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Published March 2018 | public
Journal Article

Robust-to-Uncertainties Optimal Design of Seismic Metamaterials

Abstract

Metamaterials, which draw their origin from a special class of structured (periodic) materials characterized by a dynamic filtering effect, have recently emerged as a prospective means for structural seismic protection. This paper explores such a periodic arrangement in the form of local adaptive resonators buried in the soil, serving as a seismic protection barrier. As a starting point, a simplistic representation is chosen herein that comprises chains of mass-in-mass unit cells. A robust-to-uncertainties optimization of such a chain, addressing uncertainties at the level of the excitation, the system properties and the model structure itself, is conducted. The optimization problem is formulated within the context of reliability assessment, where the objective function is the failure probability of the structure to be protected against seismic input. The problem is solved through adoption of the subset optimization algorithm enhanced through the simultaneous implementation of a stochastic approximation algorithm. It is demonstrated that not all parameters of the chain model require optimization, because the failure probability proves to be a monotonic function of a subset of the parameters. A primary objective herein lies in optimizing the internal unit-cell stiffness properties. It is further demonstrated that the effectiveness of the protection offered by the metamaterial is improved for spatially varying unit-cell properties. The optimization procedure is carried out in the frequency domain, with an example application confirming that a time domain optimization is expected to yield similar optimal configurations. A parametric study using a nonlinear model is also presented, offering a starting point for more refined future investigations.

Additional Information

© 2017 American Society of Civil Engineers. Received: April 04, 2017. Accepted: August 02, 2017. Published online: December 20, 2017. This paper is based on a master's thesis carried out during a stay of one of the authors as a visiting student researcher at Caltech. The authors thank Prof. Dr. Alexandros Taflanidis for his support during the implementation of the subset optimization algorithm. Additional thanks also to Prof. Dr. Apostolos Papageorgiou for his clarifying remarks on the authors' questions about the synthetic time histories algorithm.

Additional details

Created:
August 21, 2023
Modified:
October 18, 2023