{"title":"基于有限元和神经网络的海上钢立管波浪载荷疲劳损伤模型预测——以尼日利亚Forcados offshore为例","authors":"Usen Inemesit, Jasper Ahamefula Agbakwuru","doi":"10.53430/ijeru.2023.4.1.0014","DOIUrl":null,"url":null,"abstract":"This study aims at providing a model prediction technique for the fatigue life of offshore steel risers using a hybrid of finite element analysis and the artificial neural network (FEA-ANN) model. A 200 days’ environmental load from Forcados sea state in West Africa offshore was used in training the FEA-ANN model to predict fatigue. The prediction result showed that the mean square error (MSE) was 0.3329 and the analysis from the regression was 0.9999. The result from the training showed a high performance and the regression analysis of the model was seen to be good.","PeriodicalId":423246,"journal":{"name":"International Journal of Engineering Research Updates","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Model prediction of fatigue damage on offshore steel risers due to wave loading using FEA and ANN: A case of Forcados Offshore, Nigeria\",\"authors\":\"Usen Inemesit, Jasper Ahamefula Agbakwuru\",\"doi\":\"10.53430/ijeru.2023.4.1.0014\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study aims at providing a model prediction technique for the fatigue life of offshore steel risers using a hybrid of finite element analysis and the artificial neural network (FEA-ANN) model. A 200 days’ environmental load from Forcados sea state in West Africa offshore was used in training the FEA-ANN model to predict fatigue. The prediction result showed that the mean square error (MSE) was 0.3329 and the analysis from the regression was 0.9999. The result from the training showed a high performance and the regression analysis of the model was seen to be good.\",\"PeriodicalId\":423246,\"journal\":{\"name\":\"International Journal of Engineering Research Updates\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-02-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Engineering Research Updates\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.53430/ijeru.2023.4.1.0014\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Engineering Research Updates","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.53430/ijeru.2023.4.1.0014","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Model prediction of fatigue damage on offshore steel risers due to wave loading using FEA and ANN: A case of Forcados Offshore, Nigeria
This study aims at providing a model prediction technique for the fatigue life of offshore steel risers using a hybrid of finite element analysis and the artificial neural network (FEA-ANN) model. A 200 days’ environmental load from Forcados sea state in West Africa offshore was used in training the FEA-ANN model to predict fatigue. The prediction result showed that the mean square error (MSE) was 0.3329 and the analysis from the regression was 0.9999. The result from the training showed a high performance and the regression analysis of the model was seen to be good.
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