Digital image correlation-based structural state detection through deep learning

Abstract

This paper presents a new approach for automatical classification of structural state through deep learning. In this work, a Convolutional Neural Network (CNN) was designed to fuse both the feature extraction and classification blocks into an intelligent and compact learning system and detect the structural state of a steel frame; the input was a series of vibration signals, and the output was a structural state. The digital image correlation (DIC) technology was utilized to collect vibration information of an actual steel frame, and subsequently, the raw signals, without further pre-processing, were directly utilized as the CNN samples. The results show that CNN can achieve 99% classification accuracy for the research model. Besides, compared with the backpropagation neural network (BPNN), the CNN had an accuracy similar to that of the BPNN, but it only consumes 19% of the training time. The outputs of the convolution and pooling layers were visually displayed and discussed as well. It is demonstrated that: 1) the CNN can extract the structural state information from the vibration signals and classify them; 2) the detection and computational performance of the CNN for the incomplete data are better than that of the BPNN; 3) the CNN has better anti-noise ability.

Graphical abstract

Keywords

structural state detection / deep learning / digital image correlation / vibration signal / steel frame

References

[1] Zhang Y, Miyamori Y, Mikami S, Saito T. Vibration-based structural state identification by a 1-dimensional convolutional neural network. Computer-Aided Civil and Infrastructure Engineering, 2019, 34( 9): 822– 839

[2] Guo H, Zheng H, Zhuang X. Numerical manifold method for vibration analysis of Kirchhoff’s plates of arbitrary geometry. Applied Mathematical Modelling, 2019, 66 : 695– 727

[3] Jiang X, Adeli H. Intelligent Infrastructure-Neural Networks, Wavelets, and Chaos Theory. Boca Raton: CRC Press, Taylor & Francis, 2008

[4] Cawley P, Adams R D. The location of defects in structures from measurements of natural frequencies. Journal of Strain Analysis for Engineering Design, 1979, 14( 2): 49– 57

[5] Katunin A. Identification of structural damage using S-transform from 1D and 2D mode shapes. Measurement, 2021, 173 : 108656–

[6] Cha Y, Buyukozturk O. Structural damage detection using modal strain energy and hybrid multiobjective optimization. Computer-Aided Civil and Infrastructure Engineering, 2015, 30( 5): 347– 358

[7] Dutta A, Talukdar S. Damage detection in bridges using accurate modal parameters. Finite Elements in Analysis and Design, 2004, 40( 3): 287– 304

[8] Al-Jailawi S, Rahmatalla S. Transmissibility-based damage detection using angular velocity versus acceleration. Journal of Civil Structural Health Monitoring, 2018, 8( 4): 649– 659

[9] Hu W, Caetano E, Cunha Á. Structural health monitoring of a stress-ribbon footbridge. Engineering Structures, 2013, 57 : 578– 593

[10] Xia Y, Chen B, Weng S, Ni Y Q, Xu Y L. Temperature effect on vibration properties of civil structures: A literature review and case studies. Journal of Civil Structural Health Monitoring, 2012, 2( 1): 29– 46

[11] Sung S H, Koo K Y, Jung H J. Modal flexibility-based damage detection of cantilever beam-type structures using baseline modification. Journal of Sound and Vibration, 2014, 333( 18): 4123– 4138

[12] Jaishi B, Ren W X. Damage detection by finite element model updating using modal flexibility residual. Journal of Sound and Vibration, 2006, 290( 1−2): 369– 387

[13] Li J, Wu B, Zeng Q C, Lim C W. A generalized flexibility matrix based approach for structural damage detection. Journal of Sound and Vibration, 2010, 329( 22): 4583– 4587

[14] Lu Q, Ren G, Zhao Y. Multiple damage location with flexibility curvature and relative frequency change for beam structures. Journal of Sound and Vibration, 2002, 253( 5): 1101– 1114

[15] Pal J, Banerjee S. A combined modal strain energy and particle swarm optimization for health monitoring of structures. Journal of Civil Structural Health Monitoring, 2015, 5( 4): 353– 363

[16] Hu H, Wu C. Development of scanning damage index for the damage detection of plate structures using modal strain energy method. Mechanical Systems and Signal Processing, 2009, 23( 2): 274– 287

[17] Pandey A K, Biswas M, Samman M M. Damage detection from changes in curvature mode shapes. Journal of Sound and Vibration, 1991, 145( 2): 321– 332

[18] Anitescu C, Atroshchenko E, Alajlan N, Rabczuk T. Artificial neural network methods for the solution of second order boundary value problems. Computers, Materials & Continua, 2019, 59( 1): 345– 359

[19] Guo H, Zhuang X, Rabczuk T. A deep collocation method for the bending analysis of Kirchhoff plate. Computers, Materials & Continua, 2019, 59( 2): 433– 456

[20] Samaniego E, Anitescu C, Goswami S, Nguyen-Thanh V M, Guo H, Hamdia K, Zhuang X, Rabczuk T. An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications. Computer Methods in Applied Mechanics and Engineering, 2020, 362 : 112790–

[21] Xu H, Humar J M. Damage detection in a girder bridge by artificial neural network technique. Computer-Aided Civil and Infrastructure Engineering, 2006, 21( 6): 450– 464

[22] Yao X. Evolutionary Artificial Neural Networks. International Journal of Neural Systems, 1993, 4( 3): 203– 222

[23] Zhuang X, Guo H, Alajlan N, Zhu H, Rabczuk T. Deep autoencoder based energy method for the bending, vibration, and buckling analysis of Kirchhoff plates with transfer learning. European Journal of Mechanics. A, Solids, 2021, 87 : 104225–

[24] Lecun Y, Bengio Y. Convolutional Networks for Images, Speech, and Time Series. The Handbook of Brain Theory and Neural Networks. Cambridge: MIT Press, 1995

[25] Lécun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998, 86( 11): 2278– 2324

[26] Cruz P J S, Salgado R. Performance of Vibration-Based Damage Detection Methods in Bridges. Computer-Aided Civil and Infrastructure Engineering, 2009, 24( 1): 62– 79

[27] Abdeljaber O, Avci O, Kiranyaz S, Gabbouj M, Inman D J. Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. Journal of Sound and Vibration, 2017, 388 : 154– 170

[28] Teng S, Chen G, Gong P, Liu G, Cui F. Structural damage detection using convolutional neural networks combining strain energy and dynamic response. Meccanica, 2020, 55( 4): 945– 959

[29] Teng S, Chen G, Liu G, Lv J, Cui F. Modal strain energy-based structural damage detection using convolutional neural networks. Applied Sciences-Basel, 2019, 9( 16): 3376–

[30] Khuc T, Catbas N. Completely contactless structural health monitoring of real-life structures using cameras and computer vision. Structural Control and Health Monitoring, 2017, 24( 1): e1852–

[31] Wahbeh A M, Caffrey J P, Masri S F. A vision-based approach for the direct measurement of displacements in vibrating systems. Smart Materials and Structures, 2003, 12( 5): 785– 794

[32] Kim S W, Kim N S. Dynamic characteristics of suspension bridge hanger cables using digital image processing. NDT & E International, 2013, 59 : 25– 33

[33] Chu T C, Ranson W F, Sutton M A. Applications of digital-image-correlation techniques to experimental mechanics. Experimental Mechanics, 1985, 25( 3): 232– 244

[34] Pan B, Qian K, Xie H, Asundi A. Two-dimensional digital image correlation for in-plane displacement and strain measurement: A review. Measurement Science & Technology, 2009, 20( 6): 062001–

[35] Chen G, Wu Z, Gong C, Zhang J, Sun X. DIC-based operational modal analysis of bridges. Advances in Civil Engineering, 2021, 2021 : 1– 13

[36] Chen G, Liang Q, Zhong W, Gao X, Cui F. Homography-based measurement of bridge vibration using UAV and DIC method. Measurement, 2021, 170 : 108683–

[37] Zhang Y, Yang Y. Cross-validation for selecting a model selection procedure. Journal of Econometrics, 2015, 187( 1): 95– 112

[38] Geng X, Lu S, Jiang M, Sui Q, Lv S, Xiao H, Jia Y, Jia L. Researchon FBG-Based CFRP structural damage identification using BP neural network. Photonic Sensors, 2018, 8( 2): 168– 175

[39] Yan Y J, Cheng L, Wu Z Y, Yam L H. Development in vibration-based structural damage detection technique. Mechanical Systems and Signal Processing, 2007, 21( 5): 2198– 2211

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Higher Education Press 2021.