Designing Efficient Grid Structures Considering Structural Imperfection Sensitivity
Abstract
At the initial design stage of a grid structure, shape optimisation is an effective way to find the optimal structural form. However, most of the shape optimisation methods do not take into consideration the imperfections, thus the actual buckling load capacity of the optimised structure is usually low. In this paper, an improved shape optimisation method is proposed, one that is considering the effect of structural imperfection sensitivity. In this method, the bending strain energy ratio is taken as a constraint, and when the total strain energy decreases, yet there is a certain proportion of bending strain energy in the structure. Consequently, the resulted shape is not sensitive to the initial geometry imperfection, and therefore, an efficient structure with higher buckling load capacity and low imperfection sensitivity is obtained. In order to evaluate the redundancy performance of the optimised structure, an index called structural overall redundancy, based on damage model is proposed herein. The damage model is simulated by removing a key rod of the structure. The results demonstrate that the overall redundancy of the structure obtained by the proposed method is higher than that obtained by the traditional method, thus an optimal design of a grid structure is obtained.
Recommended Citation
F. Liu et al., "Designing Efficient Grid Structures Considering Structural Imperfection Sensitivity," Engineering Structures, vol. 204, article no. 109910, Elsevier Ltd, Feb 2020.
The definitive version is available at https://doi.org/10.1016/j.engstruct.2019.109910
Department(s)
Civil, Architectural and Environmental Engineering
Research Center/Lab(s)
Center for High Performance Computing Research
Second Research Center/Lab
Intelligent Systems Center
Keywords and Phrases
Bending Strain Energy; Imperfection Sensitivity; Redundancy; Shape Optimisation; Space Grid Structure
International Standard Serial Number (ISSN)
0141-0296
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2020 Elsevier Ltd, All rights reserved.
Publication Date
01 Feb 2020
Comments
This research was financially supported by the Natural Science Foundation of China under grant numbers 51978151 and 51538002 , by the Colleges and Universities in Jiangsu Province Plans to Graduate Research and Innovation KYLX16_0254, by the Fundamental Research Funds for the Central Universities, and by a Project Funded by the Priority Academic Program Development of the Jiangsu Higher Education Institutions.