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Lookup NU author(s): Dr Varun OjhaORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2022 The Author(s)The paper proposes a novel adaptive search space decomposition method and a novel gradient-free optimization-based formulation for the pre- and post-buckling analyses of space truss structures. Space trusses are often employed in structural engineering to build large steel constructions, such as bridges and domes, whose structural response is characterized by large displacements. Therefore, these structures are vulnerable to progressive collapses due to local or global buckling effects, leading to sudden failures. The method proposed in this paper allows the analysis of the load-equilibrium path of truss structures to permanent and variable loading, including stable and unstable equilibrium stages and explicitly considering geometric nonlinearities. The goal of this work is to determine these equilibrium stages via optimization of the Lagrangian kinematic parameters of the system, determining the global equilibrium. However, this optimization problem is non-trivial due to the undefined parameter domain and the sensitivity and interaction among the Lagrangian parameters. Therefore, we propose to formulate this problem as a nonlinear, multimodal, unconstrained, continuous optimization problem and develop a novel adaptive search space decomposition method, which progressively and adaptively re-defines the search domain (hypersphere) to evaluate the equilibrium of the system using a gradient-free optimization algorithm. We tackle three benchmark problems and evaluate a medium-sized test representing a real structural problem in this paper. The results are compared to those available in the literature regarding displacement–load curves and deformed configurations. The accuracy and robustness of the adopted methodology show a high potential for gradient-free algorithms to analyze space truss structures.
Author(s): Ojha V, Panto B, Nicosia G
Publication type: Article
Publication status: Published
Journal: Engineering Applications of Artificial Intelligence
Year: 2023
Volume: 117
Issue: Part B
Print publication date: 01/01/2023
Online publication date: 17/11/2022
Acceptance date: 31/10/2022
Date deposited: 08/03/2023
ISSN (print): 0952-1976
ISSN (electronic): 1873-6769
Publisher: Elsevier Ltd
URL: https://doi.org/10.1016/j.engappai.2022.105593
DOI: 10.1016/j.engappai.2022.105593
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