Resilience-Driven Management of Water Distribution Networks
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».