Reconstruction of Unmeasured Strain Responses in Bottom-fixed Offshore Structures by Multimetric Sensor Data Fusion

Abstract

Virtual sensing technologies have the potential to alleviate the difficulties in installing and maintaining the sensors located underwater by reconstructing unmeasured structural responses at desired locations with other measured responses. This approach is particularly advantageous when (1) some sensors are malfunctioning and (2) sensor installation at critical members is difficult. Despite the usefulness of virtual sensing, its performance and applicability for structural health monitoring of offshore structures has not been fully studied to date. This paper investigates the use of virtual sensing for the structural health monitoring of offshore structures, more specifically, for reconstruction of unmeasured strain responses in a bottom-fixed offshore structures. The Kalman filter technique is employed for the data fusion of different types of measured responses to produce unmeasured strain responses at structural members of interest. While the previous studies on the virtual sensing technique have handled the zero-mean random process, this study discusses how the non-zero-mean random force (herein, nonstationary tidal current loading) in the response estimation is appropriately handled. Numerical simulation study is conducted to verify the performance of the virtual sensing strategy using a bottom-fixed offshore structure model, showing that the unmeasured responses can be reasonably recovered from the measured responses.