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.

Three-dimensional mathematical model was developed for a rectangular TE _10n microwave heating cavity system, working at 2.45 GHz. Energy/heat, momentum equations were solved together with Maxwell's electromagnetic field equations using COMSOL MULTIPHYSICS® simulation environment. The dielectric properties, ε' and ε'', of NaY zeolite (Si/Al = 2.5) were evaluated as a function of temperature. Considering these values, the microwave heating of a porous fixed-bed made of dry NaY zeolite was simulated. Electric field distribution, axial and radial temperature profiles and temperature evolution with time were obtained. The zeolite fixed bed was heated up to 180 °C in 5 min, with 30 W power. The fixed-bed temperature evolution under non-steady state conditions showed the same trend as the one observed experimentally with only an average deviation of 10.3%. The model was used to predict microwave heating of other materials improving energy efficiency of the microwave cavity. Furthermore, the developed model was able to predict thermal runaway for zeolites.