Innovative Sensing by Using Deep Learning Framework

Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)


Structures experience large vibrations and stress variations during their life cycles. This causes reduction in their load-carrying capacity which is the main design criteria for many structures. Therefore, it is important to accurately establish the performance of structures after construction that often needs full-field strain or stress measurements. Many traditional inspection methods collect strain measurements by using wired strain gauges. These strain gauges carry a high installation cost and have high power demand. In contrast, this paper introduces a new methodology to replace this high cost with utilizing inexpensive data coming from wireless sensor networks. The study proposes to collect acceleration responses coming from a structure and give them as an input to deep learning framework to estimate the stress or strain responses. The obtained stress or strain time series then can be used in many applications to better understand the conditions of the structures. In this paper, designed deep learning architecture consists of multi-layer neural networks and Long Short-Term Memory (LSTM). The network achieves to learn the relationship between input and output by exploiting the temporal dependencies of them. In the evaluation of the method, a three-story steel building is simulated by using various dynamic wind and earthquake loading scenarios. The acceleration time histories under these loading cases are utilized to predict the stress time series. The learned architecture is tested on acceleration time series that the structure has never experienced.


Structural health monitoring Long short-term memory Recurrent neural networks Deep neural network 



Research funding is partially provided by the National Science Foundation through Grant No. CMMI-1351537 by Hazard Mitigation and Structural Engineering program, and by a grant from the Commonwealth of Pennsylvania, Department of Community and Economic Development, through the Pennsylvania Infrastructure Technology Alliance (PITA). Martin Takáč was supported by National Science Foundation grant CCF-1618717 and CMMI-1663256.


  1. 1.
    Matarazzo, T.J., Shahidi, S.G., Chang, M., Pakzad, S.N.: Are today’s SHM procedures suitable for tomorrow’s bigdata? In: Structural Health Monitoring and Damage Detection, vol. 7, pp. 59–65. Springer, Heidelberg (2015)Google Scholar
  2. 2.
    Gulgec, N.S., Shahidi, S.G., Pakzad, S.N.: A comparative study of compressive sensing approaches for a structural damage diagnosis. In: Geotechnical and Structural Engineering Congress, pp. 1910–1919. American Society of Civil Engineers, Reston (2016)Google Scholar
  3. 3.
    Gulgec, N.S., Shahidi, G.S., Matarazzo, T.J., Pakzad, S.N.: Current challenges with bigdata analytics in structural health monitoring. In: Structural Health Monitoring & Damage Detection, vol. 7, pp. 79–84. Springer, Berlin (2017)CrossRefGoogle Scholar
  4. 4.
    Kim, S., Pakzad, S., Culler, D., Demmel, J., Fenves, G., Glaser, S., Turon, M.: Health monitoring of civil infrastructures using wireless sensor networks. In: Proceedings of the 6th International Conference on Information Processing in Sensor Networks, pp. 254–263. ACM (2007)Google Scholar
  5. 5.
    Giraldo, D.F., Dyke, S.J., Caicedo, J.M.: Damage detection accommodating varying environmental conditions. Struct. Health Monit. 5(2), 155–172 (2006)CrossRefGoogle Scholar
  6. 6.
    González, A., Covián, E., Madera, J.: Determination of bridge natural frequencies using a moving vehicle instrumented with accelerometers and GPS. In: Proceedings of the Ninth International Conference on Computational Structures Technology, CST2008, Athens, 2–5 Sept 2008. Civil-Comp Press (2008)Google Scholar
  7. 7.
    Lederman, G., Wang, Z., Bielak, J., Noh, H., Garrett, JH., Chen, S., Kovacevic, J., Cerda, F., Rizzo, P.: Damage quantification and localization algorithms for indirect SHM of bridges. In: Proceeding of International Conference on Bridge Maintenance, Safety Management, Shanghai (2014)CrossRefGoogle Scholar
  8. 8.
    Yoneyama, S., Kitagawa, A., Iwata, S., Tani, K., Kikuta, H.: Bridge deflection measurement using digital image correlation. Exp. Tech. 31(1), 34–40 (2007)CrossRefGoogle Scholar
  9. 9.
    Hild, F., Roux, S.: Digital Image Correlation. Wiley-VCH, Weinheim (2012)Google Scholar
  10. 10.
    Pakzad, S.N., Fenves, G.L., Kim, S., Culler, D.E.: Design and implementation of scalable wireless sensor network for structural monitoring. J. Infrastruct. Syst. 14(1), 89–101 (2008)CrossRefGoogle Scholar
  11. 11.
    Gers, F.A., Eck, D., Schmidhuber, J.: Applying LSTM to time series predictable through time-window approaches. In: Neural Nets WIRN Vietri-01, pp. 193–200. Springer, New York (2002)Google Scholar
  12. 12.
    Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014). arXiv:1409.3215Google Scholar
  13. 13.
    Graves, A., Jaitly, N.: Towards end-to-end speech recognition with recurrent neural networks. In: Proceedings of the 31st International Conference on Machine Learning (ICML-14), pp. 1764–1772 (2014)Google Scholar
  14. 14.
    Gulgec, N.S., Takáč, M., Pakzad, S.N.: Structural damage detection using convolutional neural networks. In: Model Validation and Uncertainty Quantification, vol. 3, pp. 331–337. Springer, Cham (2017)CrossRefGoogle Scholar
  15. 15.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  16. 16.
    Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)Google Scholar
  17. 17.
    Elman, J.L.: Finding structure in time. Cogn. Sci. 14(2), 179–211 (1990)CrossRefGoogle Scholar
  18. 18.
    Lipton, Z.C., Berkowitz, J., Elkan, C.: A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019 (2015)Google Scholar
  19. 19.
    Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157–166 (1994)CrossRefGoogle Scholar
  20. 20.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  21. 21.
    Graves, A., Mohamed, A.-R., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6645–6649. IEEE (2013)Google Scholar
  22. 22.
    Gers, F.A., Schmidhuber, J., Cummins, F.: Learning to forget: continual prediction with LSTM. Neural Comput. 12(10), 2451–2471 (2000)CrossRefGoogle Scholar
  23. 23.
    Elkordy, M.F., Chang, K.C., Lee, G.C.: Neural networks trained by analytically simulated damage states. J. Comput. Civ. Eng. 7(2), 130–145 (1993)CrossRefGoogle Scholar
  24. 24.
    Dong, B., Sause, R., Ricles, J.M.: Seismic response and performance of a steel MRF building with nonlinear viscous dampers under DBE and MCE. J. Struct. Eng. 142(6), 04016023 (2016)CrossRefGoogle Scholar
  25. 25.
    PEER NGA: Pacific Engineering Research Database (2008).
  26. 26.
    Field, E.H., Jordan, T.H., Cornell, C.A.: Opensha: a developing community-modeling environment for seismic hazard analysis. Seismol. Res. Lett., 74(4), 406–419 (2003)CrossRefGoogle Scholar
  27. 27.
    Carassale, L., Solari, G.: Monte carlo simulation of wind velocity fields on complex structures. J. Wind Eng. Ind. Aerodyn. 94(5), 323–339 (2006)CrossRefGoogle Scholar
  28. 28.
    Kaimal, J.C., Wyngaard, J.C., Izumi, Y., Cote, O.R.: Spectral characteristics of surface-layer turbulence. Q. J. R. Meteorol. Soc. 98(417), 563–589 (1972)CrossRefGoogle Scholar
  29. 29.
    American Society of Civil Engineers: Null null: Minimum Design Loads for Buildings and Other Structures, ASCE/SEI 7–10 edn. American Society of Civil Engineers (2013)Google Scholar
  30. 30.
    Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)Google Scholar
  31. 31.
    Werbos, P.J.: Backpropagation through time: what it does and how to do it. Proc. IEEE 78(10), 1550–1560 (1990)CrossRefGoogle Scholar

Copyright information

© The Society for Experimental Mechanics, Inc. 2019

Authors and Affiliations

  1. 1.Department of Civil and Environmental EngineeringLehigh UniversityBethlehemUSA
  2. 2.Department of Industrial and Systems EngineeringLehigh UniversityBethlehemUSA

Personalised recommendations