Near-Real-Time Hybrid System Identification Framework for Civil Structures with Application to Burj Khalifa

Abstract

AbstractThis 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, ar...