Abstract
Large quantities of data which contain detailed condition information over an extended period of time should be utilized to prioritize infrastructure repairs. As the temporal and spatial resolution of monitoring data drastically increase by advances in sensing technology, structural health monitoring applications reach the thresholds of big data. Deep neural networks are ideally suited to use large representative training datasets to learn complex damage features. In the previous study of authors, a real-time deep learning platform was developed to solve damage detection and localization challenge. The network was trained by using simulated structural connection mimicking the real test object with a variety of loading cases, damage scenarios, and measurement noise levels for successful and robust diagnosis of damage. In this study, the proposed damage diagnosis platform is validated by using temporally and spatially dense data collected by Digital Image Correlation (DIC) from the specimen. Laboratory testing of the specimen with induced damage condition is performed to evaluate the performance and efficiency of damage detection and localization approach.
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References
Sohn, H., Farrar, C.R.: Damage diagnosis using time series analysis of vibration signals. Smart Mater. Struct. 10(3),446 (2001)
Fang, X., Luo, H., Tang, J.: Structural damage detection using neural network with learning rate improvement. Commun. Strateg. 83(25), 2150–2161 (2005)
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)
Nair, K.K., Kiremidjian, A.S., Law, K.H.: Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure. J. Sound Vib. 291(1), 349–368 (2006)
Fujimaki, R., Yairi, T., Machida, K.: An approach to spacecraft anomaly detection problem using kernel feature space. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 401–410. ACM, New York (2005)
Shi, A., Yu, X.-H.: Structural damage detection using artificial neural networks and wavelet transform. In: 2012 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings, pp. 7–11. IEEE, Tianjin (2012)
Valeti, B., Pakzad, S.: Automated detection of corrosion damage in power transmission lattice towers using image processing. In: Structures Congress, pp. 474–482. American Society of Civil Engineers, Reston (2017)
Shahidi, S.G., Gulgec, N.S., Pakzad, S.N.: Compressive sensing strategies for multiple damage detection and localization. In: Dynamics of Civil Structures, vol. 2, pp. 17–22. Springer, Cham (2016)
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)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
LeCun, Y., Bengio, Y.: Convolutional Networks for Images, Speech, and Time-Series. MIT Press, Cambridge (1995)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385 (2015)
Gulgec, N.S., Takac, M., Pakzad, S.N.: Convolutional neural network approach for robust structural damage detection and localization. J. Comput. Civ. Eng. 30(3), 04015038 (2018)
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, Berlin (2017)
Pan, B., Qian, K., Xie, H., Asundi, A.: Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review. Meas. Sci. Technol. 20(6), 062001 (2009)
Yoneyama, S., Kitagawa, A., Iwata, S., Tani, K., Kikuta, H.: Bridge deflection measurement using digital image correlation. Exp. Tech. 31(1), 34–40 (2007)
GOM. Aramis User Manual–Software (2013)
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. (2020). Experimental Study on Digital Image Correlation for Deep Learning-Based Damage Diagnostic. In: Pakzad, S. (eds) Dynamics of Civil Structures, Volume 2. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-030-12115-0_28
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DOI: https://doi.org/10.1007/978-3-030-12115-0_28
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