High resolution operational modal analysis on a five-story smart building under wind and human induced excitation

Abstract

The Goodwin Hall Smart Infrastructure facility at Virginia Tech is a five-story “smart building” with an integrated network of 225 wired accelerometers. This study utilizes a subset of 117 sensors to perform Operational Modal Analysis (OMA) of the structure under wind excitation and establish a high-resolution benchmark modal characterization. Frequency Spatial Domain Decomposition and Stochastic Subspace Identification results are compared to validate the extracted modal parameters. Twelve structural modes were identified, including five high frequency local modes. These local modes are crucial features for structures with complex geometries and can generally be identified only with high density instrumentation. Through a parametric analysis and the use of standard deviation estimates, we determine that 50–60 min time series were optimal for high confidence on frequency and damping estimates. Furthermore, we employ standard deviation estimates to improve existing OMA automation methods. This enables continuous modal parameter extraction over a four-day period to understand the characteristics of the two main forms of ambient excitation: wind and human-induced. Although similar continuous analyses have been conducted on bridges, few of this kind exist for buildings. In general, we observe that modal participation of the three fundamental modes is closely tied to wind and human activity and that the confidence in frequency and damping estimates of these modes improves as the excitation increases. Slight decreases in natural frequency with increasing participation occur for several modes, agreeing with behavior observed in bridge monitoring studies. Finally, wind is seen to excite primarily in one direction, whereas humans induce even excitation in all directions.