Abstract
Large SHM datasets often result from special applications such as long-term monitoring, dense sensor arrays, or high sampling rates. Through the development of novel sensing techniques as well as advances in sensing devices and data acquisition technology, it is expected that such large volumes of measurement data become commonplace. In anticipation of datasets magnitudes larger than today’s, it is important to evaluate current SHM processing methods at BIGDATA standards and identify potential limitations within computational procedures. This paper will focus on the processing of large SHM datasets and the computational sensitivity of common SHM procedures. Processing concerns encompass efficiency and scalability of SHM software, particularly the computational sensitivity of common system identification and damage detection algorithms with respect to a large amount of sensors and samples.
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Acknowledgement
Research funding is partially provided by the National Science Foundation through Grant No. CMMI-1351537 by Hazard Mitigation and Structural Engineering program, and by a grant from the Commonwealth of Pennsylvania, Department of Community and Economic Development, through the Pennsylvania Infrastructure Technology Alliance (PITA).
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Matarazzo, T.J., Shahidi, S.G., Chang, M., Pakzad, S.N. (2015). Are Today’s SHM Procedures Suitable for Tomorrow’s BIGDATA?. In: Niezrecki, C. (eds) Structural Health Monitoring and Damage Detection, Volume 7. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-319-15230-1_7
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DOI: https://doi.org/10.1007/978-3-319-15230-1_7
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