This paper presents a machine learning method for detecting the mooring failures of SFT (submerged floating tunnel) based on DNN (deep neural network). The floater-mooring-coupled hydro-elastic time-domain numerical simulations are conducted under various random wave excitations and failure/intact scenarios. Then, the big-data is collected at various locations of numerical motion sensors along the SFT to be used for the present DNN algorithm. In the input layer, tunnel motion-sensor signals and wave conditions are inputted while the output layer provides the probabilities of 21 failure scenarios. In the optimization stage, the numbers of hidden layers, neurons of each layer, and epochs for reliable performance are selected. Several activation functions and optimizers are also tested for the present DNN model, and Sigmoid function and Adamax are respectively adopted to enhance the classification accuracy. Moreover, a systematic sensitivity test with respect to the numbers and arrangements of sensors is performed to find the appropriate sensor combination to achieve target prediction accuracy. The technique of confusion matrix is used to represent the accuracy of the DNN algorithms for various cases, and the classification accuracy as high as 98.1% is obtained with seven sensors. The results of this study demonstrate that the DNN model can effectively monitor the mooring failures of SFTs utilizing real-time sensor signals.

This paper introduces a comprehensive, data-driven framework for parametrically estimating directional ocean wave spectra from numerically simulated FPSO (Floating Production Storage and Offloading) vessel motions. Leveraging a mid-fidelity digital twin of a spread-moored FPSO vessel in the Guyana Sea, this approach integrates a wide range of statistical values calculated from the time histories of vessel responses—displacements, angular velocities, and translational accelerations. Artificial neural networks (ANNs), trained and optimized through hyperparameter tuning and feature selection, are employed to estimate wave parameters including the significant wave height, peak period, main wave direction, enhancement parameter, and directional-spreading factor. A systematic correlation analysis ensures that informative input features are retained, while extensive sensitivity tests confirm that richer input sets notably improve predictive accuracy. In addition, comparisons against other machine learning (ML) methods—such as Support Vector Machines, Random Forest, Gradient Boosting, and Ridge Regression—demonstrate the present ANN model’s superior ability to capture intricate nonlinear interdependencies between vessel motions and environmental conditions.

서 론 해양구조물 설계에서 파랑중 구조물의 거동 해석은 설계의 핵심적인 요인이다. 특히, 구조물의 동적 거동에 절대적인 영향 을 미치는 파의 전파와 구조물간의 상호작용의 이해는 매우 중 요한 문제이다. 또한, 수치해석뿐만 아니라, 모형실험을 통해 정 확하고 정밀한 해석이 중요하다. 파랑 현상을 정확히 이해하기 위해 모형실험을 수행할 때, 시험수조에서 정확하고 안정적인 입사파를 생성하기 위해서는 조파기의 성능을 이해하고 이를 제어할 수 있는 알고리듬이 중요하다. 고전적인 조파기 설계는 조파판을 파도의 입자 운동이 생기도록 동력장치와 연동하는 왕복운동 개념의 기구설계에 초점이 맞춰졌다. 왕복운동에 따 른 조파판의 운동으로 이론해를 구하고 이를

This study presents a two-step artificial neural network (ANN) approach for detecting mooring failures in a spread-moored floating production storage and offloading (FPSO) vessel using platform motion data. Synthetic statistical data generated from time-domain simulations were utilized as input features. The first-step ANN determines whether the mooring system is intact or a failure has occurred within a specific mooring group. If a failure is detected, the second-step ANN identifies the exact failed mooring line within the group. Hyperparameter optimization was performed using Bayesian and random search methods, and multiple input variable sets were evaluated. The results indicate that the mean values of platform motions, particularly surge and yaw, play a crucial role in accurately identifying mooring failures. Additionally, selecting the top 10 features based on mutual information can be a way to improve detection accuracy. The proposed two-step ANN approach outperformed the single-step ANN method, achieving higher classification accuracy and reducing misclassification between mooring lines. These findings demonstrate the potential of machine learning for near-real-time mooring integrity monitoring, offering a practical and efficient alternative to traditional inspection methods.