Damage identification of bridge structures using the Hilbert-Huang Transform

被引:0
作者
Moughty, J. J. [1 ]
Casas, J. R. [1 ]
机构
[1] Tech Univ Catalonia BarcelonaTech, Dept Civil & Environm Engn, Catalonia, Spain
来源
LIFE-CYCLE ANALYSIS AND ASSESSMENT IN CIVIL ENGINEERING: TOWARDS AN INTEGRATED VISION | 2019年
关键词
EMPIRICAL MODE DECOMPOSITION;
D O I
暂无
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The majority of bridge condition assessment methods from acceleration data incorporate the use of the Fourier Transform (FT) to obtain or assess damage sensitive features, however the accuracy of the FT's output for non-linear and non-stationary signals can causes a problem for real-world structural applications. The Hilbert-Huang Transform (HHT) has long been cited as a potential alternative to the FT for non-linear, non-stationary signals and has gathered popularity in the condition assessment of rotating machinery due to its time-frequency-energy representation. On the other hand, instances of the HHT being applied to bridge structures has been less common, predominantly due to the inconsistency of the required Empirical Mode Decomposition (EMD) phase of the methodology. The present paper utilises recent advancements in EMD methodology through the application of Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), which considerably reduces the undesirable decomposition effects of signal noise contamination. A novel damage parameter is proposed that utilizes the HHT's instantaneous outputs to successfully achieve damage identification in a real bridge structure subjected to a progress damage test under single vehicle excitation.
引用
收藏
页码:1239 / 1246
页数:8
相关论文
共 50 条
  • [41] Gear fault identification based on Hilbert-Huang transform and SOM neural network
    Cheng, Gang
    Cheng, Yu-long
    Shen, Li-hua
    Qiu, Jin-bo
    Zhang, Shuai
    MEASUREMENT, 2013, 46 (03) : 1137 - 1146
  • [42] Ultrasonic rock microcracking characterization and classification using Hilbert-Huang transform
    Ezzeiri, Soufien
    Hamdi, Essaieb
    INNOVATIVE INFRASTRUCTURE SOLUTIONS, 2020, 5 (03)
  • [43] Multi-scale analysis of streamflow using the Hilbert-Huang Transform
    Di, Chongli
    Yang, Xiaohua
    Zhang, Xuejun
    He, Jun
    Mei, Ying
    INTERNATIONAL JOURNAL OF NUMERICAL METHODS FOR HEAT & FLUID FLOW, 2014, 24 (06) : 1363 - 1377
  • [44] Biomechanical Analysis of Golf Swing Motion Using Hilbert-Huang Transform
    Dong, Ran
    Ikuno, Soichiro
    SENSORS, 2023, 23 (15)
  • [45] Estimation of transitory changes in bending stiffness using the Hilbert-Huang transform
    Gonzalez, A.
    Aied, H.
    BRIDGE STRUCTURES, 2019, 15 (04) : 161 - 180
  • [46] Fast Protection Scheme For Distribution System using Hilbert-Huang Transform
    Shaik, Mahmood
    Yadav, Sandeep Kumar
    Shaik, Abdul Gafoor
    2019 IEEE 16TH INDIA COUNCIL INTERNATIONAL CONFERENCE (IEEE INDICON 2019), 2019,
  • [47] Decision thresholding on fMRI activation maps using the Hilbert-Huang transform
    Kuo, Po-Chih
    Liou, Michelle
    JOURNAL OF NEURAL ENGINEERING, 2022, 19 (04)
  • [48] Periodicity of flare index revisited using the Hilbert-Huang transform method
    Gao, P. X.
    Liang, H. F.
    Zhu, W. W.
    NEW ASTRONOMY, 2011, 16 (03) : 147 - 151
  • [49] Distinction between essential and physiological tremor using Hilbert-Huang transform
    Ayache, S. S.
    Al-ani, T.
    Lefaucheur, J. -P.
    NEUROPHYSIOLOGIE CLINIQUE-CLINICAL NEUROPHYSIOLOGY, 2014, 44 (02): : 203 - 212
  • [50] Phase demodulation using adaptive windowed Fourier transform based on Hilbert-Huang transform
    Wang, Chenxing
    Da, Feipeng
    OPTICS EXPRESS, 2012, 20 (16): : 18459 - 18477