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A new interval perturbation method for static structural response bounds using radial basis neural network differentiation

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Abstract

The interval perturbation method is an effective and successful tool in the uncertainty analysis; however, it suffers from the deficiency in the required differential information, which limits its application in complex engineering problems. To end this, this paper uses the radial basis neural network to formulate the derivative information, and its fine accuracy is demonstrated by a mathematical example. Moreover, a new interval analysis method combining interval perturbation and radial basis neural network differentiation, abbreviated as RBNNIPM is proposed. Furthermore, RBNNIPM is applied to calculate the boundaries of yield stress in a three-bar truss, and the detailed assessment proves that RBNNIPM has both high efficiency and high precision. Finally, an electromagnetic buffer model is established to certificate the practicability of RBNNIPM in practical engineering.

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Acknowledgments

This research was financially supported by the “China National Postdoctoral Program for Innovative Talents” [Grant No. BX2021126], the “National Natural Science Foundation of China” [Grant No. 52105106], the “Jiangsu Province Natural Science Foundation” [Grant No. BK20210342], the “China Postdoctoral Science Foundation” [Grant No. 2021M701711], the “Jiangsu Planned Projects for Postdoctoral Research Funds” [Grant No. 2021K008A], and the “Nanjing Municipal Human Resources and Social Security Bureau” [Grant No. MCA21121]. Besides, the authors wish to express their many thanks to the reviewers for their useful and constructive comments.

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, P.R. China

    Yuwei Yao, Liqun Wang, Guolai Yang & Lei Li

  2. Nanjing Chengguang Group Co., Ltd., Nanjing, 210006, P. R. China

    Fengjie Xu

Authors
  1. Yuwei Yao
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  2. Liqun Wang
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  3. Guolai Yang
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  4. Fengjie Xu
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  5. Lei Li
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Corresponding author

Correspondence to Liqun Wang.

Additional information

Liqun Wang received the Ph.D. degree in armament science and technology from Nanjing University of Science and Technology, Nanjing, China, in 2020. He is currently a Postdoctoral Fellow of Mechanical Engineering with the School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, China. He visited the Department of Mechanical Engineering, University of Alberta, Edmonton, Canada, as a visiting student from 2019 to 2020. His research interests concentrate on uncertainty quantification and propagation, specific electromagnetic phenomena under impact load, nonlinear vibration and launch dynamics.

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Yao, Y., Wang, L., Yang, G. et al. A new interval perturbation method for static structural response bounds using radial basis neural network differentiation. J Mech Sci Technol 37, 1389–1400 (2023). https://doi.org/10.1007/s12206-023-0225-z

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  • DOI: https://doi.org/10.1007/s12206-023-0225-z

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