Cointegration strategy for damage assessment of offshore platforms subject to wind and wave forces - Kuai, H.; Civera, M.; Coletta, G.; Chiaia, B.; Surace, C. (Ocean engineering, 2024)
In structural engineering, offshore structures are undoubtedly among the most exposed to the effects of harsh environmental conditions. The external conditions of these semi-immersed systems involve complex combinations of wave and wind loads. The operating conditions are also unique because oil production platforms are subjected to repeated loading and unloading cycles of the extracted material, which continuously alter their mass. These characteristics make the definition of a structural health monitoring (SHM) protocol highly challenging but necessary to avoid environmental disasters. In this regard, this study discusses an SHM method that can be applied to offshore structures under realistic wave and wind loads. This approach combines anomaly detection, frequency domain decomposition, and a cointegration strategy. Two machine learning regression algorithms were tested to define a cointegration relationship: the support vector machine and the relevance vector machine. The effectiveness of the overall method was evaluated on time-domain signals generated from a finite-element model of a fixed steel platform, on which the Davenport and JONSWAP spectra were used to simulate wind and wave forces. The results show that this damage detection strategy is effective in supervising the health conditions in the analyzed scenario. •A Structural Health Monitoring (SHM) strategy is presented, based on Frequency Domain Decomposition and Cointegration.•Innovatively, the method is output-only, allowing its unrestricted use on-site and under normal operating conditions.•It can be applied directly to the time series as recorded from the structure's dynamic response.•Instead of the classic assumption of white Gaussian noise as input, realistic wind and wave forces are considered.•Different operating conditions and damage scenarios are numerically simulated and investigated.