Displacement Estimation Using Multimetric Data Fusion

Abstract

While displacement is valuable information for the structural behavior, measuring displacements from large civil structures is often challenging and costly. To overcome difficulties found in direct measurements such as using linear variable differential transformer and LASER-based methods, indirect displacement estimation approaches are alternatively developed. Such indirect approaches in general rely on acceleration or strain that is relatively cost effective and convenient to measure. However, these measurements have own characteristics that limit wider application of the indirect estimation. For example, as the double integration of acceleration results in the low-frequency drift in the estimated displacement, high-pass filters are often used to suppress the drift, assuming displacements are close to a zero mean process; strain is difficult to use for high-frequency modes. These types of limitations can be resolved by the fusion of different types of measurements. This study develops an indirect displacement estimation method based on the multimetric data (i.e., acceleration and strain) that can estimate nonzero mean, dynamic displacements. The proposed approach is numerically validated, showing better estimation than the single measurement-based methods. Furthermore, the performance of the proposed approach is verified using dynamic response data measured from the Sorok Bridge, a cable-stayed bridge in Korea.