Temperature-induced response reconstruction method based on DL-AR model and attention mechanism
Temperature-induced response reconstruction method based on DL-AR model and attention mechanism
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DOI：
10.1016/j.istruc.2023.02.044
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发表时间：
2023-04
期刊：
影响因子：
4.1
通讯作者：
Chengbin Chen;Liqun Tang;Yonghui Lu;Licheng Zhou;Zejia Liu;Yiping Liu;Zhenyu Jiang;Bao Yang
中科院分区：
文献类型：
--
作者：
Chengbin Chen;Liqun Tang;Yonghui Lu;Licheng Zhou;Zejia Liu;Yiping Liu;Zhenyu Jiang;Bao Yang
由于设备老化、无线电干扰等因素的影响，结构健康监测系统中的数据丢失是不可避免的。以往的研究表明，从不完整的数据中提取结构状态信息更具挑战性。因此，研究高精度的缺失数据响应重建方法具有重要意义。在这项研究中，提出了一种具有注意机制的混合深度学习和自回归模型（DL-AR-ATT）框架，以准确地重建考虑数据相关性的结构响应。该模型由深度学习（DL）和自回归（AR）组件组成，可以同时对数据的线性和非线性结构进行建模。为了自适应地加权两个分量对最终结果的贡献，而不是直接求和，该研究在DL-AR-ATT中引入了自定义加权层。此外，注意力机制的引入，以消除冗余信息所造成的多个传感器的数据作为输入。为验证所提方法的有效性和可行性，采用了一座大跨度钢箱梁悬索桥和一座预应力混凝土连续箱梁桥的长期结构健康监测数据。实验结果表明，DL-AR-ATT模型的重建性能上级基于一维卷积神经网络（1D-CNN）和混合DL和AR（DL-AR）模型。最后，DL-AR和DL-AR-ATT模块的权重被可视化，以验证模型捕获数据相关性的能力，并解释注意力和自定义权重层如何提高DL-AR-ATT的性能。
Data loss is unavoidable for structural health monitoring (SHM) systems due to various potential factors, such as equipment aging, radio interference. Previous studies have revealed that it is more challenging to extract structural state information from incomplete data. Therefore, it is important to develop high-precision response reconstruction methods for missing data. In this study, a hybrid deep-learning and autoregressive model with attention mechanism (DL-AR-ATT) framework is proposed to accurately reconstruct structural responses considering data correlations. The proposed model consists of the deep-learning (DL) and autoregressive (AR) components, which can simultaneously model the linear and nonlinear structure of data. To adaptively weight the contribution of the two components to the final result instead of directly summing, the study introduces a custom weighting layer into DL-AR-ATT. Moreover, the attention mechanism is introduced to eliminate redundant information caused by the data from multiple sensors as input. To verify the validity and feasibility of the proposed method, long-term SHM data from a long-span steel box girder suspension bridge and a prestressed concrete continuous box girder bridge are used. The results show that the reconstruction performance of DL-AR-ATT is superior to that of the one-dimensional convolutional neural network (1D-CNN)-based and hybrid DL and AR (DL-AR) models. Finally, the weights of the modules of DL-AR and DL-AR-ATT are visualized to verify the ability of the model to capture data correlations, and to explain how the attention and custom weighting layers improve the performance of DL-AR-ATT.