dc.contributor.author | Gürgen, Samet | |
dc.contributor.author | Altın, İsmail | |
dc.contributor.author | Özkök, Murat | |
dc.date.accessioned | 12.07.201910:50:10 | |
dc.date.accessioned | 2019-07-12T22:06:28Z | |
dc.date.available | 12.07.201910:50:10 | |
dc.date.available | 2019-07-12T22:06:28Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Gurgen, S., Altin, I., Ozkok, M. (2018). Prediction of main particulars of a chemical tanker at preliminary ship design using artificial neural network. Ships and Offshore Structures, 13 (5), pp. 459-465.
https://doi.org/10.1080/17445302.2018.1425337 | en_US |
dc.identifier.issn | 1744-5302 | |
dc.identifier.issn | 1754-212X | |
dc.identifier.uri | https://doi.org/10.1080/17445302.2018.1425337 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12508/726 | |
dc.description | WOS: 000428683100002 | en_US |
dc.description.abstract | Preliminary ship design is an important part of the ship design and a reliable design tool is needed for this stage. The aim of this study was to develop an artificial neural network (ANN) model to predict main particulars of a chemical tanker at preliminary design stage. Deadweight and vessel speed were used as the input layer; and length overall, length between perpendiculars, breadth, draught and freeboard were used as the output layer. The back-propagation learning algorithm with two different variants was used in the network. After training the ANN, the average of mean absolute percentage error value was obtained 4.552%. It is also observed that the correlation coefficients obtained were 0.99921, 0.99775, 0.99537 and 0.9984 for training, validation, test and all data-sets, respectively. The results showed that initial main particulars of chemical tankers are determined within high accuracy levels as compared to the sample ship data. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Taylor and Francis Ltd. | en_US |
dc.relation.isversionof | 10.1080/17445302.2018.1425337 | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Preliminary ship design | en_US |
dc.subject | Artificial neural network | en_US |
dc.subject | Chemical tanker | en_US |
dc.subject.classification | Engineering | en_US |
dc.subject.classification | Marine | en_US |
dc.subject.classification | Container Ship | Whipping | Slamming | en_US |
dc.subject.other | Backpropagation | en_US |
dc.subject.other | Backpropagation algorithms | en_US |
dc.subject.other | Neural networks | en_US |
dc.subject.other | Sailing vessels | en_US |
dc.subject.other | Shipbuilding | en_US |
dc.subject.other | Ships | en_US |
dc.subject.other | Artificial neural network models | en_US |
dc.subject.other | Backpropagation learning algorithm | en_US |
dc.subject.other | Chemical tankers | en_US |
dc.subject.other | Correlation coefficient | en_US |
dc.subject.other | High-accuracy | en_US |
dc.subject.other | Mean absolute percentage error | en_US |
dc.subject.other | Preliminary ship designs | en_US |
dc.subject.other | Tankers (ships) | en_US |
dc.title | Prediction of main particulars of a chemical tanker at preliminary ship design using artificial neural network | en_US |
dc.type | article | en_US |
dc.relation.journal | Ships and Offshore Structures | en_US |
dc.contributor.department | Barbaros Hayrettin Gemi İnşaatı ve Denizcilik Fakültesi -- Gemi İnşaatı ve Gemi Makineleri Mühendisliği Bölümü | en_US |
dc.contributor.authorID | 0000-0001-7036-8829 | en_US |
dc.identifier.volume | 13 | en_US |
dc.identifier.issue | 5 | en_US |
dc.identifier.startpage | 459 | en_US |
dc.identifier.endpage | 465 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.contributor.isteauthor | Gürgen, Samet | en_US |
dc.relation.index | Web of Science - Scopus | en_US |
dc.relation.index | Web of Science Core Collection - Science Citation Index Expanded | en_US |