FEM Analysis of Dropped Container in Offshore Platform Operations with Nonlinear Fitting Based on PLS-Based Neural Network

Station-keeping using mooring lines is an important part of the design of floating offshore platforms, and has been used on most types of floating platforms, such as Spar, Semi-submersible, and FPSO. It is of great interest to monitor the integrity of the mooring lines to detect any damaged and/or failures. This paper presents a method to train an Artificial Neural Network (ANN) model for damage detection of mooring lines based on a patented methodology that uses detection of subtle shifts in the long drift period of a moored floating vessel as an indicator of mooring line failure, using only GPS monitoring. In case of an FPSO, the total mass or weight of the vessel is also used as a variable. The training of the ANN model employs a back-propagation learning algorithm and an automatic method for determination of ANN architecture. The input variables of the ANN model can be derived from the monitored motion of the platform by GPS (plus vessel’s total mass in case of an FPSO), and the output of the model is the identification of a specific damaged mooring line. The training and testing of the ANN model use the results of numerical analyses for a semi-submersible offshore platform with twenty mooring lines for a range of metocean conditions. The training data cover the cases of intact mooring lines and a damaged line for two selected adjacent lines. As an illustration, the evolution of the model at various training stages is presented in terms of its accuracy to detect and identify a damaged mooring line. After successful training, the trained model can detect with great fidelity and speed the damaged mooring line. In addition, it can detect accurately the damaged mooring line for sea states that are not included in the training. This demonstrates that the model can recognize and classify patterns associated with a damaged mooring line and separate them from patterns of intact mooring lines for sea states that are and are not included in the training. This study demonstrates a great potential for the use of a more general and comprehensive ANN model to help monitor the station keeping integrity of a floating offshore platform and the dynamic behavior of floating systems in order to forecast problems before they occur by detecting deviations in historical patterns.

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A Customized Deep Neural Network Approach to Investigate Travel Mode Choice with Interpretable Utility Information

Journal of Advanced Transportation ◽

10.1155/2020/5364252 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Zhengchao Zhang ◽

Congyuan Ji ◽

Yineng Wang ◽

Yanni Yang

Keyword(s):

Neural Network ◽

Learning Community ◽

Mode Choice ◽

Feature Representation ◽

Classification Problems ◽

Neural Network Approach ◽

Nonlinear Fitting ◽

Linear Utility ◽

Utility Models ◽

Fully Connected

Discrete choice modeling of travel modes is an essential part of traffic planning and management. Thus far, this field has been dominated by multinomial logit (MNL) models with a linear utility specification. However, deep neural networks (DNNs), owing to their powerful capacity of nonlinear fitting, are now rapidly replacing these models. This is because, by using DNNs, mode choice can be assimilated with the classification problems within the machine learning community. This article proposes a newly designed DNN framework for traffic mode choice in the style of two hidden layers. First, a local-connected layer automatically extracts an effective utility specification from the available data, and then, a fully connected layer augments the feature representation. Validated by a practical city-wide multimodal traffic dataset in Beijing, our model significantly outperforms the random utility models and simple fully connected neural network in terms of the prediction accuracy. Besides the comparison of the predictive power, we also present the interpretability of the proposed model.

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Study on the Applications of Neural Networks for Processing Deformation Monitoring Data

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.501-504.2149 ◽

2014 ◽

Vol 501-504 ◽

pp. 2149-2153 ◽

Cited By ~ 2

Author(s):

Cai Yun Gao ◽

Xi Min Cui ◽

Xue Qian Hong

Keyword(s):

Neural Network ◽

Neural Networks ◽

Prediction Models ◽

Deformation Monitoring ◽

Monitoring Data ◽

Nonlinear Fitting ◽

High Rise ◽

High Rise Building ◽

Good Ability ◽

Neural Network Prediction

Accurately estimating the deformation of high-rise building is a very important work for surveyors, however it is very difficult to get an accurate and reliable predictor. In this paper, artificial neural network has been applied here because of its good ability of nonlinear fitting. On the basis of the high-rise building monitoring data, three prediction models including the BP, RBF and GRNN neural network prediction models were established, the comparative analysis for the prediction accuracy of the three models was obtained. The results show that neural network is capable for prediction, and GRNN possess higher capability in prediction and better adaptability in comparing with other two neural networks.

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Prediction of Life of Compound Die Using Artificial Neural Network (ANN)

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.622-623.664 ◽

2014 ◽

Vol 622-623 ◽

pp. 664-671 ◽

Cited By ~ 6

Author(s):

Sachin Kashid ◽

Shailendra Kumar

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Sheet Metal ◽

Research Work ◽

Fem Simulation ◽

Fem Analysis ◽

Ann Model ◽

Sheet Metal Parts ◽

Artificial Neural ◽

Artificial Neural Network Ann

Prediction of life of compound die is an important activity usually carried out by highly experienced die designers in sheet metal industries. In this paper, research work involved in the prediction of life of compound die using artificial neural network (ANN) is presented. The parameters affecting life of compound die are investigated through FEM analysis and the critical simulation values are determined. Thereafter, an ANN model is developed using MATLAB. This ANN model is trained from FEM simulation results. The proposed ANN model is tested successfully on different compound dies designed for manufacturing sheet metal parts. A sample run of the proposed ANN model is also demonstrated in this paper.

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Sensitivity analysis of the GTN damage parameters at different temperature for dynamic fracture propagation in X70 pipeline steel using neural network

Frattura ed Integrità Strutturale ◽

10.3221/igf-esis.58.32 ◽

2021 ◽

Vol 15 (58) ◽

pp. 442-452

Author(s):

Abdelmoumin Ouladbrahim ◽

Idir Belaidi ◽

Samir Khatir ◽

Erica Magagnini ◽

Roberto Capozucca ◽

...

Keyword(s):

Neural Network ◽

Pipeline Steel ◽

Fem Analysis ◽

Maximum Load ◽

Impact Testing ◽

The Neural Network ◽

Different Temperatures ◽

Satisfactory Approximation ◽

Artificial Neural Network Ann ◽

Growth Of Voids

In this paper, the initial and maximum load was studied using the Finite Element Modeling (FEM) analysis during impact testing (CVN) of pipeline X70 steel. The Gurson-Tvergaard-Needleman (GTN) constitutive model has been used to simulate the growth of voids during deformation of pipeline steel at different temperatures. FEM simulations results used to study the sensitivity of the initial and maximum load with GTN parameters values proposed and the variation of temperatures. Finally, the applied artificial neural network (ANN) is used to predict the initial and maximum load for a given set of damage parameters X70 steel at different temperatures, based on the results obtained, the neural network is able to provide a satisfactory approximation of the load initiation and load maximum in impact testing of X70 Steel.

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Prediction of Life of Die Block Using Artificial Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.592-594.689 ◽

2014 ◽

Vol 592-594 ◽

pp. 689-693 ◽

Cited By ~ 3

Author(s):

Sachin Kashid ◽

Shailendra Kumar

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Research Work ◽

Fem Simulation ◽

Fem Analysis ◽

Die Design ◽

Ann Model ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Important Activity

Prediction of life of die block is an important activity of die design usually carried out by highly experienced die designers in sheet metal industries. In this paper, research work involved in the prediction of life of die block of compound die using artificial neural network (ANN) is presented. The parameters affecting life of die block are investigated through Finite Element Method (FEM) analysis and the critical simulation values are determined. Thereafter, an ANN model is developed using MATLAB. This ANN model is trained from FEM simulation results. The proposed ANN model is tested successfully on different die block. A sample run of the proposed ANN model is also demonstrated in this paper.

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A Method of Containment Leakage Rate Estimation Based on Convolution Neural Network

Frontiers in Energy Research ◽

10.3389/fenrg.2021.637283 ◽

2021 ◽

Vol 9 ◽

Author(s):

Hao Wang ◽

Jingquan Liu ◽

Guangyao Xie ◽

Xianping Zhong ◽

Xiangqi Fan

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Nuclear Power ◽

Power Plants ◽

Least Square Method ◽

Leakage Rate ◽

Least Square ◽

Nonlinear Fitting ◽

The Third ◽

Start Up

As the nuclear power plant containment is the third barrier to nuclear safety, real-time monitoring of containment leakage rate is very important in addition to the overall leakage test before an operation. At present, most of the containment leakage rate monitoring systems calculate the standard volume of moist air in the containment through monitoring parameters and calculate the daily leakage rate by the least square method. This method requires several days of data accumulation to accurately calculate. In this article, a new leakage rate modeling technique is proposed using a convolutional neural network based on data of the monitoring system. Use the daily monitoring parameters of nuclear power plants to construct inputs of the model and train the convolutional neural network with daily leakage rates as labels. This model makes use of the powerful nonlinear fitting ability of the convolutional neural network. It can use 1-day data to accurately calculate the containment leakage rate during the reactor start-up phase and can timely determine whether the containment leak has occurred during the start-up phase and deal with it in time, to ensure the integrity of the third barrier.

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Optimum Design of Machine Tool Structures Based on BP Neural Network and Genetic Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.655-657.1291 ◽

2013 ◽

Vol 655-657 ◽

pp. 1291-1295 ◽

Cited By ~ 1

Author(s):

Song Mei Yuan ◽

Zhong Fei Zhan ◽

Yao Li

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Machine Tool ◽

Dynamic Characteristics ◽

Bp Neural Network ◽

Traditional Method ◽

Fem Analysis ◽

Fem Model ◽

Static And Dynamic Characteristics ◽

Machine Tool Structures

In order to estimate and optimize the static and dynamic characteristics of machine tool, the full parameterized FEM model of it is established and studied in the paper. After the FEM analysis of bed, this paper takes a machine bed as example, presents a method of combination of BP Neural-Network(NN) and Genetic-Algorithm(GA) to optimize dynamic characteristics and realizes the structural optimization of the bed. It proved that this method takes less time, and more precision compared to traditional method.

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Estimation of Welding Distortion Using Neural Network

Journal of Ship Production ◽

10.5957/jsp.2008.24.4.190 ◽

2008 ◽

Vol 24 (04) ◽

pp. 190-195

Author(s):

Yasuhisa Okumoto

Keyword(s):

Neural Network ◽

Finite Element Method ◽

Computing Time ◽

Fem Analysis ◽

Steel Structure ◽

Welding Distortion ◽

Technical Knowledge ◽

Simple Manner ◽

Different Dimensions ◽

Teacher Data

Recently, the prediction of welding distortion of steel structure has been carried out widely. Though the thermal elastic-plasticity finite element method (FEM) analysis is usually required to ensure the accuracy of the estimation, technical knowledge is required, and long computing time is necessary. In this study, simplification of the prediction was done using a neural network model, in which the calculation result of the FEM analysis was incorporated in teacher data, for the fillet welding of T-type buildup structure, etc. This program would be able to estimate the welding distortion of the joint with different dimensions and welding conditions in a simple manner.

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