Evolutionary Aseismic Design And Retrofit Of Buildings
Notice bibliographique
Résumé
Over the past decade, passive energy dissipation systems have provided an increasingly attractive approach for the seismic retrofit of existing structures, as well as, for the design of new seismically resistant structures. Many different types of passive devices have been developed and general design guidelines have been prepared. However, the choice between the device types for a specific application often is not clear, particularly when consideration must be given to the performance of non-structural components. For example, in general, are rate-independent devices and rate-dependent devices equally beneficial, or are there circumstances in which one of these two categories is preferable? Furthermore, regardless of device type selection, the designer also is faced with the complex issue of effective device distribution. In this paper, we present a genetic algorithm based methodology to address these aspects of aseismic design within the context of steel frame buildings. The primary structure is represented in terms of a nonlinear two-surface plasticity lumped parameter model. Meanwhile, the available passive device types include rate-independent metallic plate dampers, along with rate-dependent viscous fluid dampers and solid viscoelastic dampers. In order to capture more accurately the dynamic response, these devices are also represented by nonlinear models. The seismic environment is characterized either in terms of a fixed set of specified ground motions or by utilizing synthetic signals generated from geophysical models that simulate the actual uncertain seismicity of the site. Within the overall algorithm, passively damped structural designs evolve toward configurations that satisfy constraints on inter-story drift and absolute acceleration, while attempting to limit damper cost. For adjacent buildings, a separation constraint also may be included to alleviate structural pounding. Besides providing an overview of the simulation algorithm, the paper includes a number of illustrative examples to highlight the benefits of the proposed computational design approach. 1 Research Assistant, Dept. of Civil, Structural and Environmental Engineering, State University of New York at Buffalo, Buffalo, NY 14260, U.S.A., Phone +1 716/645-2114, FAX 716/645-3733, sdogruel@buffalo.edu 2 Professor, Dept. of Mechanical and Aerospace Engineering, State University of New York at Buffalo, Buffalo, NY 14260, U.S.A., Phone +1 716/645-2593, FAX 716/645-3875, gdargush@eng.buffalo.edu 3 Research Assistant Professor and Computational Scientist, Center for Computational Research, State University of New York at Buffalo, Buffalo, NY 14260, U.S.A., Phone +1 716/645-6500, FAX 716/6456505, mlgreen@buffalo.edu June 14-16, 2006 Montreal, Canada Joint International Conference on Computing and Decision Making in Civil and Building Engineering
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 | 0,000 | 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 ».