Impact identification based on surrogate-assisted efficient global optimisation
File(s)
Published version
Author(s)
Xiao, Dong
Khodaei, Zahra Sharif
Aliabadi, MH Ferri
Type
Conference Paper
Abstract
Model-based methods have gained significant attention as an on-line tool for impact diagnosis in composite structures. These methods involve the development of a mathematical model that simulates impact dynamics and identifies impact characteristics by minimizing the differences between predicted and actual responses. However, the associated optimisation process is often computationally demanding and time-consuming. Traditional heuristic algorithms like the genetic algorithm may necessitate thousands of impact simulations to achieve convergence. To address the challenge of computational efficiency in model-based methods, this study introduces the surrogate-assisted efficient global optimisation algorithm to solve the minimization problem for impact identification. The proposed approach leverages the efficient global optimisation framework, which utilizes a Kriging meta-model to capture the relationships among impact location, impact force (design variables), and response differences (objective function). By iteratively infill sampling using the generalized expected improvement criterion, a suitable balance between exploration and exploitation is maintained during the optimisation process. This ensures effective search in the design space. Additionally, local surrogates for impact location and impact peak force are constructed to adaptively bound these design variables within feasible ranges. This bounding technique narrows down the search space and accelerates convergence. The validity of the proposed method is demonstrated on the finite element model of a composite plate using impact force history obtained from experiments as input excitation. The results illustrate that the proposed method accurately identifies impacts with fewer than 100 impact simulations, highlighting its efficiency and effectiveness. By leveraging surrogate models and efficient search strategies, the proposed algorithm significantly reduces the computational burden while ensuring precise and reliable impact identification.
Date Issued
2024-01-01
Date Acceptance
2022-06-12
Citation
Procedia Structural Integrity, 2024, 52, pp.667-678
ISSN
2452-3216
Publisher
Elsevier BV
Start Page
667
End Page
678
Journal / Book Title
Procedia Structural Integrity
Volume
52
Copyright Statement
© 2023 The Author(s). Published by Elsevier B.V. This is an ope access paper licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/).
Identifier
http://dx.doi.org/10.1016/j.prostr.2023.12.067
Publication Status
Published