Abstract
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.
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References
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)
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)
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)
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)
Giraldo, D.F., Dyke, S.J., Caicedo, J.M.: Damage detection accommodating varying environmental conditions. Struct. Health Monit. 5(2), 155–172 (2006)
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)
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)
Yoneyama, S., Kitagawa, A., Iwata, S., Tani, K., Kikuta, H.: Bridge deflection measurement using digital image correlation. Exp. Tech. 31(1), 34–40 (2007)
Hild, F., Roux, S.: Digital Image Correlation. Wiley-VCH, Weinheim (2012)
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)
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)
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:1409.3215
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)
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)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)
Elman, J.L.: Finding structure in time. Cogn. Sci. 14(2), 179–211 (1990)
Lipton, Z.C., Berkowitz, J., Elkan, C.: A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019 (2015)
Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157–166 (1994)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
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)
Gers, F.A., Schmidhuber, J., Cummins, F.: Learning to forget: continual prediction with LSTM. Neural Comput. 12(10), 2451–2471 (2000)
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)
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)
PEER NGA: Pacific Engineering Research Database (2008). https://ngawest2.berkeley.edu/
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)
Carassale, L., Solari, G.: Monte carlo simulation of wind velocity fields on complex structures. J. Wind Eng. Ind. Aerodyn. 94(5), 323–339 (2006)
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)
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)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Werbos, P.J.: Backpropagation through time: what it does and how to do it. Proc. IEEE 78(10), 1550–1560 (1990)
Acknowledgements
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.
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Gulgec, N.S., Takáč, M., Pakzad, S.N. (2019). Innovative Sensing by Using Deep Learning Framework. In: Pakzad, S. (eds) Dynamics of Civil Structures, Volume 2. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-319-74421-6_39
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DOI: https://doi.org/10.1007/978-3-319-74421-6_39
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