System Reliability Analysis of an Offshore Jacket Platform
This study investigates strategies for solving the system reliability of large three-dimensional jacket structures. These structural systems normally fail as a result of a series of different components failures. The failure characteristics are investigated under various environmental conditions and...
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| Veröffentlicht in: | Journal of Ocean University of China 2020-02, Vol.19 (1), p.47-59 |
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| container_title | Journal of Ocean University of China |
| container_volume | 19 |
| creator | Zhao, Yuliang Dong, Sheng Jiang, Fengyuan Guedes Soares, Carlos |
| description | This study investigates strategies for solving the system reliability of large three-dimensional jacket structures. These structural systems normally fail as a result of a series of different components failures. The failure characteristics are investigated under various environmental conditions and direction combinations. The
β
-unzipping technique is adopted to determine critical failure components, and the entire system is simplified as a series-parallel system to approximately evaluate the structural system reliability. However, this approach needs excessive computational effort for searching failure components and failure paths. Based on a trained artificial neural network (ANN), which can be used to approximate the implicit limit-state function of a complicated structure, a new alternative procedure is proposed to improve the efficiency of the system reliability analysis method. The failure probability is calculated through Monte Carlo simulation (MCS) with Latin hypercube sampling (LHS). The features and applicability of the above procedure are discussed and compared using an example jacket platform located in Chengdao Oilfield, Bohai Sea, China. This study provides a reference for the evaluation of the system reliability of jacket structures. |
| doi_str_mv | 10.1007/s11802-020-4181-2 |
| format | Article |
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β
-unzipping technique is adopted to determine critical failure components, and the entire system is simplified as a series-parallel system to approximately evaluate the structural system reliability. However, this approach needs excessive computational effort for searching failure components and failure paths. Based on a trained artificial neural network (ANN), which can be used to approximate the implicit limit-state function of a complicated structure, a new alternative procedure is proposed to improve the efficiency of the system reliability analysis method. The failure probability is calculated through Monte Carlo simulation (MCS) with Latin hypercube sampling (LHS). The features and applicability of the above procedure are discussed and compared using an example jacket platform located in Chengdao Oilfield, Bohai Sea, China. This study provides a reference for the evaluation of the system reliability of jacket structures.</description><identifier>ISSN: 1672-5182</identifier><identifier>EISSN: 1993-5021</identifier><identifier>EISSN: 1672-5174</identifier><identifier>DOI: 10.1007/s11802-020-4181-2</identifier><language>eng</language><publisher>Heidelberg: Science Press</publisher><subject>Artificial neural networks ; Component reliability ; Components ; Computer applications ; Computer simulation ; Earth and Environmental Science ; Earth Sciences ; Environmental conditions ; Failure ; Failure analysis ; Hypercubes ; Latin hypercube sampling ; Limit states ; Meteorology ; Monte Carlo simulation ; Neural networks ; Oceanography ; Offshore ; Offshore drilling rigs ; Oil and gas fields ; Oil field equipment ; Oil fields ; Probability theory ; Reliability ; Reliability analysis ; Reliability engineering ; Statistical methods ; Structural reliability ; System reliability</subject><ispartof>Journal of Ocean University of China, 2020-02, Vol.19 (1), p.47-59</ispartof><rights>Ocean University of China, Science Press and Springer-Verlag GmbH Germany 2020</rights><rights>2020© Ocean University of China, Science Press and Springer-Verlag GmbH Germany 2020</rights><rights>Copyright © Wanfang Data Co. Ltd. All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c352t-7a1f45d4fd2df3b93caffd5887e8ee86ac8aa9ee31b218969c8ea3e9db8bbb8b3</citedby><cites>FETCH-LOGICAL-c352t-7a1f45d4fd2df3b93caffd5887e8ee86ac8aa9ee31b218969c8ea3e9db8bbb8b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://www.wanfangdata.com.cn/images/PeriodicalImages/qdhydxxb-e/qdhydxxb-e.jpg</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11802-020-4181-2$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11802-020-4181-2$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,777,781,27905,27906,41469,42538,51300</link.rule.ids></links><search><creatorcontrib>Zhao, Yuliang</creatorcontrib><creatorcontrib>Dong, Sheng</creatorcontrib><creatorcontrib>Jiang, Fengyuan</creatorcontrib><creatorcontrib>Guedes Soares, Carlos</creatorcontrib><title>System Reliability Analysis of an Offshore Jacket Platform</title><title>Journal of Ocean University of China</title><addtitle>J. Ocean Univ. China</addtitle><description>This study investigates strategies for solving the system reliability of large three-dimensional jacket structures. These structural systems normally fail as a result of a series of different components failures. The failure characteristics are investigated under various environmental conditions and direction combinations. The
β
-unzipping technique is adopted to determine critical failure components, and the entire system is simplified as a series-parallel system to approximately evaluate the structural system reliability. However, this approach needs excessive computational effort for searching failure components and failure paths. Based on a trained artificial neural network (ANN), which can be used to approximate the implicit limit-state function of a complicated structure, a new alternative procedure is proposed to improve the efficiency of the system reliability analysis method. The failure probability is calculated through Monte Carlo simulation (MCS) with Latin hypercube sampling (LHS). The features and applicability of the above procedure are discussed and compared using an example jacket platform located in Chengdao Oilfield, Bohai Sea, China. This study provides a reference for the evaluation of the system reliability of jacket structures.</description><subject>Artificial neural networks</subject><subject>Component reliability</subject><subject>Components</subject><subject>Computer applications</subject><subject>Computer simulation</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Environmental conditions</subject><subject>Failure</subject><subject>Failure analysis</subject><subject>Hypercubes</subject><subject>Latin hypercube sampling</subject><subject>Limit states</subject><subject>Meteorology</subject><subject>Monte Carlo simulation</subject><subject>Neural networks</subject><subject>Oceanography</subject><subject>Offshore</subject><subject>Offshore drilling rigs</subject><subject>Oil and gas fields</subject><subject>Oil field equipment</subject><subject>Oil fields</subject><subject>Probability theory</subject><subject>Reliability</subject><subject>Reliability analysis</subject><subject>Reliability engineering</subject><subject>Statistical methods</subject><subject>Structural reliability</subject><subject>System reliability</subject><issn>1672-5182</issn><issn>1993-5021</issn><issn>1672-5174</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp1kM1Lw0AQxRdRsFb_AG8Bj7K6M5s0G2-l-Emh4sd52SSzbWqatLspNv-9CRE8eRhmDr_3mPcYuwRxA0LEtx5ACeQCBQ9BAccjNoIkkTwSCMfdPYmRR6DwlJ15vxYiktEkHrG799Y3tAneqCxMWpRF0wbTypStL3xQ28BUwcJav6odBS8m-6ImeC1NY2u3OWcn1pSeLn73mH0-3H_Mnvh88fg8m855JiNseGzAhlEe2hxzK9NEZsbaPFIqJkWkJiZTxiREElIElUySTJGRlOSpStNu5JhdD77fprKmWup1vXfdi17v8lWbHw6pJuyCC-hTjdnVQG9dvduTb_5wlKFEhVJiR8FAZa723pHVW1dsjGs1CN0XqodCdeer-0J1r8FB4zu2WpL7c_5f9AOm8Hh1</recordid><startdate>20200201</startdate><enddate>20200201</enddate><creator>Zhao, Yuliang</creator><creator>Dong, Sheng</creator><creator>Jiang, Fengyuan</creator><creator>Guedes Soares, Carlos</creator><general>Science Press</general><general>Springer Nature B.V</general><general>College of Engineering,Ocean University of China,Qingdao 266100,China%Centre for Marine Technology and Ocean Engineering (CENTEC),Instituto Superior Técnico,Lisboa 1049-001,Portugal</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7T7</scope><scope>7TN</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H95</scope><scope>H96</scope><scope>L.G</scope><scope>P64</scope><scope>2B.</scope><scope>4A8</scope><scope>92I</scope><scope>93N</scope><scope>PSX</scope><scope>TCJ</scope></search><sort><creationdate>20200201</creationdate><title>System Reliability Analysis of an Offshore Jacket Platform</title><author>Zhao, Yuliang ; Dong, Sheng ; Jiang, Fengyuan ; Guedes Soares, Carlos</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c352t-7a1f45d4fd2df3b93caffd5887e8ee86ac8aa9ee31b218969c8ea3e9db8bbb8b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Artificial neural networks</topic><topic>Component reliability</topic><topic>Components</topic><topic>Computer applications</topic><topic>Computer simulation</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Environmental conditions</topic><topic>Failure</topic><topic>Failure analysis</topic><topic>Hypercubes</topic><topic>Latin hypercube sampling</topic><topic>Limit states</topic><topic>Meteorology</topic><topic>Monte Carlo simulation</topic><topic>Neural networks</topic><topic>Oceanography</topic><topic>Offshore</topic><topic>Offshore drilling rigs</topic><topic>Oil and gas fields</topic><topic>Oil field equipment</topic><topic>Oil fields</topic><topic>Probability theory</topic><topic>Reliability</topic><topic>Reliability analysis</topic><topic>Reliability engineering</topic><topic>Statistical methods</topic><topic>Structural reliability</topic><topic>System reliability</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhao, Yuliang</creatorcontrib><creatorcontrib>Dong, Sheng</creatorcontrib><creatorcontrib>Jiang, Fengyuan</creatorcontrib><creatorcontrib>Guedes Soares, Carlos</creatorcontrib><collection>CrossRef</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Oceanic Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 1: Biological Sciences & Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Wanfang Data Journals - Hong Kong</collection><collection>WANFANG Data Centre</collection><collection>Wanfang Data Journals</collection><collection>万方数据期刊 - 香港版</collection><collection>China Online Journals (COJ)</collection><collection>China Online Journals (COJ)</collection><jtitle>Journal of Ocean University of China</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhao, Yuliang</au><au>Dong, Sheng</au><au>Jiang, Fengyuan</au><au>Guedes Soares, Carlos</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>System Reliability Analysis of an Offshore Jacket Platform</atitle><jtitle>Journal of Ocean University of China</jtitle><stitle>J. Ocean Univ. China</stitle><date>2020-02-01</date><risdate>2020</risdate><volume>19</volume><issue>1</issue><spage>47</spage><epage>59</epage><pages>47-59</pages><issn>1672-5182</issn><eissn>1993-5021</eissn><eissn>1672-5174</eissn><abstract>This study investigates strategies for solving the system reliability of large three-dimensional jacket structures. These structural systems normally fail as a result of a series of different components failures. The failure characteristics are investigated under various environmental conditions and direction combinations. The
β
-unzipping technique is adopted to determine critical failure components, and the entire system is simplified as a series-parallel system to approximately evaluate the structural system reliability. However, this approach needs excessive computational effort for searching failure components and failure paths. Based on a trained artificial neural network (ANN), which can be used to approximate the implicit limit-state function of a complicated structure, a new alternative procedure is proposed to improve the efficiency of the system reliability analysis method. The failure probability is calculated through Monte Carlo simulation (MCS) with Latin hypercube sampling (LHS). The features and applicability of the above procedure are discussed and compared using an example jacket platform located in Chengdao Oilfield, Bohai Sea, China. This study provides a reference for the evaluation of the system reliability of jacket structures.</abstract><cop>Heidelberg</cop><pub>Science Press</pub><doi>10.1007/s11802-020-4181-2</doi><tpages>13</tpages></addata></record> |
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| source | Springer Nature - Complete Springer Journals; Alma/SFX Local Collection |
| subjects | Artificial neural networks Component reliability Components Computer applications Computer simulation Earth and Environmental Science Earth Sciences Environmental conditions Failure Failure analysis Hypercubes Latin hypercube sampling Limit states Meteorology Monte Carlo simulation Neural networks Oceanography Offshore Offshore drilling rigs Oil and gas fields Oil field equipment Oil fields Probability theory Reliability Reliability analysis Reliability engineering Statistical methods Structural reliability System reliability |
| title | System Reliability Analysis of an Offshore Jacket Platform |
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