Journal of Structural and Construction Engineering

Journal of Structural and Construction Engineering

Optimization of triple-friction pendulum isolator using Kriging as a surrogate model under El Centro earthquake

Document Type : Original Article

Authors
1 Ph.D. student, Department of Civil Engineering, Yazd University, Yazd, Iran.
2 Associate Professor, Department of Civil Engineering, Yazd University, Yazd, Iran
3 Assistant Professor, Department of Civil Engineering, Yazd University, Yazd, Iran
Abstract
The triple friction pendulum isolator is adaptable and can exhibit variable stiffness and damping in response to different earthquakes. Therefore, the need to optimize the effective parameters of this isolator to achieve the desired performance is significant. In this research, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm is used to optimize the effective parameters of the triple friction pendulum isolator to minimize the maximum relative displacement between floors and the roof acceleration in two separate optimization processes. As optimizing complex models is computationally intensive and time-consuming, substituting the actual responses of the finite element model with cost-effective surrogate models results in significant time savings. In this article, structural analysis is performed using a nonlinear model implemented in OpenSees software, making the structural optimization process time-consuming and computationally expensive. To address this challenge, the Kriging model is employed as a surrogate to replace the finite element analysis computations necessary to calculate the responses, i.e., minimizing the maximum relative displacement between floors and the roof acceleration. The optimization results using the combination of the CMA-ES algorithm and the surrogate model are compared with optimization using the genetic algorithm. The findings reveal that utilizing the surrogate model for optimization reduces relative displacement between floors by 66.39% and roof acceleration by 12.28%. Additionally, computational costs are reduced by over 84%.
Keywords

Subjects


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Volume 11, Issue 9 - Serial Number 86
December 2024
Pages 143-162

  • Receive Date 31 October 2023
  • Revise Date 29 February 2024
  • Accept Date 08 April 2024