Notice bibliographique
Résumé
The goal of this research is to develop a methodology to accurately determine the reliability of spillway gate systems particularly for spillways that experience harsh environmental conditions and prolonged periods of dormancy. The significance of this study lies in the fact that spillways are rarely in use and remain inactive for most of their service life. Components of emergency spillway gate systems spend the majority of their service life in a dormant state and are activated only during emergencies such as floods or load rejection or on a regular basis for inspection and testing. Also, most spillways are located in remote areas and are subjected to severe environmental conditions which can cause early degradation of components. Furthermore, components of old spillway gate systems are often custom made with no readily available spare parts and little information on the reliability of existing components. These characteristics are very different from those of the equipment used in an industrial setting making it difficult for traditional methods to deliver accurate estimates on the reliability of such systems. Therefore, the development of a methodology that is customized to such conditions and incorporates unique parameters and state-of-the-art reliability techniques can contribute greatly to the dam industry by ensuring the safe operation of spillway systems on demand. The first step in this approach is geared towards system modeling in which a reliability model is developed for the spillway gate system taking into account all components, their relative interactions, latent failures due to dormancy, environmental conditions and type and frequency of inspections and tests. The next step is to develop a quantitative approach to update the availability of the spillway gate system based on real time conditions after each inspection. In this step, a Condition Indexing (CI) approach is combined with dormant availability analysis to evaluate the changes in the state of the system in real time using CI data obtained at each inspection. This approach provides a tool for dam owners to convert qualitative and descriptive results obtained from inspections to an index used as a comparative measure to detect real time changes in the availability of spillway gate systems. Next, inspection and testing procedures of spillway gate systems are investigated to evaluate the effect of different types and frequencies on the reliability of various types of components and the entire system. Lastly, the optimum inspection and testing strategy is determined, minimizing system costs including costs related to inspection and testing and the consequences of failure while at the same time maintaining the availability of the spillway gate system above a predefined limit. Genetic algorithm and Creeping Random Search are used to solve this optimization problem. Using these methods the optimum interval for each type of test is determined and the minimum system cost is calculated based on the optimum intervals.This methodology is used to develop a software application that incorporates all of the above steps into a user friendly program. This software application has been developed for availability analysis of spillway systems and allows users to model complex systems, add inspection, tests and component replacement options to the system, determine the availability of the system as a function of service life and identify the optimum inspection and testing period based on unavailability limits and costs of inspections/tests vs. consequence of failure. This program can be used as a tool by dam owners to accurately determine the availability of custom spillways and to select optimal inspection and testing plans that contribute most to increase the availability of the system.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 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 | 0,000 |
| 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 ».