Finding optimal parameters of tuned mass dampers

Şaban Suat ÖZSARIYILDIZ | Ali BOZER

In this paper, optimum design of tuned mass damper for seismically excited structures is discussed. In thedesign process, a benchmark multi-degree of freedom system is considered, and the performance measureof the optimization criterion is selected as the H2and H1norms of the transfer function of the combinedtuned mass damper and building system. Differential evolution algorithm is then utilized to minimize theseobjective functions. The objective function choice on performance and the effectiveness of differential evo-lution optimization algorithm in comparison with other algorithms in the lit ...Daha fazlası

Makale/Derleme 2015 STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS 14 Görüntülenme

Free parameter search of multiple tuned mass dampers by using artificial bee colony algorithm

Ali BOZER | Şaban Suat ÖZSARIYILDIZ

In optimization of multiple tuned mass dampers (MTMDs), certain restric-tions or preconditions such as uniform distribution of stiffness, mass, or fre-quency spacing had been applied for simplification, but in turn, solution ofindividual stiffness and damping parameters are not the true optima. Themain purpose of this paper is to obtain the true optima of individual stiffnessand damping parameters of MTMD system. In the proposed method, param-eters of TMD units are treated as free search optimization variables, and anefficient optimization algorithm, namely, artificial bee colony algorithm has ...Daha fazlası

Makale/Derleme 2018 STRUCTURAL CONTROL & HEALTH MONITORING 6 Görüntülenme

A deep learning approach to dental restoration classification from bitewing and periapical radiographs

Özcan KARATAŞ | Nazire Nurdan ÇAKIR | Şaban Suat ÖZSARIYILDIZ | Cem Abdülkadir GÜRGAN

Objective: The aim of this study was to examine the success of deep learning-based convolutional neural networks (CNN) in the detection and differentiation of amalgam, composite resin, and metal-ceramic restorations from bitewing and periapical radiographs. Method and materials: Five hundred and fifty bitewing and periapical radiographs were used. Eighty percent of the images were used for training, and 20% were left for testing. Twenty percent of the images allocated for training were then used for validation during learning. The image classification model was based on the application of CNN. ...Daha fazlası

Makale/Derleme 2021 Quintessence International 12 Görüntülenme

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