Notice bibliographique
Résumé
Infrastructure is a critical element in the countries’ growth and development. Poor management of these systems would lead to their failure and in turn to disastrous situations. According to the United States Environmental Protection Agency (EPA) fifth report on drinking water infrastructure, the investments in the drinking water utilities need a total amount of $384.2 billion for the next 15 years, i.e. until December 2030. Also, according to the 2013 American’s Infrastructure Report Card, the Drinking Water System (DWS) is graded as “D”, implying a status between poor and fair, with an increasing failure probability. Similarly, as stated in the last 2016 Canadian Infrastructure Report Card, the water system received a ranking of “Good”, representing an ‘adequate for now’ status. However, about 29 percent of pipelines condition is rated between fair and very poor, signifying that an urgent repair is needed with total replacement cost of $ 60 billion. Meanwhile, due to budget deficits, municipalities find it is a challenge to prioritize which asset to repaire or rehabilitate. Thus, a lot of research is done to predict the probability of failure. Yet, most of this research is limited to the consequence of failure and the criticality of water pipelines. \nThe main objective of this study is to develop a priority index induced by a combination of the criticality and performance of water distribution network. In this research, criticality factors that affect the water distribution networks are identified. Criticality is divided into three main aspects: (i) Economic, (ii) Environmental/Operational and (iii) Social factors. Each of these key elements is divided into subfactors with different attributes to describe the actual status of the proposed area. Paprika and Swing techniques are used to determine the weights of subfactors. The effect values are obtained from experts from North America, Europe and Qatar through questionnaires and meetings. After all the required data are collected, the data are analyzed and incorporated into the criticality model to determine the criticality index for each pipeline in the desired location. A sensitivity analysis is conducted to define the factors with the highest and the lowest impact on the criticality index. It is determined that the “Road type” sub-factor has the highest influence on the criticality index, based on Qatar’s data analysis. Meanwhile, the “Pipeline diameter” sub-factor has the greatest impact on the criticality index, based on North America and Europe data analysis. \nThe developed criticality index is utilized with the performance index to develop the priority index, which is illustrated on the emerged priority scale and matrix for a better evaluation of the current asset status. It is concluded that “Ville Marrie” sector is found to have the highest priority index in Montreal city, equals to 4.42. While,“Bizard Island” has the lowest priority index value in the city, equals to 3.69. The developed model will guide municipalities and governments to generate a capital plan and allocate the available budget to the most critical parts of their networks. These results are also used as a reference to highlight the key areas in each sector of the designed city that need an urgent repair. This will decrease the risks, defects and health hazards of the water networks while maintaining the safety and durability of the water distribution networks in a cost-effective manner.
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 | 0,000 |
| Études des sciences et des technologies | 0,001 | 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 ».