There is a large risk of damage, triggered by harsh ocean environments, associated with offshore structures, so structural health monitoring plays an important role in preventing the occurrence of critical and global structural failure from such damage. However, obstacles, such as applicability in the field and increasing calculation costs with increasing structural complexity, remain for real-time structure monitoring offshore. Therefore, this study proposes the comparison of cosine similarity with sensor data to overcome such challenges. As the comparison target, this method uses the rate of changes of natural frequencies before and after the occurrence of various damage scenarios, including not only single but multiple damages, which are organized by the experiment technique design. The comparison method alerts to the occurrence of damage using a normalized warning index, which enables workers to manage the risk of damage. By comparison, moreover, the case most similar with the current status is directly figured out without any additional analysis between monitoring and damage identification, which renders the damage identification process simpler. Plus, the averaged rate of errors in detection is suggested to evaluate the damage level more precisely, if needed. Therefore, this method contributes to the application of real-time structural health monitoring for offshore structures by providing an approach to improve the usability of the proposed technique.

This research article focuses on developing a baseline digital twin model for a wave power generator structure located in Yongsu-ri, Jeju-do, South Korea. First, this study performs a cause analysis on the discrepancy of the dynamic properties from the real structure and an existing simulation model and finds the necessity of modeling the non-structural masses and the environmental factors. The large amounts of the ballast are modeled in the finite element model to enhance the accuracy of the digital twin. Considering the influence of environmental factors such as tide level and wave direction, the added mass effect of structural members, one of the hydrodynamic effects, depending on the change of the ocean environments is calculated based on the rule of Det Norske Veritas and applied. The results indicate that non-structural mass components significantly impact the dynamic characteristics of the structure. Additionally, environmental factors have a greater effect on the dynamic behavior of the box-type structure compared to lightweight offshore structures.

A helideck is an essential structure in an offshore platform, and it is crucial to maintain its structural integrity and detect the occurrence of damage early. Because helidecks usually consist of complex lattice truss members, precise measurements are required for structural health monitoring based on accurate modal parameters. However, available sensors and data acquisition are limited. Therefore, we propose a two-step damage detection process using an artificial neural network. Based on the mode shape database collected from 137,400 damage scenarios by finite element analysis, the neural network in the first step was trained to estimate the mode shapes of the entire helideck model using the selected mode shape data obtained from the limited measuring points. Then, the neural network in the second step is consecutively trained to detect the location and amount of structural damage to individual parts. As a result, it is shown that the proposed procedure provides the damage detection capability with only a quarter of the entire mode shape data, while the estimation accuracy is sufficiently high compared to the single network directly trained using all mode shape data. It was also found that, compared to the network directly trained from the same data, the proposed technique tends to detect minor damages more accurately.

A helideck is an essential structure in offshore platforms for the transportation of personnel and goods between the land and offshore sites; therefore, the overall processes from the design to the installation should be conducted carefully to ensure the safety of the platform. This article discusses an optimal structural design of a cantilever-type offshore helideck based on a genetic algorithm (GA) considering its manufacturability and buckling safety. Because helidecks are not mass-produced structures, they are assembled from the ready-made products of structural steel. Therefore, the design variable sets in the GA optimization were efficiently limited to the standard products in the American Institute of Steel Construction (AISC) code using the attainable design set concept. In addition, owing to the nature of the offshore industry, offshore structures are designed conservatively, complying with the classification regulations. Thus, both the maximum allowable stress and unity values were precisely checked during the optimization. Furthermore, both linear and nonlinear buckling analyses were performed to check the dynamic stability of the optimal design.

The key to coping with global warming is reconstructing energy governance from carbon-based to sustainable resources. Offshore energy sources, such as offshore wind turbines, are promising alternatives. However, the abnormal climate is a potential threat to the safety of offshore structures because construction guidelines cannot embrace climate outliers. A cosine similarity-based maintenance strategy may be a possible solution for managing and mitigating these risks. However, a study reporting its application to an actual field structure has not yet been reported. Thus, as an initial study, this study investigated whether the technique is applicable or whether it has limitations in the real field using an actual example, the Gageocho Ocean Research Station. Consequently, it was found that damage can only be detected correctly if the damage states are very similar to the comparison target database. Therefore, the high accuracy of natural frequencies, including environmental effects, should be ensured. Specifically, damage scenarios must be carefully designed, and an alternative is to devise more efficient techniques that can compensate for the present procedure.

Structural integrity assessment of floating wind turbine (FOWT) systems is performed based on fully coupled load analysis in time domain and structural analysis that consider the complex interactions among wind, wave, and currents. However, there is a limit to repeating load and structural analyses several times for numerous DLCs required in actual projects. In this study, to determine the reasonable analysis time for structural integrity evaluation, actual time series for 1 hour and 3 hours were calculated based on FOWT load and structural analysis. Next, each time series was stochastically fitted to Weibull distribution function. The most probable maximum (MPM) von-Mises stresses were estimated according to the probability level corresponding to the design life of the platform on the fitted curves. The maximum von-Mises stress from 1 h and 3 h showed a large deviation depending on the analysis time, while the MPM von-Mises stress estimated by applying the same probability level did not show a large deviation, and the variability according to random execution also showed a similar trend. Through further research, it is expected that the optimal analysis time for estimating MPM stress can be determined, allowing for efficient structural integrity assessment for FOWT.