Structural damage quantification using long short-term memory (LSTM) auto-encoder and impulse response functions

被引：0
作者：
Chencho [1 ]
Li J. [1 ]
Hao H. [1 ,2 ]
机构：
[1] Centre for Infrastructure Monitoring and Protection, School of Civil and Mechanical Engineering, Curtin University, Kent Street, Bentley
[2] Earthquake Engineering Research and Test Center, Guangzhou University, Guangzhou
来源：
Journal of Infrastructure Intelligence and Resilience | 2024年 / 3卷 / 02期
基金：
澳大利亚研究理事会;
关键词：
Auto-encoder; Damage identification; Impulse response function; Long short-term memory; Stiffness reduction; Time domain response;
D O I：
10.1016/j.iintel.2024.100086
中图分类号：
学科分类号：
摘要：
This paper presents an approach for structural damage quantification using a long short-term memory (LSTM) auto-encoder and impulse response functions (IRF). Among time domain responses-based methods for structural damage identification, using IRF is advantageous over the original time domain responses, since IRF consists of information of system properties and is loading effect independent. In this study, IRFs are extracted from the acceleration responses measured from different locations of structures under impact force excitations. The obtained IRFs are concatenated. Moving averaging with a suitable window size is performed to reduce random variations in the concatenated responses. Further, principal component analysis is performed for dimensionality reduction. These selected principal components are then fed to the LSTM auto-encoder for structural damage identification. A noise layer is added as an input layer to the LSTM auto-encoder to regularise the model. The proposed model consists of two phases: (1) reconstruction of the selected “principal components” to extract the features; and (2) damage identification of structural elements. Numerical studies are conducted to verify the accuracy of the proposed approach. The results demonstrate that the proposed approach can accurately identify and quantify structural damage for both single- and multiple-element damage cases with noisy measurements, as well as uncertainties in the stiffness parameters. Furthermore, the performance of the proposed approach is evaluated using the limited measurements from a few sensors. © 2024 The Authors
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  • [21] Kan M.S., Tan A.C.C., Mathew J., A review on prognostic techniques for non-stationary and non-linear rotating systems, Mech. Syst. Signal Process., 62-63, pp. 1-20, (2015)
  • [22] Khan S., Yairi T., A review on the application of deep learning in system health management, Mech. Syst. Signal Process., 107, pp. 241-265, (2018)
  • [23] Khodabandehlou H., Pekcan G., Sami Fadali M., Vibration-based structural condition assessment using convolution neural networks, Struct. Control Health Monit., 26, 2, (2019)
  • [24] Law S.S., Li X.Y., Wavelet-based sensitivity analaysis of the impulse response function for damage detection, J. Appl. Mech., 74, 2, pp. 375-377, (2007)
  • [25] LeCun Y., Bengio Y., Hington G., Deep learning, Nature, 521 7553), pp. 436-444, (2015)
  • [26] Lee E.W.M., Lam H.F., Intelligent-based structural damage detection model, Mech. Adv. Mater. Struct., 18, 8, pp. 590-596, (2011)
  • [27] Li J., Hao H., Fan X., Structural damage identification with extracted impulse response functions and optimal sensor locations, Electron. J. Struct. Eng., 14, 1, pp. 123-132, (2015)
  • [28] Li P., Zhou B., Wang C., Hu G., Yan Y., Guo R., Xia H., CNN-based pavement defects detection using grey and depth images, Autom. Constr., 158 105192, (2024)
  • [29] Li X.Y., Law S.S., Structural damage detection with statistical analysis from support excitation, Mech. Syst. Signal Process., 22, 8, pp. 1793-1808, (2008)
  • [30] Lin J.F., Wang J., Wang L.X., Law S.S., Structural damage diagnosis-oriented impulse response function estimation under seismic excitations, Sensors, 19, 24, (2019)
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