BI Aorui, Luo Zhengshan, Song Yingying, Z. Xinsheng
{"title":"基于FOA-GRNN模型的海底内腐蚀管道残余强度分析","authors":"BI Aorui, Luo Zhengshan, Song Yingying, Z. Xinsheng","doi":"10.16265/J.CNKI.ISSN1003-3033.2020.06.012","DOIUrl":null,"url":null,"abstract":"In order to explore residual strength of submarine pipelines with internal corrosionꎬ and provide reference for maintenance so as to ensure safe operationꎬ a FOA ̄GRNN calculation method of residual strength was proposed and a prediction model was constructed by using GRNN based on influencing factors like wall thicknessꎬ diameterꎬ corrosion depthꎬ lengthꎬ width and ultimate tensile strength. Thenꎬ FOA was used to optimize the modelꎬ and negative influence of smooth factors were set artificially. Influencing factors and residual strength database were simulated and generated by finite element methodꎬ and trained and predicted through FOA ̄GRNN model. Finallyꎬ with experimental data of pipeline ultimate strength blasting from PETROBRAS Research Institute as an exampleꎬ the prediction model was verified. The results show that average relative error of FOA ̄GRNN model is 16 53% for residual strength prediction of finite element simulation dataꎬ and 7 81% for experimental data predictionꎬ 第 6 期 毕傲睿等: 内腐蚀海底管道剩余强度的 FOA ̄GRNN 模型 which are reasonable and accurate.","PeriodicalId":9976,"journal":{"name":"中国安全科学学报","volume":"50 1","pages":"78"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Residual strength analysis of internally corroded submarine pipeline based on FOA-GRNN model\",\"authors\":\"BI Aorui, Luo Zhengshan, Song Yingying, Z. Xinsheng\",\"doi\":\"10.16265/J.CNKI.ISSN1003-3033.2020.06.012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to explore residual strength of submarine pipelines with internal corrosionꎬ and provide reference for maintenance so as to ensure safe operationꎬ a FOA ̄GRNN calculation method of residual strength was proposed and a prediction model was constructed by using GRNN based on influencing factors like wall thicknessꎬ diameterꎬ corrosion depthꎬ lengthꎬ width and ultimate tensile strength. Thenꎬ FOA was used to optimize the modelꎬ and negative influence of smooth factors were set artificially. Influencing factors and residual strength database were simulated and generated by finite element methodꎬ and trained and predicted through FOA ̄GRNN model. Finallyꎬ with experimental data of pipeline ultimate strength blasting from PETROBRAS Research Institute as an exampleꎬ the prediction model was verified. The results show that average relative error of FOA ̄GRNN model is 16 53% for residual strength prediction of finite element simulation dataꎬ and 7 81% for experimental data predictionꎬ 第 6 期 毕傲睿等: 内腐蚀海底管道剩余强度的 FOA ̄GRNN 模型 which are reasonable and accurate.\",\"PeriodicalId\":9976,\"journal\":{\"name\":\"中国安全科学学报\",\"volume\":\"50 1\",\"pages\":\"78\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"中国安全科学学报\",\"FirstCategoryId\":\"1089\",\"ListUrlMain\":\"https://doi.org/10.16265/J.CNKI.ISSN1003-3033.2020.06.012\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"中国安全科学学报","FirstCategoryId":"1089","ListUrlMain":"https://doi.org/10.16265/J.CNKI.ISSN1003-3033.2020.06.012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Residual strength analysis of internally corroded submarine pipeline based on FOA-GRNN model
In order to explore residual strength of submarine pipelines with internal corrosionꎬ and provide reference for maintenance so as to ensure safe operationꎬ a FOA ̄GRNN calculation method of residual strength was proposed and a prediction model was constructed by using GRNN based on influencing factors like wall thicknessꎬ diameterꎬ corrosion depthꎬ lengthꎬ width and ultimate tensile strength. Thenꎬ FOA was used to optimize the modelꎬ and negative influence of smooth factors were set artificially. Influencing factors and residual strength database were simulated and generated by finite element methodꎬ and trained and predicted through FOA ̄GRNN model. Finallyꎬ with experimental data of pipeline ultimate strength blasting from PETROBRAS Research Institute as an exampleꎬ the prediction model was verified. The results show that average relative error of FOA ̄GRNN model is 16 53% for residual strength prediction of finite element simulation dataꎬ and 7 81% for experimental data predictionꎬ 第 6 期 毕傲睿等: 内腐蚀海底管道剩余强度的 FOA ̄GRNN 模型 which are reasonable and accurate.
期刊介绍:
China Safety Science Journal is administered by China Association for Science and Technology and sponsored by China Occupational Safety and Health Association (formerly China Society of Science and Technology for Labor Protection). It was first published on January 20, 1991 and was approved for public distribution at home and abroad.
China Safety Science Journal (CN 11-2865/X ISSN 1003-3033 CODEN ZAKXAM) is a monthly magazine, 12 issues a year, large 16 folo, the domestic price of each book is 40.00 yuan, the annual price is 480.00 yuan. Mailing code 82-454.
Honors:
Scopus database includes journals in the field of safety science of high-quality scientific journals classification catalog T1 level
National Chinese core journals China Science and technology core journals CSCD journals
The United States "Chemical Abstracts" search included the United States "Cambridge Scientific Abstracts: Materials Information" search included