Yang Hongji, Jiang Jinhui, Chen Guoping, Mohamed M Shadi, Lu Fan
State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
School of Energy, Geoscience, Infrastructure and Society, Heriot-Watt University, Edinburgh EH14 4AS, UK.
Materials (Basel). 2021 Dec 18;14(24):7846. doi: 10.3390/ma14247846.
The determination of structural dynamic characteristics can be challenging, especially for complex cases. This can be a major impediment for dynamic load identification in many engineering applications. Hence, avoiding the need to find numerous solutions for structural dynamic characteristics can significantly simplify dynamic load identification. To achieve this, we rely on machine learning. The recent developments in machine learning have fundamentally changed the way we approach problems in numerous fields. Machine learning models can be more easily established to solve inverse problems compared to standard approaches. Here, we propose a novel method for dynamic load identification, exploiting deep learning. The proposed algorithm is a time-domain solution for beam structures based on the recurrent neural network theory and the long short-term memory. A deep learning model, which contains one bidirectional long short-term memory layer, one long short-term memory layer and two full connection layers, is constructed to identify the typical dynamic loads of a simply supported beam. The dynamic inverse model based on the proposed algorithm is then used to identify a sinusoidal, an impulsive and a random excitation. The accuracy, the robustness and the adaptability of the model are analyzed. Moreover, the effects of different architectures and hyperparameters on the identification results are evaluated. We show that the model can identify multi-points excitations well. Ultimately, the impact of the number and the position of the measuring points is discussed, and it is confirmed that the identification errors are not sensitive to the layout of the measuring points. All the presented results indicate the advantages of the proposed method, which can be beneficial for many applications.
结构动力特性的确定可能具有挑战性，尤其是对于复杂情况。这可能是许多工程应用中动态载荷识别的主要障碍。因此，避免为结构动力特性寻找众多解决方案可以显著简化动态载荷识别。为了实现这一点，我们依靠机器学习。机器学习的最新发展从根本上改变了我们在众多领域处理问题的方式。与标准方法相比，机器学习模型可以更容易地建立来解决反问题。在这里，我们提出了一种利用深度学习进行动态载荷识别的新方法。所提出的算法是一种基于递归神经网络理论和长短期记忆的梁结构时域解决方案。构建了一个包含一个双向长短期记忆层、一个长短期记忆层和两个全连接层的深度学习模型，以识别简支梁的典型动态载荷。然后使用基于所提出算法的动态逆模型来识别正弦、脉冲和随机激励。分析了模型的准确性、鲁棒性和适应性。此外，评估了不同架构和超参数对识别结果的影响。我们表明该模型可以很好地识别多点激励。最终，讨论了测量点数量和位置的影响，并证实识别误差对测量点的布局不敏感。所有呈现的结果都表明了所提出方法的优点，这对许多应用可能是有益的。
