Optimal sensor placement for parameter estimation of bridges

Edward Eskew; Shinae Jang

doi:10.1117/12.2261244

5 April 2017 Optimal sensor placement for parameter estimation of bridges

Edward Eskew, Shinae Jang

Proceedings Volume 10170, Health Monitoring of Structural and Biological Systems 2017; 101702F (2017) https://doi.org/10.1117/12.2261244
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2017, Portland, Oregon, United States

Abstract

Gathering measurements from a structure can be extremely valuable for tasks such as verifying a numerical model, or structural health monitoring (SHM) to identify changes in the natural frequencies and mode shapes which can be attributed to changes in the system. In most monitoring applications, the number of potential degrees-of-freedom (DOF) for monitoring greatly outnumbers the available sensors. Optimal sensor placement (OSP) is a field of research into different methods for locating the available sensors to gather the optimal measurements. Three common methods of OSP are the effective independence (EI), effective independence driving point residue (EI-DPR), and modal kinetic energy (MKE) methods. However, comparisons of the different OSP methods for SHM applications are limited. In this paper, a comparison of the performance of the three described OSP methods for parameter estimation is performed. Parameter estimation is implemented using modified parameter localization with direct model updating, and added mass quantification utilizing a genetic algorithm (GA). The quantification of the mass addition, using simulated measurements from the sensor networks developed by each OSP method, is compared to provide an evaluation of each OSP methods capability for parameter estimation applications.

Conference Presentation

Citation Download Citation

Edward Eskew and Shinae Jang "Optimal sensor placement for parameter estimation of bridges", Proc. SPIE 10170, Health Monitoring of Structural and Biological Systems 2017, 101702F (5 April 2017); https://doi.org/10.1117/12.2261244

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