Reconstruction of structural long-term acceleration response based on BiLSTM networks
Reconstruction of structural long-term acceleration response based on BiLSTM networks
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DOI：
10.1016/j.engstruct.2023.116000
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发表时间：
2023-04-01
影响因子：
5.5
通讯作者：
Yang, Bao
中科院分区：
文献类型：
--
作者：
Lu, Yonghui;Tang, Liqun;Yang, Bao
在结构健康监测中，损失动力响应的重建对于结构状态评估具有重要意义。目前的先进方法通常采用基于卷积神经网络（CNN）的深度学习模型来重建结构的加速度响应。然而，这些方法不能充分挖掘监测数据之间的时间相关性，导致精度相对较低。为此，提出了一种基于双向长短期记忆（BiLSTM）网络的结构加速度响应重构方法，能够更好地挖掘传感器数据之间复杂的时空相关性。以某大跨度斜拉桥为例进行了数值计算，并以Z24桥为例进行了实测数据分析，验证了该方法的准确性和有效性。结果表明，该方法能很好地提取结构长期连续加速度数据的时空相关性，在传感器数量有限的情况下，仍能以较高的精度重建结构长期连续加速度数据。该方法优于目前先进的基于机器学习的方法，并在实际的结构加速度数据恢复和损伤识别中表现出令人兴奋的应用前景。
Reconstructing lost dynamic responses is significant for structural condition assessment in structural health monitoring (SHM). Current advanced methods usually employ deep learning models based on convolutional neural networks (CNN) to reconstruct the acceleration response of structures. However, such methods cannot fully mine the temporal correlations among the monitoring data, leading to a relatively low accuracy. Therefore, this study proposes a structural acceleration response reconstruction method based on bidirectional long short-term memory (BiLSTM) networks, which can better mine complex spatial-temporal correlations among the sensor data. A numerical study of a long-span cable-stayed bridge and a practical monitoring data study on the Z24 bridge were conducted to verify the accuracy and efficacy of the proposed reconstruction method. The results show that structural long-term successive acceleration data can be reconstructed with high accuracy using the proposed method despite limited sensors due to the fact that the spatial-temporal correlations of acceleration data can be well extracted. The proposed method outperforms current advanced machine learning-based methods and exhibits exciting application prospects in practical structural acceleration data recovery and damage identification.