Structural Analysis of GFRP Elastic Gridshell Structures by Particle Swarm Optimization and Least Square Support Vector Machine Algorithms

Document Type : Original Article

Authors

1 Department of Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

2 State Key Laboratory of Ocean Engineering, Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, Department of Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Abstract

The gridshell structure is a kind of freeform structure, which is formed by the deformation of a flat grid and the final shape is a double curvature structure. The structural performance of the gridshell is usually obtained by finite element analysis (FEA), which is a time-consuming procedure. This paper aims to present a framework for structural analysis based on the machine learning (ML) model in order to reduce computational time. To this aim, design parameters including the length, width, height, and grid size of the structure are taken into consideration as inputs. The outputs are the member-stresses and the ratio of displacement to self-weight. Therefore, a combination of two algorithms, least-square support vector machine (LSSVM) and particle swarm optimization (PSO), is considered. PSO-LSSVM hybrid model is applied to predict the results of the structural analysis rather than the FEA. The results show that the proposed hybrid approach is an efficient method for obtaining structural performance.

Keywords

Main Subjects

Copyright © 2021 Soheila Kookalani. This is an open access paper distributed under the Creative Commons Attribution License. Journal of Civil Engineering and Materials Application is published by Pendar Pub; Journal p-ISSN 2676-332X; Journal e-ISSN 2588-2880.

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Journal of Civil Engineering and Materials Application
Volume 5, Issue 3
September 2021
Pages 139-150

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History

  • Receive Date: 15 July 2021
  • Revise Date: 01 September 2021
  • Accept Date: 04 September 2021

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