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Adaptive momentum-based optimization to train deep neural network for simulating the static stability of the composite structure

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Abstract

This article is the first attempt to employ deep learning to estimate the mechanical performance of multi-phase systems. Features of the design-points are obtained with the aid of the fast-converging numerical method used to solve the governing motion equations developed according to the kinematics of shear deformable structures. The optimum values of the parameters involved in the mechanism of the fully-connected neural network are determined through the momentum-based optimizer. The strength of the method applied in this survey comes from the high accuracy besides lower epochs needed to train the multi-layered network. It should be mentioned that the mechanical characteristics of the structure are computed through a two-step micromechanical scheme including the Halpin–Tsai method. The accuracy of the employed approach is examined and verified through the comparison of the results with those published in the literature. The numerical results give the practical hint that increasing the content of the reinforcement phase not always equal to increasing the resistance of the composite structure toward static instability. Thus, designers must choose the weight content of nano or macro-scale reinforcements by considering the shape factors of these materials to boost the strength of the system appropriately.

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Acknowledgements

This work was supported by Key Scientific Research Projects of Higher Education Institutions in Henan Province (No. 18B170008). This work was supported by Guilin science research and technology development plan project (2016012006, 20160208, 20170101-3, 20180104-12) and Basic ability improvement project for young and middle-aged teachers in Guangxi Universities (2017KY0862). This work was supported by Jouf university, Sakaka, Al-Jouf, KSA

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Authors and Affiliations

  1. College of Geographic Sciences, Xinyang Normal University, Xinyang, 464000, Henan, China

    Zhifeng Chi

  2. Henan Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution, Xinyang Normal University, Xinyang, 464000, Henan, China

    Zhifeng Chi

  3. Practical Teaching Department, Guilin University of Aerospace Technology, Guilin, 541004, Guangxi, China

    Zhiyong Jiang

  4. Department of Computer and Information Science, Jouf University, Sakaka, Al Jouf, 72311, Kingdom of Saudi Arabia

    M. M. Kamruzzaman

  5. Department of Statistics, University of Wisconsin-Madison, Madison, WI, 53706, USA

    Behzad Aalipur Hafshejani

  6. Department of Mechanical Engineering, Faculty of Engineering, Tarbiat Modares University, Tehran, Iran

    Mehran Safarpour

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  1. Zhifeng Chi
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  2. Zhiyong Jiang
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  3. M. M. Kamruzzaman
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Correspondence to M. M. Kamruzzaman.

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Chi, Z., Jiang, Z., Kamruzzaman, M.M. et al. Adaptive momentum-based optimization to train deep neural network for simulating the static stability of the composite structure. Engineering with Computers 38 (Suppl 5), 4027–4049 (2022). https://doi.org/10.1007/s00366-021-01335-5

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