Structural identification of a super-tall tower by GPS and accelerometer data fusion using a multi-rate Kalman filter

Abstract

This work outlines a procedure for the effective fusion of different types of sensor data, namely acceleration and displacement records, aiming at the successful simulation and monitoring of the behavior of large structures under dynamic loading. In this study, a method of combining displacement and acceleration data for the purpose of structural health monitoring is demonstrated and the example of a new innovative high-rise structure in China, featuring a fully functional on-line monitoring system is conceived as a test-case. The overall goal is to successfully fuse the heterogeneous measurements, obtained on-line while sampled at different frequencies. The tools employed involve a multi-rate Kalman Filter scheme, as presented in (Smyth & Wu 2007) and an artificial white noise (WN) observation technique necessary for eradicating the displacement drift issue which stems from the integration process. As shown in the application section, this methodology is able to increase the accuracy of the estimated quantities against the use of conventional numerical processing methods.