Simplified and applied generalization of the spectral stochastic finite element method to solve structural problems with large deformations

Document Type : Original Article

Authors

1 associate professor، department of civil engineering، Yazd university، Yazd، Iran

2 PhD candidate، department of civil engineering، Yazd university، Yazd، Iran

10.22065/jsce.2024.413811.3207

Abstract

Analysis of structures with large deformations and uncertain parameters is computationally costly and requires the use of powerful computational tools, which with the development of computational tools, the use of these analytical methods has become more common. On the other hand, with the development of computational tools, engineers and designers turn to designs that involve more complex conditions and less simplification, and therefore researchers are always looking for solutions that simultaneously have the appropriate speed and accuracy. One of the most efficient methods in solving structural problems with uncertainties is the stochastic finite element method . In the present study using mathematical relations and basic concepts of this method, we have developed a novel method, the large deformations spectral stochastic finite element method to analyze the structures with large deformations. This method is a simple and applied computational method has been proposed to obtain the answers of large deformation structures with uncertainties. By solving various problems of plane stress structures and by applying uncertainties in structural parameters, input loads and boundary conditions, the efficiency of the proposed method has been evaluated. Comparing the results of this method with the Monte Carlo simulation shows the very high efficiency and accuracy of the proposed method. . The source code of the proposed NSSFEM is available at https://github.com/seyedsajadmousavi/NSSFEM

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Articles in Press, Accepted Manuscript
Available Online from 14 March 2024
  • Receive Date: 12 September 2023
  • Revise Date: 22 January 2024
  • Accept Date: 14 March 2024