Damage detection in jacket-type offshore platforms via generalized flexibility matrix and optimal genetic algorithm (GFM-OGA)

Abstract

Jacket-type offshore platforms are crucial infrastructure assets that can be damaged, disrupting operations and causing significant economic losses. To manage these risks, Structural Health Monitoring (SHM) is crucial in extending the lifespan of these structures. Recent years have seen the development of innovative vibration-based methods for SHM, which are based on the principle that changes in the dynamic model specifications of a structure indicate damage. The aim of this paper is to present an effective process for detecting structural damage through vibrational analysis and determining its location and severity. The proposed method uses vibrational characteristics of the structure, such as mass and stiffness, to deduce changes in its physical properties through monitoring with sensors. The methodology is formulated as an optimization problem where the target function is established based on the modal information of the structure in both intact and damaged states, and optimized subject to specific constraints. The solution to the optimization problem provides information on the location and severity of the damage. The methodology is demonstrated through a case study on a scaled model of a jacket-type offshore platform, showing its effectiveness in accurately identifying damage location and severity at varying levels of damage.