Comparative Analysis of Robust Kalman Filtering Techniques for Structural Health Monitoring Purposes

Abstract eng:
To avoid damage in structures due to aging, deterioration or extreme events, and consequently save money and lives, damage should be detected at an early stage. Structural Health Monitoring (SHM) is the process of implementing a damage detection strategy for various branches of engineering. This process used to be based on periodic visual inspections of the structures; such process was both impractical and time consuming. However, with the recent development of sensors and other monitoring devices, SHM became more accurate and abundant. Sequential data assimilation has been widely used for SHM and system identification problems. Many extensions of the Kalman Filter were developed as adaptations to important classes of these problems. This study aims at presenting a comparative analysis based on the accuracy and efficacy of the different Kalman Filter based data assimilation techniques when used for the purpose of SHM of complex nonlinear systems. The sequential data assimilation methodologies are combined with a non-parametric modeling technique to tackle structural health monitoring of a four-story shear building. The structure is subject to a base motion specified by a time series consistent with the El-Centro earthquake and undergoes a preset damage in the first floor. The purpose of the problem is localizing the damage in both space and time, and tracking the state of the system throughout and subsequent to the damage time. The data assimilation techniques are compared taking into consideration accuracy, computational expediency, and implementation practicality.

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