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This paper presents a process for the inverse analysis of planar beam structures using the finite element method and neural networks. The Euler–Bernoulli beam model and the assumption of linear analysis are considered for the finite element method. The inverse analysis involves determination of the unknown variation of Young’s modulus. A neural network is constructed receiving the axial coordinate of a point as input and returning the output as the value of Young’s modulus at the point. Then, the neural network is trained by minimizing the imbalance of the equilibrium conditions. The accuracy of the presented process is validated by an example.

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Inverse Analysis of Planar Beams with Euler–Bernoulli Beam Model and Neural Networks

  • 会议论文

  • 发表日期：

    2026年

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  • Pana Suttakul,
  • Itthidet Thawon,
  • Thongchai Fongsamootr,
  • Patrapon Kamonpet,
  • Duy Vo

摘要

This paper presents a process for the inverse analysis of planar beam structures using the finite element method and neural networks. The Euler–Bernoulli beam model and the assumption of linear analysis are considered for the finite element method. The inverse analysis involves determination of the unknown variation of Young’s modulus. A neural network is constructed receiving the axial coordinate of a point as input and returning the output as the value of Young’s modulus at the point. Then, the neural network is trained by minimizing the imbalance of the equilibrium conditions. The accuracy of the presented process is validated by an example.