Observer Kalman Filter Identification for Output-Only Systems Using Interactive Structural Modal Identification Toolsuite

Several modal identification techniques have been developed in the past few decades, and their use is rapidly expanding due to new focus on the instrumentation of major structures. This paper focuses on the expansion of the eigenvalue realization algorithm (ERA)-observer Kalman filter identification (OKID) to identify modal parameters of output-only systems (OO) by splitting the state-space model into deterministic and stochastic subsystems (ERA-OKID-OO). The performance is then compared with other output-only identification methods in terms of the level of accuracy and efficiency. A newly developed software package [Structural Modal Identification Toolsuite (SMIT)] is used to provide a uniform and convenient way of utilizing several system identification (SID) methods, including variations of ERA, auto-regressive with exogenous terms (ARX) models, system realization using information matrix (SRIM), and numerical algorithms for subspace state space system identification (N4SID). The main purpose of SMIT is to provide a convenient platform for an expanding list of SID methods to estimate the modal parameters based on measured data and produce reports and compare them. With a brief explanation of the theoretical background of implemented algorithms, the paper continues with the preprocessing procedure to define the geometric information, sensor data, and setup for the applied method. It then explains the postprocessing procedure to construct stabilization diagrams, obtain modal parameters from the overparameterized estimation, and visualize and save the results. The applications for a numerically simulated simply supported beam as well as sensor data from the Lehigh River Bridge (LRB) on Highway 33 and Golden Gate Bridge (GGB) are used to demonstrate the validity of SMIT and investigate the performance of ERA-OKID-OO.