An adaptive modeling approach to structural health monitoring of multistory buildings

Abstract

Abstract Presented is an adaptive model-based approach for structural health monitoring (SHM) of multistory buildings. Fault detection, isolation and estimation (FDIE) are accomplished through the integration of reduced-order physics models with a new observer-based parameter identification algorithm that directly estimates changes in structural stiffness and damping. In the proposed method, each building floor is connected to its adjoining floors using springs and dampers (i.e., structure columns) to capture the planar motion of the system. The novelty in this method is that the structure features of stiffness and damping are directly estimated from the observer model states. To demonstrate the proposed method, a finite element analysis of a scaled digital building is used to generate dynamic structural data. The simulated data will be corrupted to emulate sensor noise. It will be shown that for this numerical study a 15% stiffness change in one of the nine columns between the floors that produces 1.67% decrease in overall stiffness is detected. It will be also shown that the proposed approach is able to detect, isolate and estimate faults of different magnitudes at single and multiple locations.