Reconstruction of long-term strain data for structural health monitoring with a hybrid deep-learning and autoregressive model considering thermal effects
Reconstruction of long-term strain data for structural health monitoring with a hybrid deep-learning and autoregressive model considering thermal effects
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DOI：
10.1016/j.engstruct.2023.116063
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发表时间：
2023-06
影响因子：
5.5
通讯作者：
Chengbin Chen;Liqun Tang;Yonghui Lu;Yong Wang;Zejia Liu;Yiping Liu;Licheng Zhou;Zhenyu Jiang;Bao Yang
中科院分区：
文献类型：
--
作者：
Chengbin Chen;Liqun Tang;Yonghui Lu;Yong Wang;Zejia Liu;Yiping Liu;Licheng Zhou;Zhenyu Jiang;Bao Yang
完整的数据对于实施可靠的结构健康监测是必不可少的，然而，由于设备故障或其他潜在因素导致的数据丢失是不可避免的。因此，有必要研究可靠的结构响应重建方法。然而，以前的应变重建方法有三个缺点，导致重建精度较低：1)它们通常没有使用捕获时间序列长期和短期变化模式的能力更好的模块，如双向门控递归单元(BiGRU)和卷积神经网络(CNN)；2)它们没有同时考虑传感器之间的时空相关性和温度与应变之间的强相关性，这些都被证明有助于分别提高重建精度；3)它们没有仔细考虑SHM数据之间的线性和非线性相关性。为了解决这些问题，我们提出了一种结合了非线性深度学习(DL)分量和线性自回归(AR)分量的应变重建方法。该方法还利用DL分量中的BiGRU和CNN来更好地捕捉SHM数据的长期和短期模式，以及上述两种数据的相关性。为了充分验证该方法的有效性，利用一座大跨度钢箱梁悬索桥的长期健康监测数据进行了验证。结果表明，在不同的场景下，混合的DL和AR模型能够以更高的精度重建长期和短期缺失数据，比以往的模型，如CNN。
Complete data are essential for implementing reliable structural health monitoring (SHM); however, data loss due to equipment malfunction or other potential factors is unavoidable. Therefore, it is necessary to develop reliable structural-response-reconstruction methods. However, previous strain reconstruction methods have three shortcomings that lead to relatively low accuracy: 1) they typically do not use modules with better ability to capture the long- and short-term variation patterns of time series, such as the bidirectional gated recurrent unit (BiGRU) and convolutional neural network (CNN); 2) they do not simultaneously consider the spatiotemporal correlation among sensors and the strong correlation between temperature and strain, each of which has been demonstrated to contribute to improving the reconstruction accuracy separately; and 3) they do not carefully consider the linear and nonlinear correlations among SHM data. To address these problems, we proposed a strain reconstruction method that combines a nonlinear deep-learning (DL) component with a linear autoregressive (AR) component. The method also utilizes BiGRU and CNN in the DL component to capture the long- and short-term patterns of the SHM data better, as well as the two above-mentioned data correlations. To fully validate the performance of the proposed method, the long-term SHM data of a long-span steel box girder suspension bridge were utilized for validation. The results show that the hybrid DL and AR model can reconstruct the long- and short-term missing data with higher accuracy under different scenarios than previous models, such as the CNN.