An Efficient Global Optimization Approach for Reliability Maximization of Friction-Tuned Mass Damper-Controlled Structures

• Published in: Shock and vibration Vol. 2018; no. 2018; pp. 1 - 8
• Main Authors: Sampaio, Rubens, Souza de Cursi, Eduardo, Lopez, Rafael H., Nascentes, Fábio F. S., Miguel, Leandro Fleck Fadel
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2018; Hindawi; John Wiley & Sons, Inc; Wiley
• Subjects: Algorithms; Civil engineering; Costs; Design optimization; Earthquake dampers; Earthquakes; Energy dissipation; Friction; Global optimization; Kriging interpolation; Nonlinear analysis; Parameter uncertainty; Random variables; Random vibration; Reliability analysis; Seismic engineering; Seismic response; Time dependence; Vibration isolators; Brazil
• DOI: 10.1155/2018/7414817
• ISSN: 1070-9622

The application of optimization techniques to design passive energy dissipation devices of structures subject to seismic excitation has rapidly increased in the past decades. It is now widely acknowledged that uncertainties inherent to the earthquake loading and structural parameters must be taken into account in the design process. In the case of friction-tuned mass dampers (FTMDs), this optimization under the uncertainty problem leads to the following issues: (a) the high computational cost of the objective function since we are dealing with time-dependent reliability analysis of nonlinear dynamical models and (b) the nonconvexity and multimodality of the resulting optimization objective function. In order to address these issues, we propose here the use of efficient global optimization (EGO) for the probability of failure minimization in FTMD design. EGO is a metamodel-(kriging-) based optimization scheme able to handle expenses to evaluate objective functions, and its capabilities have not been explored in the optimal FTMD design. In order to show the effectiveness of EGO, its results are compared to those of other algorithms from the literature. The results showed that EGO outperformed the competing algorithms, successfully providing the optimum solution of FTMD design under uncertainty within a reasonable computational effort.