...Yi Zhang; Ning Ma; Xiechong Gu; QiQi Shi _ This paper introduces a method combining Bayesian optimization (BO) with multilayer perceptron (MLP) neural network regression to build a surrogate model in the process of automatic ship hull form optimization. The MLP regression replaces computer fluid...

... 2023 6 1 2023 1 6 2023 1 6 2023 Copyright 2023, The International Society of Offshore and Polar Engineers asia government freight & logistics services library marine transportation artificial intelligence frequency threshold neural network international journal...

... and Polar Engineers deep learning prediction elevation asia government artificial intelligence ship international journal neural network united states government china government simulation dimension polar engineering frequency machine learning wave elevation application engineering...

... a multicomponent hybrid model for wave height prediction. Wu et al. (2020) proposed a hybrid physics-based machine learning model and successfully trained it with the wind-driven wave data. Also, a convolutional neural network (CNN) and recurrent neural network (RNN) might be used in geosciences. Wang et al. (2020...

... wave elevations as in many other elds. Although the wave propagation in the time domain is nonlinear, waves are not completely random and can be predicted within a certain range of accuracy error. The arti cial neural network Received July 14, 2022; updated and further revised manuscript received...

... utilized as precise, fast, and cost-effective tools to simulate the ice-gouging problem. Kioka et al. (2003) modeled the scour depth as a function of the ice keel by using a layered neural network (NN) method. They showed that the NN algorithm managed to simulate the target value with good precision. Azimi...

... that a properly trained convolutional neural network (CNN) is able to accurately detect both the location and level of damage associated with cracks in steel plates based on acceleration signals. However, the effects of the aspect ratio of the plates and crack direction on the detection results have not been...

... network was established between the ocean environmental loads and the extreme values of the 6 DoF response. The predicted results indicate that the present LSTM neural network method could provide higher accuracy, with root mean square errors of 0.0724 and 0.0326 for rolling and pitching values...

...Dongliang Ma; Deyu Wang This paper presents a prediction model to detect the location and level of cracks in plates using a method based on the convolutional neural network (CNN), which can automatically learn damage characteristics without prior knowledge. A simply supported plate with cracks...

... of Offshore and Polar Engineers production monitoring neural network Reservoir Surveillance Upstream Oil & Gas Engineering Artificial Intelligence frequency mechanism production control production logging flow pattern cylinder fluid mech Triantafyllou instability Flow Instability foil...

... that employs artificial neural networks (ANNs) calibrated from nonlinear finite element (FE) analyses. The method presented intends to improve on current industry best practice by directly considering the limit states relevant to global buckling to produce designs with consistent levels of reliability...

... electricity is a random process. In such a working condition, a single design/operation point is nonexistent. For this purpose, this paper presents a short-term prediction of the random process using an artificial neural network (ANN), aiming to provide near-future information for the control system...

... construction performance so as to avoid failures of the supporting system. This paper attempts to predict the diaphragm wall deflection in deep excavations by using a back-propagation artificial neural network (NN) learning model. Case histories of deep excavations (with 4 to 8 excavation stages) from...

... of the current total unstretched length of the mooring lines, The model consists of 2 modules. The first includes the identification of the current status of the MFB, which is implemented through the adoption of artificial neural networks. The second includes the determination, through the development...

... D ) , and the dilatometer modulus ( E D )—undrained shear strength is estimated using only the horizontal stress index ( K D ). In this paper, the applicability of the flat DMT to Korean soft clay deposits is investigated. An artificial neural network (ANN) model is developed to predict undrained...

... responsibility sustainable development logic & formal reasoning neural network Artificial Intelligence sustainability carpet tile initial pressure Upstream Oil & Gas renewable energy approximation transient experiment impulse response function water free surface mathematical model air...

... Intelligence radiation force infinite frequency surge radiation force transformation neural network identification frequency response time-domain model Simulation transfer function Tension Leg Platform application Jeffery system identification Fourier transformation frequency domain description...

... be used to investigate the stress distribution in the ice around the structure. 1 6 1992 1 6 1992 1992. The International Society of Offshore and Polar Engineers Artificial Intelligence fractured zone ice sheet investigation numerical experiment neural network ice load time...