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With recent advances in low-cost wireless sensing and data acquisition technology, it has become feasible to instrument a large structure with a dense array of wireless sensors. Furthermore, analog-to-digital conversion and data processing capabilities of current wireless sensor prototypes offer the ability to efficiently distribute data processing tasks across a large network of wireless sensing nodes. For decades, the structural engineering community has been adapting input-output modal identification techniques for use in large-scale civil structures. However, unlike in mechanical or aerospace engineering, it is often difficult to excite a large civil structure in a controlled manner. Thus, additional emphasis has been placed on developing a number of output-only modal identification methods for use in structural engineering applications. In this paper, three of these output-only methods (peak picking, random decrement, and frequency domain decomposition) are modified for implementation in a distributed array of processors embedded within a network of wireless sensor prototypes. The software architecture proposed emphasizes parallel data processing and minimal communication so as to ensure scalability and power efficiency. Using the balcony of a historic theater in metropolitan Detroit as a testbed, this network of wireless sensors is allowed to collect and process acceleration response data during a set of vibration tests. The embedded algorithms proposed in this study are used to autonomously determine the balcony’s modal properties with network-derived results found to be comparable to those derived from traditional offline techniques.
Zimmerman et al. (Tue,) studied this question.