network (BBN), Dynamic Bayesian Network (DBN) and Bayesian Model Averaging (BMA)) models to predict/forecast pipe and valve failures by considering driving factors with particular attention to soil corrosion and climate change. Firstly, a comprehensive BBN-based Soil Corrosivity Index (SCI) model was developed to account for interdependencies among different soil parameters. The developed BBN-SCI model combines in situ, experimental, and expert opinion data sources. This model is, then, extended to BBN-based Remaining Service Life (RSL) model which predicts pit depth and quanti es the probability and time to failure for cast iron pipes. The next part of this research evaluates the failure of valves using BBN-based failure mode and effect analysis. The DBN model considers static, operational and time-dependent factors to predict annual and monthly pipe failure rates. Finally, the BMA model integrates climate projection data for future pipe failure forecasting. Overall, this research integrated physical, environmental, and operational factors speculated in contributing to failures of pipes and valves. The data analysis and methodology proposed in this study will help water utility managers and operators to make informed decision making for e cient design and construction of a new water supply systems or planning renewal and rehabilitation programs for old water supply systems. By implementing this methodology, water utilities can incorporate the most impacting pipe and valve failure factors, speci cally soil corrosion and climate change, in operational, tactic, and strategic level decision making.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".