Abstract
Accurate structural damage identification calls for dense sensor networks, which are becoming more feasible as the price of electronic sensing systems reduces. To transmit and process data from all nodes of a dense network would be an onerous task which creates a BIG DATA problem; therefore scalable algorithms are needed so that decision on the current state of the structure can be made based on efficient data processing. In this paper, an iterative spatial compressive sensing scheme for damage existence identification and localization will be introduced. At each iteration, a subset of sensors is selected for data transmission and relevant information will be extracted at central station for damage existence identification/localization. This information will also provide useful guidance in future selection of sensing locations. The devised algorithm is applied to identify damage in a simulated gusset plate.
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
Lynch J, Loh K (2006) A summary review of wireless sensors and sensor networks for structural health monitoring. Shock Vib Dig 38(91): 91–128
Farrar CR, Park G, Allen DW, Todd MD (2006) Sensor network paradigms for structural health monitoring. Struct Control Health Monit 13(1):210–225
Pakzad S (2010) Development and deployment of large scale wireless sensor network on a long-span bridge. Smart Struct Syst 6(5–6):525–543
Cheng L, Pakzad SN (2009) Agility of wireless sensor networks for earthquake monitoring of bridges. In: 2009 sixth international conference on networked sensing systems (INSS), IEEE, pp 1–4
O’Connor SM, Lynch JP, Gilbert AC (2014) Compressed sensing embedded in an operational wireless sensor network to achieve energy efficiency in long-term monitoring applications. Smart Mater Struct 23(8):085014
Mascarenas D, Cattaneo A, Theiler J, Farrar C (2013) Compressed sensing techniques for detecting damage in structures. Struct Health Monit 12(4):325–338
Dorigo M, Birattari M (2010) Ant colony optimization. Encyclopedia of machine learning
Putha R, Quadrifoglio L, Zechman E (2012) Comparing ant colony optimization and genetic algorithm approaches for solving traffic signal coordination under oversaturation conditions. Comput-Aided Civ Infrastruct Eng 27(1):14–28
Yao R, Pakzad SN (2012) Autoregressive statistical pattern recognition algorithms for damage detection in civil structures. Mech Syst Signal Process 31:355–368
Yao R, Pakzad SN (2014) Time and frequency domain regression-based stiffness estimation and damage identification. Struct Control Health Monit 21(3):356–380
Dorvash S, Pakzad S, Labuz E (2014) Statistics based localized damage detection using vibration response. Smart Struct Syst (in press)
Shahidi SG, Pakzad SN (2014) Generalized response surface model updating using time domain data. J Struct Eng 140(8):A4014001
Dorvash S, Pakzad SN, Labuz E, Ricles J, Hodgson IC (2014) Localized damage detection algorithm and implementation on a large-scale steel beam-to-column moment connection. Earthq Spectra (in press)
Nigro MB, Pakzad SN, Dorvash S (2014) Localized structural damage detection: a change point analysis. Comput-Aided Civ Infrastruct Eng 29(6):416–432
Shahidi SG, Nigro MB, Pakzad SN, Pan Y (2014) Structural damage detection and localisation using multivariate regression models and two-sample control statistics. Struct Infrastruct Eng:1–17
Yao R, Pakzad SN (2014) Damage and noise sensitivity evaluation of autoregressive features extracted from structure vibration. Smart Mater Struct 23(2):025007
Yao R, Pakzad SN (2014) Multi-sensor aggregation algorithms for structural damage diagnosis based on substructure concept. J Eng Mech (in Press)
Conradsen K, Nielsen AA, Schou J, Skriver H (2003) A test statistic in the complex Wishart distribution and its application to change detection in polarimetric SAR data. IEEE Trans Geosci Remote Sens 41(1):4–19
Gupta A, Nagar D (2004) Distribution of the determinant of the sample correlation matrix from a mixture normal model. Random Oper Stochastic Equ 12(2):193–199
ABAQUS V (2013) “6.13,” Dassault systemes. Pawtucket
Acknowledgement
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
© 2015 The Society for Experimental Mechanics, Inc.
About this paper
Cite this paper
Yao, R., Pakzad, S.N., Venkitasubramaniam, P., Hudson, J.M. (2015). Iterative Spatial Compressive Sensing Strategy for Structural Damage Diagnosis as a BIG DATA Problem. In: Caicedo, J., Pakzad, S. (eds) Dynamics of Civil Structures, Volume 2. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-319-15248-6_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-15248-6_19
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-15247-9
Online ISBN: 978-3-319-15248-6
eBook Packages: EngineeringEngineering (R0)