Resilience-Driven Management of Water Distribution Networks
Bibliographic record
Abstract
Water distribution networks (WDNs) are critical infrastructure systems that secure needed supply of potable water to the public. Efficient management of WDNs has always been a primary concern for decision-makers, particularly in events of natural disasters, deliberate attacks, human-made accidents and/or sudden failures. Aging and deterioration of WDNs further exacerbate their vulnerability and likelihood of service disruption. Previous hazards reveal that classical risk-based approaches are not sufficient to prevent disruptions of WDNs. As a result, the concept of resilient WDNs has emerged to cope up with inevitable disruptions that are becoming more frequent. \nThe objective of this research is to develop a holistic resilience-based management model for WDNs. In this context, WDNs is sought to be strong enough to withstand unforeseen disruptions with a minimum performance impact and to recover rapidly after a service interruption. Firstly, a multi-attribute metric is developed for assessing resilience of WDNs based on robustness and redundancy. Attributes from graph theory are employed to quantify the network redundancy. Robustness is measured by integrating the reliability and criticality of pipe segments of the network. Multi-attribute utility theory and Fuzzy analytical network process are exploited to estimate the criticality of water segments based on a set of economic, social, and environmental factors. Survival analysis and maximum likelihood estimate are employed to dynamically determine reliability of pipe segments. Censored inter-failure time data are leveraged to model the deterioration behavior of homogenous cohorts of pipe. The developed metric was used to measure the resilience of a real-life WDN in the City of London, Ontario. The results obtained showed an average of 5% variation when compared to previously developed flow-based and topology-based metrics. \nIn the second step, rapidity and resourcefulness qualities are considered to develop a resilience-based restoration model. A failure scenario causing multiple simultaneous breaks across the network is simulated to investigate the recovery process. A stochastic multi-objective optimization model that maximizes resilience while minimizing the total time and cost of the recovery process is then formulated. This model accounts for different restoration methods, relocation time and cost of restoration crews, and uncertainties in recovery estimates. The optimum restoration plan encompasses a sequence of failed segments restorations along with the restoration method. This plan achieved 4% cost saving, 48% duration reduction, and 4% resilience improvement when compared to current planning practices. \nThe last step involves developing a multi-objective resilience enhancement model so that WDNs can be better prepared for future disruptions. The aim is to maximize resilience of WDNs while minimizing the life cycle cost and carbon emissions of enhancement actions. Optimum enhancement interventions are firstly determined and clustered into work packages before an optimized schedule is generated considering various operational and managerial factors. Applied to a section of an actual WDN of 34 km in a length and average age of 40 years, resilience was increased by 20% with CAD 1.65 million of current investment. The study of that network indicates that a cost-saving of 32% could be attained when adopting the developed model over ongoing portfolio management practices. \nThe novel resilience-driven management model introduced in this research is expected to assist decision-makers better assess and enhance resilience of WDNs and improve restoration planning. The developed model can assist city mangers in allocation and utilization of resources more effectively in development of optimized plans for resilient and sustainable WDNs.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".