Leveraging hybrid simulation for vibration-based damage detection studies

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Abstract

Approaches for structural health monitoring of civil infrastructure using vibration-based damage detection methods have progressed significantly over the past decade as a result of extensive experimentation on both full-scale and laboratory-scale structures. However, the field instrumentation of in-service structures involving well-characterized damage scenarios is a complex, costly, and high-risk investment. In contrast, the use of numerical simulations to generate synthetic data for structural health monitoring research is restricted by challenges associated with modeling experimental uncertainties associated with measurement devices and ambient disturbances. In both cases, the faithful representation of meaningful damage progression is often a technical limitation. Recently, hybrid simulation has been explored as a novel alternative for experimental structural health monitoring research that alleviates the expense and logistics associated with full structural testing, yet retains the empirical nature of experimentation by combining an experimental substructure with an analytical model. In this paper, the use of pseudo-dynamic hybrid simulation for exploration of vibration-based structural health monitoring techniques is demonstrated using a truss structure where a single member and bolted gusset plate connection are incorporated in the experimental model. Hybrid simulations are performed in the baseline state and following introduction of localized damage developed at a limit state of the bolted connection. The results correlate strongly with predictions obtained from a fully analytical model and support the further exploration of hybrid simulation as a tool for vibration-based structural health monitoring research.

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Kernicky, T. P., Tedeschi, M., & Whelan, M. J. (2016). Leveraging hybrid simulation for vibration-based damage detection studies. In Conference Proceedings of the Society for Experimental Mechanics Series (Vol. 8, pp. 333–341). Springer New York LLC. https://doi.org/10.1007/978-3-319-30084-9_31

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