| description abstract | This study proposes a near-real-time hybrid framework for system identification (SI) of structures using data from structural health monitoring systems. To account for both stationary/weakly nonstationary response under normal conditions (e.g., extratropical winds and/or ambient excitations) and transient/highly nonstationary response under transient events (e.g., earthquakes, windstorms, or time-varying traffic loadings), a hybrid framework is introduced by integrating a new nonstationary SI scheme based on wavelets in tandem with transformed singular value decomposition, and a robust stationary SI scheme called covariance-driven stochastic subspace identification. Extensive numerical simulations as well as analysis of full-scale data are conducted to evaluate the efficacy of the scheme. To facilitate expeditious and convenient utilization of this framework in a practical application, a web-enabled approach and its workflow concerning measurements of the world’s tallest building, Burj Khalifa, are presented. This web approach facilitates automated hybrid SI in near real time as an Internet of Things service, which remotely provides end users (e.g., building owners, managers, engineers, and other stakeholders) with timely information on structural performance and ultimately supports the user’s need in decision making regarding structural operation. It is demonstrated that natural frequencies and damping ratios are successfully identified from the streaming data in near real time under both winds and earthquakes. The identified system properties are very useful for tracking the structure’s health condition in its lifecycle. The resulting probabilistic characterization of the system properties can be used to enhance performance-based structural design and retrofitting. | |
