Abstract
Structural health monitoring (SHM) techniques have been studied over the past few decades to detect the deficiencies affecting the performance of the structures. Detecting and localizing these deficiencies require long-term data collection from dense sensor networks which creates a challenging task for data transmission and processing. To address this problem, a comparative study of two image-based compressive sensing approaches for multiple damage localization is presented in this paper. The first methodology consists of compressive sampling from the sensor network in global and local formats. Then through statistical change point analysis on the sampled datasets, and Bayesian probability estimation, the study estimates the location of damage. The second algorithm implements compressive sensing to the subset of samples obtained from the sensor network space divided into blocks. The damage existence and location are determined by statistical hypothesis testing of Discrete Cosine Transformation (DCT) coefficients avoiding the original signal recovery. In order to evaluate the performance of both algorithms, multiple damage scenarios are simulated in steel gusset plate model. The comparison results are presented in terms of compression ratios and successful detection rates.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Matarazzo, T.J., Shahidi, S.G., Pakzad, S.N.: Exploring the efficiency of BIGDATA analyses in SHM. In: Proceedings of the 10th International Workshop on Structural Health Monitoring, Stanford, CA, vol. 2, pp. 2981–2989 (2015)
Matarazzo, T.J., Shahidi, G., Chang, M., Pakzad, S.N.: Are today’s SHM procedures suitable for tomorrow’s data? In: Proceedings of the Society of Experimental Mechanics IMAC XXXIII, Orlando, FL (2015)
Candès, E.J., Romberg, J., Tao, T.: Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inform. Theory 52(2), 489–509 (2006)
Donoho, D.L.: Compressed sensing. IEEE Trans. Inform. Theory 52(4), 1289–1306 (2006)
Candès, E.J., Wakin, M.B.: An introduction to compressive sampling. IEEE Signal Process. Mag. 25(2), 21–30 (2008)
Bao, Y., Beck, J.L., Li, H.: Compressive sampling for accelerometer signals in structural health monitoring. Struct. Health Monit. 10(3), 235–246 (2010)
Haile, M., Ghoshal, A.: Application of compressed sensing in full-field structural health monitoring. In: Proceedings of the SPIE 8346, Smart Sensor Phenomena, Technology, Networks and Systems Integration, San Diego, CA (2012)
Zhou, S., Bao, Y., Li, H.: Structural damage identification based on substructure sensitivity and l1 sparse regularization. In: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, pp. 86923N–86923N. International Society for Optics and Photonics (2013)
Cortial, J., Farhat, C., Guibas, L.J., Rajashekhar, M.: Compressed sensing and time-parallel reduced-order modeling for structural health monitoring using a DDDAS. In: Computational Science–ICCS 2007, pp. 1171–1179. Springer, Heidelberg (2007)
Kirolos, S., Laska, J., Wakin, M., Duarte, M., Baron, D., Ragheb, T., Massoud, Y., Baraniuk, R.: Analog-to-information conversion via random demodulation. In: Design, Applications, Integration and Software, 2006 IEEE Dallas/CAS Workshop on, pp. 71–74. IEEE (2006)
Mascareñas, D., Cattaneo, A., Theiler, J., Farrar, C.: Compressed sensing techniques for detecting damage in structures. Struct. Health Monit. 12(4), 325–338 (2013)
O’Connor, S.M., Lynch, J.P., Gilbert, A.C.: Compressed sensing embedded in an operational wireless sensor network to achieve energy efficiency in long-term monitoring applications. Smart Mater. Struct. 23(8), 085014 (2014)
Davenport, M.A., Duarte, M.F., Wakin, M.B., Laska, J.N., Takhar, D., Kelly, K.F., Baraniuk, R.G.: The smashed filter for compressive classification and target recognition. In: Electronic Imaging 2007, pp. 64980H–64980H. International Society for Optics and Photonics (2007)
Yao, R., Pakzad, S.N., Venkitasubramaniam, P., Hudson, J.M.: Iterative spatial compressive sensing strategy for structural damage diagnosis as a BIG DATA problem. In: Proceedings of the Society of Experimental Mechanics IMAC XXXIII, vol. 2, pp. 185–190. Springer International Publishing (2015)
Shahidi, S.G., Pakzad, S.N.: A compressed sensing approach in structural damage identification. In: Proceedings of the 10th International Workshop on Structural Health Monitoring, Stanford, CA, vol. 2, pp. 2245–2252 (2015)
Gulgec, N.S., Shahidi, S.G., Pakzad S.N.: A comparative study of compressive sensing approaches for structural damage diagnosis. In: Proceedings of GeoStructures Congress, Phoenix, AZ (2016)
Nigro, M.B., Pakzad, S.N., Dorvash, S.: Localized structural damage detection: a change point analysis, (Blackwell Publishing). Comput. Aided Civ. Infrastruct. Eng. 29(6), 416–432 (2014). doi:10.1111/mice.12059
Shahidi, S.G., Nigro, M.B., Pakzad, S.N., Pan, Y.: Structural damage detection and localization using multivariate regression models and two-sample control statistics. Struct. Infrastruct. Eng. 11(10), 1277–1293 (2015)
Dorvash, S., Pakzad, S.N., Labuz, E.L.: Statistics based localized damage detection using vibration response. Smart Struct. Syst., Int. J. 14(2), 85–104 (2014). doi:10.12989/sss.2014.14.2.085,2014
Dorvash, S., Pakzad, S.N., Labuz, E.L., Ricles, J.M., Hodgson, I.C.: Localized damage detection algorithm and implementation on a large-scale steel beam-to-column moment connection. Earthq. Spectra 31(3), 1543–1566 (2014). doi:10.1193/031613EQS069M
Shahidi, G., Yao, R., Pakzad, S.N., Chamberlain, M., Nigro, M.B.: Data-driven structural damage identification using DIT. In: Proceedings of the Society of Experimental Mechanics IMAC XXXIII, Orlando, FL (2015)
Lloyd, S.P.: Least squares quantization in PCM. IEEE Trans. Inform. Theory 28(2), 129–137 (1982)
Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge (2005)
Tropp, J.A., Gilbert, A.C.: Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inform. Theory 53(12), 4655 (2007)
Lam, E.Y., Goodman, J.W.: A mathematical analysis of the DCT coefficient distributions for images. IEEE Trans. Image Process. 9(10), 1661–1666 (2000)
ABAQUS. Version “6.13,” Dassault systemes. Pawtucket, Rhode Island (2013)
Ibrahim, F.I.S.: Load Rating Evaluation of Gusset Plates in Truss Bridges, FHWA Design Guidance No. 1. (2008)
Acknowledgement
The authors would like to thank Jamie Hudson who created the FE model used in the simulation study. 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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 The Society for Experimental Mechanics, Inc.
About this paper
Cite this paper
Shahidi, S.G., Gulgec, N.S., Pakzad, S.N. (2016). Compressive Sensing Strategies for Multiple Damage Detection and Localization. In: Pakzad, S., Juan, C. (eds) Dynamics of Civil Structures, Volume 2. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-319-29751-4_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-29751-4_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-29750-7
Online ISBN: 978-3-319-29751-4
eBook Packages: EngineeringEngineering (R0)