Reliability-Based Management of Water Distribution \nNetworks
Notice bibliographique
Résumé
Reliability-Based Management of Water Distribution Networks \nAlaa Salman, Ph.D. \nConcordia University, 2011 \nCanada’s civil infrastructure systems have been in use for over 79 % of their expected service life. Municipalities in Canada have noted that 59% of their water systems needed repair and the condition of 43% of these systems is unacceptable. Therefore, a significant volume of rehabilitation projects are necessary to improve infrastructure performance. Reliability and criticality \nassessments (RCA) as well as the ability to determine the most suitable methods of rehabilitation are urgently needed in order to allocate the available budget efficiently. The research presented in this thesis aims at developing a priority index (PI) for intervention that considers the combination of RCA for water networks. Sound techniques are utilized to develop the PI such as reliability theory, simple multi-attribute rating technique (SMART), and Analytical Hierarchy Process (AHP). \nThe reliability assessment encompasses two levels: (1) segment and (2) subnetwork reliabilities. The priority index (PI) for intervention is crucial to schedule \nsegment rehabilitation. Simple Multi Attribute Rating Technique (SMART) is used to select the most suitable methods of rehabilitation for these components. \nSelection of a rehabilitation method is based on several factors: (1) technical feasibility, (2) whether the selection is contractually acceptable, (3) cost \niv effectiveness, (4) environmental impact, and (5) whether the rehabilitation method is a new technology or not. The output of rehabilitation selection model is the method of rehabilitation for components coupled with the associated costs and durations for rehabilitation activities for each sub-network. The final stage of this research is to schedule these rehabilitation activities. Scheduling of the rehabilitation activities related to water main networks depends mainly on available budget and planning time. Other factors, such as network reliability, criticality, location, contract size, and rehabilitation method(s), also \naffect the scheduling process. This research presents a method for optimizing the scheduling of rehabilitation \nwork for water distribution networks. The method utilizes unsupervised neural networks (UNNs) and Mixed Integer Non Linear Programming (MINLP) and performs the scheduling in two stages. In the first stage, UNNs are used to group \nwater mains according to their locations and rehabilitation methods. In the second stage, MINLP is used to determine the number of rehabilitation contract packages and to generate an optimized schedule based on these packages \nconsidering network reliability, criticality, contract size, and planning time. Data on water network are collected from the city of Hamilton, Ontario, Canada. Four \nsub-networks are selected randomly from the entire network to represent four types of land use; undeveloped, residential, park, and commercial/industrial. The \ndata is used as a test bed to validate and demonstrate the use of the developed research methodology. An automated tool (DSSWATER), based on the developed methodology, is developed to assist users and decision makers. The \ndeveloped models and tools are expected to be beneficial to municipal engineers and managers as well as to academics.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».