ANN-Based Decision Making in Station Keeping for Geotechnical Drilling Vessel
Offshore vessels (OVs) often require precise station-keeping and some vessels, for example, vessels involved in geotechnical drilling, generally use Spread Mooring (SM) or Dynamic Positioning (DP) systems. Most of these vessels are equipped with both systems to cover all ranges of water depths. Howe...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/9/6/596 |
| _version_ | 1797531997834313728 |
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| author | Murugan Ramasamy Mohammed Abdul Hannan Yaseen Adnan Ahmed Arun Kr Dev |
| author_facet | Murugan Ramasamy Mohammed Abdul Hannan Yaseen Adnan Ahmed Arun Kr Dev |
| author_sort | Murugan Ramasamy |
| collection | DOAJ |
| description | Offshore vessels (OVs) often require precise station-keeping and some vessels, for example, vessels involved in geotechnical drilling, generally use Spread Mooring (SM) or Dynamic Positioning (DP) systems. Most of these vessels are equipped with both systems to cover all ranges of water depths. However, determining which system to use for a particular operational scenario depends on many factors and requires significant balancing in terms of cost-benefit. Therefore, this research aims to develop a platform that will determine the cost factors for both the SM and DP station-keeping systems. Operational information and cost data are collected for several field operations, and Artificial Neural Networks (ANN) are trained using those data samples. After that, the trained ANN is used to predict the components of cost for any given environmental situation, fieldwork duration and water depth. Later, the total cost is investigated against water depth for both DP and SM systems to determine the most cost-effective option. The results are validated using two operational scenarios for a specific geotechnical vessel. This decision-making algorithm can be further developed by adding up more operational data for various vessels and can be applied in the development of sustainable decision-making business models for OVs operators. |
| first_indexed | 2024-03-10T10:52:46Z |
| format | Article |
| id | doaj.art-8979ea1968684f0986195e8bf123a7a9 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-10T10:52:46Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-8979ea1968684f0986195e8bf123a7a92023-11-21T22:05:02ZengMDPI AGJournal of Marine Science and Engineering2077-13122021-05-019659610.3390/jmse9060596ANN-Based Decision Making in Station Keeping for Geotechnical Drilling VesselMurugan Ramasamy0Mohammed Abdul Hannan1Yaseen Adnan Ahmed2Arun Kr Dev3Singapore Campus, The Faculty of Science, Agriculture & Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UKSingapore Campus, The Faculty of Science, Agriculture & Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UKThe Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, Skudai 81310, Johor, MalaysiaSingapore Campus, The Faculty of Science, Agriculture & Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UKOffshore vessels (OVs) often require precise station-keeping and some vessels, for example, vessels involved in geotechnical drilling, generally use Spread Mooring (SM) or Dynamic Positioning (DP) systems. Most of these vessels are equipped with both systems to cover all ranges of water depths. However, determining which system to use for a particular operational scenario depends on many factors and requires significant balancing in terms of cost-benefit. Therefore, this research aims to develop a platform that will determine the cost factors for both the SM and DP station-keeping systems. Operational information and cost data are collected for several field operations, and Artificial Neural Networks (ANN) are trained using those data samples. After that, the trained ANN is used to predict the components of cost for any given environmental situation, fieldwork duration and water depth. Later, the total cost is investigated against water depth for both DP and SM systems to determine the most cost-effective option. The results are validated using two operational scenarios for a specific geotechnical vessel. This decision-making algorithm can be further developed by adding up more operational data for various vessels and can be applied in the development of sustainable decision-making business models for OVs operators.https://www.mdpi.com/2077-1312/9/6/596ANN offshoredynamic positioningspread mooringdecision makingoffshore vessel |
| spellingShingle | Murugan Ramasamy Mohammed Abdul Hannan Yaseen Adnan Ahmed Arun Kr Dev ANN-Based Decision Making in Station Keeping for Geotechnical Drilling Vessel Journal of Marine Science and Engineering ANN offshore dynamic positioning spread mooring decision making offshore vessel |
| title | ANN-Based Decision Making in Station Keeping for Geotechnical Drilling Vessel |
| title_full | ANN-Based Decision Making in Station Keeping for Geotechnical Drilling Vessel |
| title_fullStr | ANN-Based Decision Making in Station Keeping for Geotechnical Drilling Vessel |
| title_full_unstemmed | ANN-Based Decision Making in Station Keeping for Geotechnical Drilling Vessel |
| title_short | ANN-Based Decision Making in Station Keeping for Geotechnical Drilling Vessel |
| title_sort | ann based decision making in station keeping for geotechnical drilling vessel |
| topic | ANN offshore dynamic positioning spread mooring decision making offshore vessel |
| url | https://www.mdpi.com/2077-1312/9/6/596 |
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