Skip to main content
SpringerLink
Log in

Autoregressive Model-Based Structural Damage Identification and Localization Using Convolutional Neural Networks

  • Structural Engineering
  • Published:
KSCE Journal of Civil Engineering Aims and scope Submit manuscript

Abstract

Traditional autoregressive (AR) model-based damage identification methods construct structural damage sensitive features by trial and error, which are time-consuming, laborious and may lead to poor recognition effect. This study applies convolutional neural networks (CNNs) to quickly and automatically extract high-dimensional features of autoregressive model coefficients (ARMCs). In this research, AR model was utilized to fit the acceleration time series. The input matrices marked with damage location were produced by ARMCs, and then those matrices were sent to the proposed CNN for training. The trained CNN was employed for damage identification and localization. The effectiveness of the proposed method was verified by the damage identification and localization of a three-storied frame structure. The performance of the proposed CNN was compared with multilayer perception (MLP), random forest, and support vector machine (SVM). Meanwhile, the prediction results from different sample types were also discussed. Furthermore, parametric study in relation to the number of accelerometers and ARMCs used is conducted. These analyses demonstrate that the accuracy of CNN tests results reach 100%, 6.67%, 20%, and 25% higher than that of MLP, random forest, and SVM, respectively. Besides, other metrics calculated in this paper (e.g., precision, recall) further indicate that the proposed CNN performs well. The combination of AR and CNN does show excellent performance in damage identification and localization, which seems to be able to resist external excitation changes and accurately identify the multi-location damage and minor damage using limited accelerometers and ARMCs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Alpaydin E (2004) Introduction to machine learning (adaptive computation and machine learning). MIT Press, Boston, MA, USA, 302–309

    Google Scholar 

  • Alvandi A, Cremona C (2006) Assessment of vibration-based damage identification techniques. Journal of Sound and Vibration 292(1–2): 179–202, DOI: 10.1016/j.jsv.2005.07.036

    Article  Google Scholar 

  • Atha DJ, Jahanshahi MR (2017) Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection. Structural Health Monitoring 17(5):1110–1128, DOI: 10.1177/1475921717737051

    Article  Google Scholar 

  • Bao C, Hao H, Li Z (2013a) Integrated ARMA model method for damage detection of subsea pipeline system. Engineering Structures 48:176–192, DOI: 10.1016/j.engstruct.2012.09.033

    Article  Google Scholar 

  • Bao C, Hao H, Li Z (2013b) Vibration-based structural health monitoring of offshore pipelines: Numerical and experimental study. Structural Control & Health Monitoring 20(5):769–788, DOI: 10.1002/stc.1494

    Article  Google Scholar 

  • Bengio Y (2012) Practical recommendations for gradient-based training of deep architectures. In: Montavon G, Orr GB, Müller KR (eds) Neural networks: Tricks of the trade. Lecture notes in computer science. Springer, Berlin, Germany, 437–478

    Chapter  Google Scholar 

  • Brockwell PJ, Davis RA (2002) Introduction to time series and forecasting, 2nd edition. Springer, New York, NY, USA, 112–120

    Book  MATH  Google Scholar 

  • Cha YJ, Choi W, Büyüköztürk O (2017) Deep learning-based crack damage detection using convolutional neural networks. Computer-Aided Civil and Infrastructure Engineering 32(5):361–378, DOI: 10.1111/mice.12263

    Article  Google Scholar 

  • Chao SH, Loh CH (2013) Vibration-based damage identification of reinforced concrete member using optical sensor array data. Structural Health Monitoring 12(5–6):397–410, DOI: 10.1177/1475921713501109

    Article  Google Scholar 

  • Chen C, Jahanshahi MR (2018) NB-CNN: Deep learning-based crack detection using convolutional neural network and Naïve Bayes data fusion. IEEE Transactions on Industrial Electronics 65(5):4392–4400, DOI: 10.1109/TIE.2017.2764844

    Article  Google Scholar 

  • Ciresan DC, Meier U, Masci J, Gambardella LM, Schmidhuber J (2011) Flexible, high performance convolutional neural networks for image classification. Proceedings of the 22nd international joint conference on artificial intelligence, July 16–22, Barcelona, Spain

    Google Scholar 

  • Datteo A, Busca G, Quattromani G, Cigada A (2018) On the use of AR models for SHM: A global sensitivity and uncertainty analysis framework. Reliability Engineering and System Safety 170:99–115, DOI: 10.1016/j.ress.2017.10.017

    Article  Google Scholar 

  • Farrar CR, Worden K (2013) Structural health monitoring: A machine learning perspective. John Wiley & Sons, Inc., Hoboken, NJ, USA, 2–15

    Google Scholar 

  • Fujita A, Sakurada K, Imaizumi T, Ito R, Hikosaka S, Nakamura R (2017) Damage detection from aerial images via convolutional neural networks. Proceedings of 2017 fifteenth IAPR international conference on machine vision applications (MVA), May 8–12, Nagoya, Japan

    Book  Google Scholar 

  • Hoell S, Omenzetter P (2016) Optimal selection of autoregressive model coefficients for early damage detectability with an application to wind turbine blades. Mechanical Systems and Signal Processing 70–71:557–577, DOI: 10.1016/j.ymssp.2015.09.007

    Article  Google Scholar 

  • Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the 32nd international conference on international conference on machine learning, July 7, Lille, France

    Google Scholar 

  • Jonathan DC, Kung-Silk C (2008) Time series analysis with applications in R, 2nd edition. Springer, New York, NY, USA, 109–188

    Google Scholar 

  • Kiani J, Camp C, Pezeshk S (2019) On the application of machine learning techniques to derive seismic fragility curves. Computers & Structures 218(7):108–122, DOI: 10.1016/j.compstruc.2019.03.004

    Article  Google Scholar 

  • Kim Y, Chong WJ, Chon KH, Kim J (2013) Wavelet-based AR–SVM for health monitoring of smart structures. Smart Materials and Structures 22(1):1–12, DOI: 10.1088/0964-1726/22/1/015003

    Google Scholar 

  • Kiremidjian AS, Kiremidjian G, Sarabandi P (2011) A wireless structural monitoring system with embedded damage algorithms and decision support system. Structure and Infrastructure Engineering 7(12): 881–894, DOI: 10.1080/15732470903208773

    Article  Google Scholar 

  • Kosorus H, Hollrigl-Binder M, Allmer H, Kung J (2012) On the identification of frequencies and damping ratios for structural health monitoring using autoregressive models. Proceedings of 2012 23rd international workshop on database and expert systems applications, September 3–7, Vienna, Austria

    Book  Google Scholar 

  • Krizhevsky A, Sutskever I, Hinton G (2012) ImageNet classification with deep convolutional neural networks. Proceedings of the 25th international conference on neural information processing systems, December 3–6, Lake Tahoe, NV, USA

    Google Scholar 

  • Lautour ORD, Omenzetter P (2010) Damage classification and estimation in experimental structures using time series analysis and pattern recognition. Mechanical Systems and Signal Processing 24(5): 1556–1569, DOI: 10.1016/j.ymssp.2009.12.008

    Article  Google Scholar 

  • Lawhern V, Hairston WD, Mcdowell K, Westerfield M, Robbins K (2012) Detection and classification of subject-generated artifacts in EEG signals using autoregressive models. Journal of Neuroscience Methods 208(2):181–189, DOI: 10.1016/j.jneumeth.2012.05.017

    Article  Google Scholar 

  • Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553): 436–444, DOI: 10.1038/nature14539

    Article  Google Scholar 

  • Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11): 2278–2324, DOI: 10.1109/5.726791

    Article  Google Scholar 

  • Li Z, Yan X, Yuan C, Peng Z, Li L (2011) Virtual prototype and experimental research on gear multi-fault diagnosis using wavelet-autoregressive model and principal component analysis method. Mechanical Systems and Signal Processing 25(7):2589–2607, DOI: 10.1016/j.ymssp.2011.02.017

    Article  Google Scholar 

  • Liu S, Liu T, Zhou J, Chen L, Yang X (2019) Relationship between shear-stress distribution and resulting acoustic-emission variation along concrete joints in prefabricated girder structures. Engineering Structures 196:109319, DOI: 10.1016/j.engstruct.2019.109319

    Article  Google Scholar 

  • Liu J, Yin L, He C, Wen B, Hong X, Li Y (2018) A multiscale autoregressive model-based electrocardiogram identification method. IEEE Access 6:18251–18263, DOI: 10.1109/ACCESS.2018.2820684

    Article  Google Scholar 

  • Ljung GM, Box GEP (1978) On a measure of lack of fit in time series models. Biometrika 65(2):297–303, DOI: 10.2307/2335207

    Article  MATH  Google Scholar 

  • Los Alamos National Laboratory (2019) Los Alamos national laboratory engineering institute. Los Alamos National Laboratory, Retrieved October 25, 2019, https://www.lanl.gov/projects/national-security-education-center/engineering/ei-software-downlo-ad

    Google Scholar 

  • Lynch JP (2004) Linear classification of system poles for structural damage detection using piezoelectric active sensors. Proceedings of Spie the international society for optical engineering, July 29, San Diego, CA, USA

    Book  Google Scholar 

  • Martinez-Hernandez HG, Aljama-Corrales CT, Gonzalez-Camarena R, Charleston-Villalobos VS, Chi-Lem G (2005) Computerized classification of normal and abnormal lung sounds by multivariate linear autoregressive model. Proceedings of 2005 IEEE engineering in medicine and biology 27th annual conference, January 17–18, Shanghai, China

    Book  Google Scholar 

  • Modarres C, Astorga N, Droguett EL, Meruane V (2018) Convolutional neural networks for automated damage recognition and damage type identification. Structural Control & Health Monitoring 25(10), DOI: 10.1002/stc.2230

    Google Scholar 

  • Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th international conference on machine learning, June 21–24, Haifa, Israel

    Google Scholar 

  • Nair KK, Kiremidjian AS, Law KH (2006) Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure. Journal of Sound and Vibration 291(1–2):349–368, DOI: 10.1016/j.jsv.2005.06.016

    Article  Google Scholar 

  • Nitze I, Barrett B, Cawkwell F (2015) Temporal optimisation of image acquisition for land cover classification with Random Forest and MODIS time-series. International Journal of Applied Earth Observation and Geoinformation 34:136–146, DOI: 10.1016/j.jag.2014.08.001

    Article  Google Scholar 

  • Roy K, Bhattacharya B, Ray-Chaudhuri S (2015) ARX model-based damage sensitive features for structural damage localization using output-only measurements. Journal of Sound and Vibration 349:99–122, DOI: 10.1016/j.jsv.2015.03.038

    Article  Google Scholar 

  • Scherer D, Müller A, Behnke S (2010) Evaluation of pooling operations in convolutional architectures for object recognition. In: Diamantaras K, Duch W, Iliadis LS (eds) Artificial neural networks–ICANN 2010. Springer, Berlin, Germany, 92–101

    Chapter  Google Scholar 

  • Shang X, Tian Y, Li Y (2011) Feature extraction and classification of sEMG based on ICA and EMD decomposition of R model. Proceedings of 2011 international conference on electronics, communications and control, September 9–11, Ningbo, China

    Google Scholar 

  • Soukup D, Huber-Mörk R (2014) Convolutional neural networks for steel surface defect detection from photometric stereo images In: Bebis G, Boyle R, Parvin B, Koracin D, Fowlkes C, Wang S, Choi MH, Mantler S, Schulze J, Acevedo D, Mueller K, Papka M (eds) Advances in visual computing. ISVC 2014, Springer, Cham, Switzerland, 668–677

    Google Scholar 

  • Spencer BF Jr, Ruiz-Sandoval ME, Kurata N (2004) Smart sensing technology: Opportunities and challenges. Structural Control & Health Monitoring 11(4):349–368, DOI: 10.1002/stc.48

    Article  Google Scholar 

  • Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research 15(6):1929–1958

    MathSciNet  MATH  Google Scholar 

  • Tong Z, Gao J, Zhang H (2017) Recognition, location, measurement, and 3D reconstruction of concealed cracks using convolutional neural networks. Construction and Building Materials 146:775–787, DOI: 10.1016/j.conbuildmat.2017.04.097

    Article  Google Scholar 

  • Wang C, Kang Y, Shen P, Chan Y, Chung Y (2010) Applications of fault diagnosis in rotating machinery by using time series analysis with neural network. Expert Systems with Applications 37(2):1696–1702, DOI: 10.1016/j.eswa.2009.06.089

    Article  Google Scholar 

  • Wei WWS (2006) Time series analysis: univariate and multivariate methods, 2nd edition. Pearson Addison Wesley, Boston, MA, USA, 6–15

    Google Scholar 

  • Xu Y, Bao Y, Chen J, Zuo W, Li H (2018) Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images. Structural Health Monitoring 18(3):653–674, DOI: 10.1177/1475921718764873

    Article  Google Scholar 

  • Xu Y, Wei S, Bao Y, Li H (2019) Automatic seismic damage identification of reinforced concrete columns from images by a region-based deep convolutional neural network. Structural Control & Health Monitoring 26(3), DOI: 10.1002/stc.2313

    Google Scholar 

  • Yao R, Pakzad SN (2012) Autoregressive statistical pattern recognition algorithms for damage detection in civil structures. Mechanical Systems and Signal Processing 31:355–368, DOI: 10.1016/j.ymssp.2012.02.014

    Article  Google Scholar 

Download references

Acknowledgements

This study was supported by the National Key Research and Development Program of China (2018YFB1600301), the National Natural Science Foundation of China (51908094, 51978111), the Chongqing Natural Science Foundation of China (cstc2019jcyj-cxttX0004, cstc2019jscx-gksbX0047, cstc2018jscx-mszdX0084) and the Science and Technology Project of Guizhou Provincial Transportation Department (2018-122-013).

Author information

Authors and Affiliations

  1. State Key Laboratory of Mountain Bridges and Tunnel Engineering, Chongqing Jiaotong University, Chongqing, 400074, China

    Qizhi Tang, Jianting Zhou, Jingzhou Xin & Siyu Zhao

  2. Yunnan Wuyi Expressway Construction Command, Kunming, 650000, China

    Yingxin Zhou

Authors
  1. Qizhi Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianting Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jingzhou Xin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Siyu Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yingxin Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianting Zhou.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tang, Q., Zhou, J., Xin, J. et al. Autoregressive Model-Based Structural Damage Identification and Localization Using Convolutional Neural Networks. KSCE J Civ Eng 24, 2173–2185 (2020). https://doi.org/10.1007/s12205-020-2256-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12205-020-2256-7

Keywords

Search

Navigation