... the prediction of riser VIV an extremely challenging task. Recently, thanks to the rapid development of machine learning techniques, different neural network (NN) architectures have been applied to many nonlinear problems to establish the mapping relation between input and output variables by learning from...

.... (2020) investigated ship hull scale effects on real-time motion prediction using the AR model. However, these methods are constrained by the intricate and variable marine waves, limiting their accuracy of prediction. deep learning machine learning neural network trimaran ocean engineering...

... ABSTRACT Many machine learning applications in engineering fields have been conducted without enough description on implementation details during dataset preparations. Theoretically a pure data driven machine learning procedure works well when a single measuring unit, or a combination of base...

... production monitoring gnn isph-cnn production logging simulation prediction artificial intelligence production control isph isph-gnn application isph-cq single-phase isph interaction preliminary investigation qalefoam trough reservoir surveillance machine learning particle...

... the historical data of the motion response of the platform itself to analyze and seek the law and predict. artificial intelligence machine learning sery neural network subsea system algorithm information deep learning floating production systems decomposition eemd algorithm lstm neural network...

... hyperparameter concentration factor geometric parameter machine learning scf prediction bayesian optimization conference proceedings civil engineering Proceedings of the Thirty-fourth (2024) International Ocean and Polar Engineering Conference Rhodes, Greece, June 16 21, 2024 Copyright © 2024...

...), which is the most time-consuming part. Recently, the machine learning (ML) techniques have been widely used in the fluid dynamics. In this paper, the graph neural network (GNN) is combined with ISPH and used to predict the fluid pressure instead of solving the PPE directly. The GNN supported ISPH method...

... of airfoil hydrodynamic performance based on machine learning is carried out. The integrated learning method XGBoost is used to establish a rapid prediction model of airfoil characteristics to achieve efficient prediction of unknown airfoil performance. The results show that the improved motion model can...

... geology optimization mooring system geologist artificial intelligence machine learning simulation turbine prediction configuration engineering fowt constraint efficiency optimization problem subsea system platform diameter complexity neuron surrogate optimization modeling...

... can be found for instance in Liu et al. (2017), who proposed a combined hydrodynamic and hydraulic model with increasing levels of details. equation artificial intelligence geology geologist social responsibility subsea system floater coefficient energy raft frequency machine...

... energy sustainability machine learning setup spatial auto-correlation application avg 8 journal prediction bathymetry assessment Proceedings of the Thirty-fourth (2024) International Ocean and Polar Engineering Conference Rhodes, Greece, June 16 21, 2024 Copyright © 2024 by the International...

... incorporated during the baseline (training) phase. machine learning turbine vibration vibration turbine structural vibration anomaly decision tree learning prediction detection health monitoring probability abnormal vibration artificial intelligence owt offshore wind turbine detection...

... et al. (2023) trained CNN algorithms on satellite images for sinkhole detection and classification. The use of infrared techniques is essential to overcome these challenges. To this end, this research focuses on machine learning-based image analysis methods. This study aims to improve analysis...

... surveys. probability node artificial intelligence maintenance allocation corrosion recoverability resilience assessment conditional probability dynamic bayesian network riser steel catenary riser reliability machine learning indicator diagnosis formula resilience assessment...

... the installation approach, hammer selection, and pile driving induced fatigue. This paper explores the use of machine learning (ML) techniques as an alternative to the industry standard wave equation analysis method. A ‘black-box’ methodology to predict blow count profiles based on typically available data...

... should be adaptable and feature the ability to fine tune soil input parameters to match the actual ground conditions progressively. This would provide improvement in estimates of settlement prediction with time. settlement probability settlement prediction machine learning extensometer...

..., the measured underwater sound should be classified. Hence, in this study, we investigated a method to automatically classify underwater sounds measured under a shipping lane using machine learning ( k -means clustering), and the results were validated using AIS data. The underwater sounds were recorded under...

... on electromechanical tuning fork resonators, which is integrated into edge computing systems, it not only can improve data collection but also accelerate deployment of machine learning models. It was noted (Jinxin An et al, 2021) that an autonomous underwater vehicle (AUV) was introduced, which improves the safety...

... matured, offering solutions for various applications. The focus now is on applying these algorithms effectively in the specific context of underwater Christmas tree installation and maintenance. artificial intelligence deep learning neural network module dilated convolution machine learning...

... this issue, an algorithm of multi-layer perceptron (MLP) is employed to predict critical geotechnical parameters, specifically friction angle for sand and shear strength for clay, from Cone Penetration Testing (CPT) data. Machine learning integrated with traditional geotechnical methods represents...