Exploring the Efficiency of BIGDATA Analyses in SHM

In SHM, BIGDATA is currently perceived as the result of special applications such as long-term monitoring, dense sensor arrays, or high sampling rates. Along the development of novel sensing techniques as well as advances in sensing devices and data acquisition technology, it is expected that BIGDATA will become more easily obtained. In a previous study, the evaluation of selected SHM procedures exemplified computational challenges for BIGDATA. It was concluded that processing BIGDATA in SHM can be prohibitive, especially when incorporating more sensors into the analysis. This paper focuses on the relationship between sensor network size and information extracted by an SHM procedure, e.g., system identification or damage detection. An application is presented to study the accuracy and effectiveness of these procedures as more sensors are included in the datasets. doi: 10.12783/SHM2015/369

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