对比深度模型与物理滤波法在少样本下识别结构动态荷载的能力
Deep recurrent-convolutional neural network learning and physics Kalman filtering comparison in dynamic load identification
- 用GRU、LSTM和CNN对比学习，结合物理残差卡尔曼滤波
- 在加州建筑地震响应与基准问题中，模型表现优于传统方法
- 物理模型可辨识时，卡尔曼滤波仍更优，适合工程可靠性场景
本文考察了门控循环单元（GRU）、长短期记忆网络（LSTM）和卷积神经网络在小样本训练条件下的结构动态荷载识别能力，并与基于物理的残差卡尔曼滤波（RKF）进行对比。在实际工程中，由于测试次数有限或结构模型不可辨识，导致荷载识别存在不确定性。研究首先在顶部激励的模拟结构上进行分析；其次，在加州一幢建筑上模拟地震基础激励，实现全自由度加载；最后，针对国际结构控制协会-美国土木工程师学会（IASC-ASCE）结构健康监测基准问题，分析冲击与瞬时荷载条件下的表现。结果表明，不同方法在不同荷载场景下各有优势，但在物理参数可辨识的情况下，RKF表现优于神经网络。
原文摘要 · Abstract (English)
The dynamic structural load identification capabilities of the gated recurrent unit, long short-term memory, and convolutional neural networks are examined herein. The examination is on realistic small dataset training conditions and on a comparative view to the physics-based residual Kalman filter (RKF). The dynamic load identification suffers from the uncertainty related to obtaining poor predictions when in civil engineering applications only a low number of tests are performed or are available, or when the structural model is unidentifiable. In considering the methods, first, a simulated structure is investigated under a shaker excitation at the top floor. Second, a building in California is investigated under seismic base excitation, which results in loading for all degrees of freedom. Finally, the International Association for Structural Control-American Society of Civil Engineers (IASC-ASCE) structural health monitoring benchmark problem is examined for impact and instant loading conditions. Importantly, the methods are shown to outperform each other on different loading scenarios, while the RKF is shown to outperform the networks in physically parametrized identifiable cases.
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