Modified Natural Excitation Technique for Stochastic Modal Identification
Publication: Journal of Structural Engineering
Volume 139, Issue 10
Abstract
This paper presents an improvement to the eigensystem realization algorithm (ERA) with natural excitation technique (NExT), which is called the ERA-NExT-AVG method. The method uses a coded average of row vectors in each Markov parameter for evaluating modal properties of a structure. The modification is important because, for the existing stochastic system identification methods, the state-space model, obtained from output sensor data, is usually overparameterized resulting in large systems. Solving such a problem can be computationally very intensive especially in the applications when using the computational capabilities of embedded sensor networks. As a way to improve the efficiency of the ERA-NExT method, the proposed method focuses on the number of components in a single Markov parameter, which can theoretically be minimized down to the number of structural modes. Applying the coded average column vectors as Markov parameters to the ERA, the computational cost of the algorithm is significantly reduced, whereas the accuracy of the estimates is maintained or improved. Numerical simulations are performed for a shear frame model subjected to Gaussian white noise ground excitation. The efficiency of the proposed method is evaluated by comparing the accuracy and computational cost of the estimated modal parameters using the proposed method, with several other stochastic modal identification methods including the ERA-observer Kalman filter identification, ERA-NExT, and autoregressive models. The performance of the method is then evaluated by applying it to ambient vibration data from the Golden Gate bridge, collected using a dense wireless sensor network, and its vertical and torsional modes are successfully and accurately identified.
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Acknowledgments
This work reflects the advice of Professor Gregory Fenves. Dr. Sukun Kim developed the software and closely collaborated in design of hardware and deployment of the network on Golden Gate bridge. The authors thank the staff and management of Golden Gate bridge, Highway and Transportation District, in particular Dennis Mulligan and Jerry Kao, for close cooperation in installation and maintenance of the WSN on GGB. Jorge Lee provided extraordinary help in the deployment, which made this project possible. This research was partially supported by National Science Foundation Grant No. CMMI-0926898 by Sensors and Sensing Systems program and a grant from the Commonwealth of Pennsylvania, Department of Community and Economic Development, through the Pennsylvania Infrastructure Technology Alliance. The authors thank Dr. Shih-Chi Liu from the National Science Foundation for his support and encouragement.
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© 2013 American Society of Civil Engineers.
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Received: Mar 31, 2011
Accepted: Dec 29, 2011
Published online: Jan 2, 2012
Published in print: Oct 1, 2013
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