Prediction of main particulars of a chemical tanker at preliminary ship design using artificial neural network

Gürgen, Samet; Altın, İsmail; Özkök, Murat

doi:10.1080/17445302.2018.1425337

Cited by 28 publications

(20 citation statements)

References 15 publications

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“…ANN is a logical software developed by imitating the working mechanism of the human brain, to perform basic functions such as brain learning the new information, recalling the learned information (Silva et al, 2017). ANN also provides estimates of intermediate values that cannot be performed in experiments and is frequently used in scientifi c work areas such as engineering (Khorasani and Yazdi, 2017;Gurgen et al, 2018), health sciences (Beauchet et al, 2018), etc. Recently, ANN has received considerable attention in the fi eld of wood products industry.…”

Section: Sažetak • U Istraživanju Je Modelirana Upojnost Vode I Debljmentioning

confidence: 99%

“…A total of 128 data obtained from experimental study were used to set an ANN model. The number of different hidden neurons (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) and two different training algorithms (LM and SCG) were used to achieve the optimal network with the best performance. As a result, the optimal network was obtained in 16 hidden neurons and the LM algorithm.…”

Section: Zaključakmentioning

confidence: 99%

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Application of artificial neural network to predict the effect of paraffin addition on water absorption and thickness swelling of MDF

2019

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In this study, water absorption and thickness swelling values of medium density fiberboard (MDF) were modelled by artificial neural networks (ANN). MDF panels were produced with different rates of paraffin (0.0-control, 0.5, 1 and 1.5 %) at different press temperatures (170 and 190 °C). After conditioning of MDF, water absorption (WA) and thickness swelling (TS) of samples were carried out at specific intervals within 24 hours. Then, the data obtained from these experiment were modelled using ANN. Paraffin addition rate, press temperature and immersion time in water were used as the input parameters, while WA and TS values of MDF were used as the output parameters. After training of ANN, it was found that correlation coefficients (R) were close to 1 for training, validation, test and all data set. Mean absolute percentage error (MAPE) and mean square error (MSE) were determined as 2.94 % and 0.57, respectively, for all data sets. As a result of this study, the use of proposed ANN model may be recommended to predict the water absorption and thickness swelling of panels instead of complex and time-consuming studies such as empirical formulas.

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Section: Sažetak • U Istraživanju Je Modelirana Upojnost Vode I Debljmentioning

confidence: 99%

Section: Zaključakmentioning

confidence: 99%

Application of artificial neural network to predict the effect of paraffin addition on water absorption and thickness swelling of MDF

2019

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“…There have been no publications in the scientific literature on the use of artificial neural networks (ANN) to determine the length of a container vessel. Only Gurgen et al [10] applied an ANN to predict the main dimensions of chemical tankers using deadweight capacity and speed. An ANN based on a multi-layer perceptron structure with 13 neurons in a hidden layer was used in this research.…”

Section: Introductionmentioning

confidence: 99%

Application of an Artificial Neural Network and Multiple Nonlinear Regression to Estimate Container Ship Length Between Perpendiculars

Cepowski

Chorab

Łozowicka

2021

Polish Maritime Research

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Container ship length was estimated using artificial neural networks (ANN), as well as a random search based on Multiple Nonlinear Regression (MNLR). Two alternative equations were developed to estimate the length between perpendiculars based on container number and ship velocity using the aforementioned methods and an up-to-date container ship database. These equations could have practical applications during the preliminary design stage of a container ship. The application of heuristic techniques for the development of a MNLR model by variable and function randomisation leads to the automatic discovery of equation sets. It has been shown that an equation elaborated using this method, based on a random search, is more accurate and has a simpler mathematical form than an equation derived using ANN.

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“…The pre-building process includes ship design and consists of four stages: concept design, preliminary design, contract design, and detailed design. 22,23 During building, there are stages such as steel construction, welding, equipment, and outfitting processes. Significantly, the outfitting phase starts from the production steps in the steel construction phase of the ship and continues rapidly until the delivery phase after the vessel is launched.…”

Section: Introductionmentioning

confidence: 99%

Minimization of total trim loss occurring in pipe cutting in shipbuilding with genetic algorithm

Türk

Saral

Özkök

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part M:

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Outfitting is a critical stage in the shipbuilding process. Within the outfitting, the construction of pipe systems is a phase that has a significant effect on time and cost. While cutting the pipes required for the pipe systems in shipyards, the cutting process is usually performed randomly. This can result in large amounts of trim losses. In this paper, we present an approach to minimize these losses. With the proposed method it is aimed to base the pipe cutting process on a specific systematic. To solve this problem, Genetic Algorithms (GA), which gives successful results in solving many problems in the literature, have been used. Different types of genetic operators have been used to investigate the search space of the problem well. The results obtained have proven the effectiveness of the proposed approach.

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Prediction of main particulars of a chemical tanker at preliminary ship design using artificial neural network

Cited by 28 publications

References 15 publications

Application of artificial neural network to predict the effect of paraffin addition on water absorption and thickness swelling of MDF

Application of artificial neural network to predict the effect of paraffin addition on water absorption and thickness swelling of MDF

Application of an Artificial Neural Network and Multiple Nonlinear Regression to Estimate Container Ship Length Between Perpendiculars

Minimization of total trim loss occurring in pipe cutting in shipbuilding with genetic algorithm

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