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Data-driven load identification method of structures with uncertain parameters

含有不确定参数结构的数据驱动载荷识别方法

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Abstract

Dynamic load identification plays a crucial role in structural design and optimization. The majority of current studies are focused on deterministic structures. However, the structural parameters of actual engineering structures are unknown. It is essential to investigate the issue of dynamic load identification for uncertain structures since the existence of uncertain parameters can lead to errors between load identification results and actual load values. Therefore, in this paper, we propose a data-driven dynamic load identification method for structures containing some uncertain parameters. To start, the uncertain parameters are characterized by a set of closed interval vectors. Then a convolutional neural network (CNN) is introduced for the reconstruction of the interval of unknown load. Combining the interval analysis theory with Taylor expansion, the upper and lower boundaries of the supervised loads are obtained and used as training samples. Finally, the trained CNN model directly identifies the boundaries of the unknown load interval. The simulation results demonstrate that the proposed method has great accuracy in load identification and has good robustness to noise. We construct a simply supported beam structure for experiments to further validate the feasibility of the proposed method in engineering. Additionally, we discuss the effect of measurement point distribution and number of samples on the identification accuracy, which is beneficial for applications in engineering practice.

摘要

摘要动载荷识别在结构设计和优化中起着至关重要的作用目前的大多数研究都集中在确定性结构上. 然而, 实际工程结构中的结构 参数是未知的. 不确定参数的存在会导致载荷识别结果与实际载荷值之间的误差, 因此研究不确定结构的动载荷识别问题至关重要的. 本文针对含有不确定参数的结构, 提出了一种数据驱动的动载荷识别方法. 首先, 将不确定参数由一组闭区间向量表征. 然后引入卷积 神经网络(CNN)对未知载荷区间进行重构. 将区间分析理论与泰勒展开相结合, 得到了监督载荷的上下边界, 并将其用作训练样本. 最 后, 训练后的CNN模型直接识别未知载荷区间的边界. 仿真结果表明, 该方法具有较高的载荷识别精度和对噪声的鲁棒性. 我们构造了 一个简支梁结构进行实验, 以进一步验证所提出方法在工程中的可行性. 此外, 我们还讨论了测量点分布和样本数量对识别精度的影 响, 这有利于在工程实践中的应用

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Acknowledgements

This work was supported by the Foundation of National Key Laboratory of Science and Technology on Rotorcraft Aeromechanics (Grant No. 61422202105), Qing Lan Project and the National Natural Science Foundation of China (Grant No. 51775270).

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Authors and Affiliations

  1. State Key Laboratory of Mechanics and Control for Aerospace Structures, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Wenxu Cui  (崔文旭), Jinhui Jiang  (姜金辉), Huiyu Sun  (孙慧玉) & Hongji Yang  (杨泓基)

  2. College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, 210037, China

    Xu Wang  (王旭)

  3. Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, Southeast University, Nanjing, 210016, China

    Lihui Wang  (王立辉)

  4. Mechatronic Engineering College, Jinling Institute of Technology, Nanjing, 211169, China

    Hongqiu Li  (李鸿秋)

Authors
  1. Wenxu Cui  (崔文旭)
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  2. Jinhui Jiang  (姜金辉)
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  3. Huiyu Sun  (孙慧玉)
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  4. Hongji Yang  (杨泓基)
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  5. Xu Wang  (王旭)
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  6. Lihui Wang  (王立辉)
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  7. Hongqiu Li  (李鸿秋)
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Contributions

Author contributions Wenxu Cui contributed to the conceptualization, data curation, formal analysis, methodology, software, writing-original draft, and writing–review & editing. Jinhui Jiang contributed to the conceptualization, funding acquisition, methodology, resources, validation, and writing–review & editing. Huiyu Sun contributed to the conceptualization, methodology, and validation. Hongji Yang contributed to the formal analysis and methodology. Xu Wang contributed to the validation and visualization. Lihui Wang and Hongqiu Li contributed to the writing–review & editing.

Corresponding author

Correspondence to Jinhui Jiang  (姜金辉).

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Conflict of interest On behalf of all authors, the corresponding author states that there is no conflict of interest.

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Cui, W., Jiang, J., Sun, H. et al. Data-driven load identification method of structures with uncertain parameters. Acta Mech. Sin. 40, 523138 (2024). https://doi.org/10.1007/s10409-023-23138-x

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  • DOI: https://doi.org/10.1007/s10409-023-23138-x

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