Static Testing of Civil Structures: Data Quality and Post- Processing

Structural identification programs executed without appropriate methods for data interpretation usually fail to provide meaningful information to support engineering decisions. Identifying and implementing appropriate data interpretation methods represent fundamental scientific challenges in the field of structural identification. Given the many recent developments in and diversity of available sensor technologies, and the impressive gains in computing capacity, data interpretation challenges often remain as the final “bottleneck” restricting the potential of structural identification through static and quasi-static measurements. High quality data interpretation leads to a significantly increased understanding of the real structural behavior. Analytical quality and measurement data quality both need to be evaluated to establish the degree of correlation between predictive structural analysis-based responses and the collected measurements; however, an exact correlation should rarely be the objective. This paper will discuss the issues related to quality of measurements and interpretation of this data using structural analysis and structural identification algorithms. It should be noted that data interpretation methods must always respect the physical reality associated with structures, and no single approach is best for all cases.

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