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.

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.

본 연구의 목표는 허용 응력 기준을 만족하도록 설계된 캔틸레버형 헬리데크 모델을 대상으로 비선형 좌굴에 대한 안전성을 평가하는 것이다. 헬리데크를 구성하는 대부분의 부재들이 좌굴에 취약하다고 알려진 세장형 구조물이기 때문에, 비선형 좌굴에 대한 안전성이 특히 보장되어야 한다. 적절한 설계 조건들과 연구 결과 평가를 위한 기준을 결정하기 위해, 헬리데크 설계에 널리 쓰이는 DNV의 OS-E401과 CAA의 CAP-437 규정을 참고하였다. 또한 헬리데크의 동적 안정성을 평가하기 위해 상용 프로그램인 ANSYS Mechanical APDL을 사용하여 유한 요소 해석을 수행하였다. 먼저 비상착륙조건 하에서 9개의 헬리콥터 착륙 위치와 5개의 풍향으로 조합된 45개의 상황에 대한 선형 좌굴 해석을 수행하고, 그 후 착륙 하중에 대한 임계 좌굴하중을 계산하여 가장 위험한 하중 조건을 찾아내었다. 다음으로 그 조건에서의 첫번째 좌굴 모드 형상을 초기 결함으로 가한 후 비선형 해석을 수행하였고, 소성 변형률이 0.2%가 되는 지점에서의 착륙하중을 비선형 좌굴하중으로 결정하였다. 최종적으로 설계 착륙 하중에 대한 선형 또는 비선형 좌굴이 발생할 때 가해진 착륙하중의 비를 임계 설계하중계수로 산정하였으며, 이를 통해 주어진 캔틸레버형 헬리데크 설계가 비선형 좌굴에 대해 구조적으로 안전함을 검증하였다.

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.