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| Restoring method of structural abnormal monitoring data based on GRU neural network |
| JU Hanwen1, DENG Yang1,2, LI Aiqun1,2 |
| 1.School of Civil & Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China；
2.Beijing Energy Conservation & Sustainable Urban and Rural Development Provincial and Ministry Co-construction Collaboration Innovation Center, Beijing University of Civil Engineering and Architecture, Beijing 100044, China； |
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Abstract There are usually a large number of anomaly monitoring data in structural health monitoring systems. To ensure the integrity and practicability of data, it is necessary to restore anomaly monitoring data. Most studies on restoring anomaly data based on deep learning usually used single input dimension and unidirectional prediction to build models. This paper proposed a restoration method of structural anomaly monitoring data based on a Gated Recurrent Unit (GRU) neural network. The advantage of deep learning neural network to deal with the complex nonlinear mapping problem was fully utilized in this method by optimizing and reconstructing GRU neural network. The configurations of input and output of neural network were optimized by using the correlations of temperature and time series. Meanwhile a bidirectional sequence prediction method by using the information before and after anomaly data sequences was proposed to improve the prediction and restoration accuracy. At last, the proposed method was verified based on the strain, crack, and temperature monitoring data of an ancient city wall. The reconstructed GRU neural network model was used to restore the anomaly data sequences, and the restoration accuracy was compared with Long and Short-Term Memory (LSTM) neural network and Back Propagation (BP) neural network. The results show that compared with the neural network model of single input dimension and unidirectional prediction, the reconstructed GRU neural network has better prediction accuracy. And the prediction accuracy of the reconstructed GRU neural network is also significantly better than that of LSTM neural network and BP neural network. After anomaly data sequences are restored, the linear correlation of structural temperature and responses including strain and crack width gets greatly enhanced. The proposed method has a great ability to restore structural monitoring data with temperature correlation.
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Received: 19 April 2022
Published: 15 May 2023
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[1] 荆根强，段发阶，彭 璐，等. 基于被动激励的结构应变监测系统在线校准方法[J]. 仪器仪表学报，2021,41(08):137-145.
JING Gen-qiang, DUAN Fa-jie, PENG Lu, et al. On-line calibration method for structural strain monitoring system based on passive excitation [J]. Chinese Journal of Scientific Instrument, 2021, 41(08): 137-145.
[2] 王 莉，吴 桐. 基于光纤传感技术的混凝土结构深层裂缝监测研究[J]. 激光杂志，2020, 41(11): 193-197.
WANG Li, WU Tong. Study on the monitoring of deep crack of concrete structure based on fiber-optic sensing technology [J]. Laser Journal, 2020, 41(11): 193-197.
[3] 马 帜，罗尧治，万华平，等. 基于概率主成分分析的结构健康监测数据修复方法研究[J]. 振动与冲击，2021, 40(21): 135-141+167.
MA Zhi, LUO Yao-zhi, WAN Hua-ping, et al. Repair method of structural health monitoring data based on probabilistic principal component analysis [J]. Journal of vibration and shock, 2021, 40(21): 135-141+167.
[4] 吴 迪. 基于深度学习的结构健康监测系统异常数据识别方法研究[J]. 智能城市，2020, 6(20): 10-13.
WU Di. Research on abnormal data identification method of structural health monitoring system based on deep learning [J]. Intelligent City, 2020, 6(20): 10-13.
[5] 任 普. 基于大数据的桥梁监测数据清洗方法研究[D]. 南京：东南大学，2019.
REN Pu. Research on the data cleansing methods for bridge monitoring data based on big-data platform [D]. Nanjing: Southeast University, 2019.
[6] 高照明. 结构健康监测数据异常诊断与修复方法研究[D]. 大连：大连理工大学，2020.
GAO Zhao-ming. Research on anomaly diagnosis and repair methods of structural health monitoring data [D]. Dalian: Dalian University of Technology, 2020.
[7] Eskelson BNI, Temesgen H, LeMay V, et al. The roles of nearest neighbor methods in imputing missing data in forest inventory and monitoring databases [J]. Scandinavian Journal of Forest Research, 2009, 24(3): 235-246.
[8] 陆化普，屈闻聪，孙智源. 基于S-G滤波的交通流故障数据识别与修复算法[J]. 土木工程学报，2015, 48(05): 123-128.
LU Hua-pu, QU Wen-cong, SUN Zhi-yuan. Detection and repair algorithm of traffic erroneous data based on S-G filtering [J]. China Civil Engineering Journal, 2015, 48(05): 123-128.
[9] Farreras-Alcover I, Chryssanthopoulos MK, Andersen JE. Regression models for structural health monitoring of welded bridge joints based on temperature, traffic and strain measurements [J]. Structural Health Monitoring, 2015, 14(6): 648-62.
[10] Wan HP, Ni YQ. Bayesian multi-task learning methodology for reconstruction of structural health monitoring data [J]. Structural Health Monitoring, 2018, 18(8): 147592171879495.
[11] Zhang Z, Luo Y. Restoring method for missing data of spatial structural stress monitoring based on correlation [J]. Mechanical Systems and Signal Processing, 2017, 91: 266-277.
[12] Li LC, Liu HL, Zhou HJ, et al. Missing data estimation method for time series data in structure health monitoring systems by probability principal component analysis [J]. Advances in Engineering Software, 2020, 149: 102901.
[13] 谢晓凯，罗尧治，张 楠. 基于神经网络的大跨度空间钢结构应力实测缺失数据修复方法研究[J]. 空间结构，2019, 25(03): 38-44.
XIE Xiao-kai, LUO Yao-zhi, ZHANG Nan, et al. Missing data reconstruction in stress monitoring of steel spatial structures using neural network techniques [J]. Spatial Structures, 2019, 25(03): 38-44.
[14] 杜曼玲，高嘉欣，张礼兵，等. 大坝监测数据的时序预测与补全[J]. 水力发电，2020, 46(11): 111-115.
DU Man-ling, GAO Jia-xin, ZHANG Li-bing, et al. Time series forecasting and imputation of dam physical quantities [J]. Water Power, 2020, 46(11): 111-115.
[15] Li S, Li S, Laima S, et al. Data driven modeling of bridge buffeting in the time domain using long short-term memory network based on structural health monitoring [J]. Structural Control and Health Monitoring, 2021, 28: e2772.
[16] Jeong S, Ferguson M, Hou R, et al. Sensor data reconstruction using bidirectional recurrent neural network with application to bridge monitoring [J]. Advanced Engineering Informatics, 2019, 42(10): 100991.1-100991.14.
[17] 盖彤彤，曾 森，于德湖，等. 索力振动法测量神经网络赋泛研究[J]. 工程科学与技术，2021, 53(04): 118-127.
GAI Tong-tong, ZENG Sen, YU De-hu, et al. Research on neural network generalization of cable force vibration measurement [J]. Advanced Engineering Sciences, 2021, 53(04): 118-127.
[18] Yue ZX, Ding YL, Zhao HW. Deep learning-based minute-scale digital prediction model of temperature-induced deflection of a cable-stayed bridge: case study [J]. Journal of Bridge Engineering, 2021, 26(6): 05021004.
[19] Yang JX, Yang F, Zhou YX, et al. A data-driven structural damage detection framework based on parallel convolutional neural network and bidirectional gated recurrent unit [J]. Inform Sciences. 2021, 566: 103-117.
[20] Chen JL, Jing HJ, Chang YH, et al. Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process [J]. Reliability Engineering and System Safety, 2019, 185: 372-382.
[21] 姚德臣，李博阳，刘恒畅，等. 基于注意力GRU算法的滚动轴承剩余寿命预测[J]. 振动与冲击，2021,40(17): 116-123.
YAO De-chen, LI Bo-yang, LIU Heng-chang, et al. Residual life prediction of rolling bearing based on attention GRU algorithm [J]. Journal of Vibration and Shock, 2021, 40(17): 116-123.
[22] 孟恒宇，李元祥. 基于Transformer重建的时序数据异常检测与关系提取[J]. 计算机工程，2021, 47(02): 69-76.
MENG Heng-yu, LI Yuan-xiang. Anomaly detection and relation extraction for time series data based on transformer reconstruction [J]. Computer Engineering, 2021, 47(02): 69-76.
[23] Yue ZX, Ding YL, Zhao HW, et al. Mechanics-guided optimization of an LSTM network for real-time modeling of temperature-induced deflection of a cable-stayed bridge [J]. Engineering Structures, 2022. DOI:10.1016/j.engstruct.2021. 113619.
[24] Zhao HW, Ding YL, Li AQ, et al. Digital modeling on the nonlinear mapping between multi-source monitoring data of in-service bridges [J]. Structural Control and Health Monitoring, 2020, 27: e2618.
[25] 郑 直，张华钦，潘 月. 基于改进鲸鱼算法优化LSTM的滚动轴承故障诊断[J]. 振动与冲击，2021, 40(07): 274-280.
ZHENG Zhi, ZHANG Hua-qin, PAN Yue. Rolling bearing fault diagnosis based on IWOA-LSTM [J]. Journal of Vibration and Shock, 2021, 40(07): 274-280.
[26] Ni YQ, Zhou HF, Ko JM. Generalization capability of neural network models for temperature-frequency correlation using monitoring data [J]. Journal of Structural Engineering, 2009, 135(10): 1290-1300.
[27] Chang YS, Chiao HT, Abimannan S, et al. An LSTM-based aggregated model for air pollution forecasting [J]. Atmospheric Pollution Research, 2020, 11: 1451-1463.
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. [J]. JOURNAL OF VIBRATION AND SHOCK, 2024, 43(3): 8-. |
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