Structural Damage Detection of Offshore Structures Using Kalman Filtering

Offshore platforms can be subjected to harsh environmental conditions increasing their vulnerability to failures. Hence, the deployment of a Structural Health Monitoring (SHM) system is highly relevant to safeguard their structural integrity. Typically, an SHM system includes a Damage Detection (DD) scheme, which aims to identify structural damages by using information recorded by the sensing system. Plenty of DD schemes have been proposed for various types of structures. However, several peculiarities of the offshore platforms, being associated with the near impossibility to operate sensors underwater, can challenge the performance of conventional DD schemes. In this context, the current study emphasizes the development of a new Kalman filter-based framework that is anticipated to estimate the location and severity of the damage. Especially, the DD problem is approached as an input-state estimation problem for nonlinear systems. The main objective of this method is to identify and quantify the damages induced by wave loads in terms of, for example, plastic deformations. Advanced modeling techniques are required for this task. The proposed DD scheme is tested on a 2D steel jacket structure subjected to wave loading. The outcome of the specific DD scheme is expected to provide the damage profile of the structure after various hazardous events.