Real-time safety risk assessment of floating offshore platforms based on on-site monitoring

Abstract

Real-time safety risk assessment of in-service marine engineering structures is one of the key components for the intelligent development of marine resources. Floating platforms typically have weak modes and time-varying characteristics, and hence, traditional structural damage identification and safety assessment methods cannot be directly applied to floating platforms. The objective of this study was to achieve the real-time safety risk assessment of in-service floating platforms. First, a structural damage identification method for floating platforms was proposed based on virtual loads, and the physical parameters of the platforms were obtained through inversion of measurable data. The identification result was verified based on the lengths and stiffnesses of polyester cables, which affected the dynamic response of the floating platform. The results showed that the proposed method effectively detected the parameter changes of the platform during long-term service. Then, the neural network was used to construct a load–stress surrogate model at key positions of the floating offshore platform. This model could calculate the stresses at key positions in real time, thereby evaluating the safety risks of the platform. Lastly, a real-time safety risk assessment method for floating offshore platforms was proposed, which provides a technical solution for ensuring the safety of offshore platforms.