1.重庆人文科技学院，计算机工程学院，重庆 401524;2.重庆人文科技学院，大数据与网络信息安全工程技术研究中心，重庆 401524;3.重庆大学 土木工程学院，重庆 400045
何盈盈（1994—），女，硕士，主要从事大数据分析与挖掘、结构健康监测、人工智能方向研究，（E-mail）heyingyg@163.com。
张利凯（1993—），男，（E-mail）zhanglikai@cqu.edu.cn。
重庆市教委科学技术研究项目（KJQN202201805，KJQN202301801）；重庆市合川区科技计划项目（HCKJ-2024-110）；重庆人文科技学院科学技术研究项目（CRKZK2023007，JSJGC202201，JSJGC202205）。
HE Yingying
a. School of Computer Engineering; 1b. Big Data and Network Information Security Engineering Technology Research Center, Chongqing College of Huamnitics Science & Technology, Chongqing 401524, P. R. China; 2. College of Civil Engineering, Chongqing University, Chongqing 400045, P. R. China;HUANG Zhenghong
a. School of Computer Engineering; 1b. Big Data and Network Information Security Engineering Technology Research Center, Chongqing College of Huamnitics Science & Technology, Chongqing 401524, P. R. China; 2. College of Civil Engineering, Chongqing University, Chongqing 400045, P. R. China1.a. School of Computer Engineering; 1b. Big Data and Network Information Security Engineering Technology Research Center, Chongqing College of Huamnitics Science & Technology, Chongqing 401524, P. R. China; 2. College of Civil Engineering, Chongqing University, Chongqing 400045, P. R. China
Supported by the Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202201805, KJQN202301801), Science and Technology Program of Hechuan District of Chongqing(HCKJ-2024-110), and Science and Technology Research Program of Chongqing College of Humanities Science & Technology (CRKZK2023007, JSJGC202201, JSJGC202205).
在长期监测过程中，由于传感器设备故障、供能中断、网络传输问题等诸多因素，导致结构健康监测系统采集的数据存在不完整性。针对这一问题，结合集合经验模态分解（ensemble empirical mode decomposition，EEMD）与双向长短期记忆网络（bi-directional long short-term memory，BiLSTM）在时序处理方面的优势，提出一种基于EEMD-BiLSTM的结构监测缺失数据重构方法。该方法利用EEMD自适应分解监测时序数据为1组代表不同时间尺度的本征模态分量（intrinsic mode function，IMF），使非线性、非平稳序的时序信号平稳化。然后，将IMF分量输入到BiLSTM网络中进行缺失数据重构，提高BiLSTM预测精度。针对6层框架结构缩尺模型和Benchmark有限元仿真模型进行分析，试验结果表明，相比EEMD-LSTM、BiLSTM、LSTM主流方法，提出的EEMD-BiLSTM具有最高预测精度，在5%、10%、15%缺失数据情况下，其R2指标保持在0.8以上。因此，采用EEMD方法对非稳态结构加速响应数据进行预处理，可有效提高BiLSTM的预测精度，对于结构监测缺失数据问题，提供更具适应性的方法。
In long-term monitoring processes, the structural health monitoring(SHM) system often encounters data incompleteness due to various factors, including sensor malfunctions, power interruptions, and network transmission issues. To address this challenge, this study proposes a missing data reconstruction method for structural monitoring based on ensemble empirical mode decomposition(EEMD) and bidirectional long short-term memory(BiLSTM) networks, leveraging their advantages in time-series processing. The proposed approach utilizes EEMD to adaptively decompose the monitoring time-series data into a set of intrinsic mode functions (IMFs), each representing different time scales. This decomposition effectively transforms the nonlinear and non-stationary time-series signals into stationary components. The IMFs are then input into a BiLSTM network for missing data reconstruction, enhancing the prediction accuracy of the BiLSTM model. Analysis is conducted on a six-story scaled structural model and a benchmark finite element simulation model. Experimental results demonstrate that, compared to the mainstream methods such as EEMD-LSTM, BiLSTM, and LSTM, the proposed EEMD-BiLSTM approach achieves the highest prediction accuracy. In cases of 5%, 10% and 15% missing data, the R2 value remains above 0.8. Therefore, the use of the EEMD method for preprocessing non-stationary structural acceleration response data significantly improves the prediction accuracy of BiLSTM, providing a more adaptive solution to the problem of missing data in structural monitoring.
何盈盈,黄正洪,张利凯,赵智航,关腾达.融合
