Tao Zhang , Selda Oterkus , Erkan Oterkus , Xueliang Wang , Fang Wang , Song Shiqian
{"title":"基于模拟和数据增强的海上平台连接器剪力反演方法","authors":"Tao Zhang , Selda Oterkus , Erkan Oterkus , Xueliang Wang , Fang Wang , Song Shiqian","doi":"10.1016/j.marstruc.2024.103577","DOIUrl":null,"url":null,"abstract":"<div><p><span>This study introduces a Simulation-Based and Data-Augmented method for shear force inversion to address the challenge of directly measuring shear force on connector pins in multi-module floating platforms. Stress sensors are strategically placed in adjacent areas. Extensive Finite Element simulation scenarios lead to the identification of optimal features sensitive to both force magnitude and direction. Subsequently, an </span>Artificial Neural Network (ANN) is developed to distill the simulation data into characteristic sensor responses. Fine-tuning with physical measurements further enhances shear force inversion accuracy. Using simulated and experimental data, the method demonstrates a shear force inversion error below 3.2 % and an angular inversion error under 1.4 % across test conditions. This methodology provides essential load data for connector safety assessments and crucial guidelines for the assembly of multi-module floating platforms.</p></div>","PeriodicalId":49879,"journal":{"name":"Marine Structures","volume":"95 ","pages":"Article 103577"},"PeriodicalIF":4.0000,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A simulation-based and data-augmented shear force inversion method for offshore platform connector\",\"authors\":\"Tao Zhang , Selda Oterkus , Erkan Oterkus , Xueliang Wang , Fang Wang , Song Shiqian\",\"doi\":\"10.1016/j.marstruc.2024.103577\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p><span>This study introduces a Simulation-Based and Data-Augmented method for shear force inversion to address the challenge of directly measuring shear force on connector pins in multi-module floating platforms. Stress sensors are strategically placed in adjacent areas. Extensive Finite Element simulation scenarios lead to the identification of optimal features sensitive to both force magnitude and direction. Subsequently, an </span>Artificial Neural Network (ANN) is developed to distill the simulation data into characteristic sensor responses. Fine-tuning with physical measurements further enhances shear force inversion accuracy. Using simulated and experimental data, the method demonstrates a shear force inversion error below 3.2 % and an angular inversion error under 1.4 % across test conditions. This methodology provides essential load data for connector safety assessments and crucial guidelines for the assembly of multi-module floating platforms.</p></div>\",\"PeriodicalId\":49879,\"journal\":{\"name\":\"Marine Structures\",\"volume\":\"95 \",\"pages\":\"Article 103577\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2024-01-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Marine Structures\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0951833924000054\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Marine Structures","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0951833924000054","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
A simulation-based and data-augmented shear force inversion method for offshore platform connector
This study introduces a Simulation-Based and Data-Augmented method for shear force inversion to address the challenge of directly measuring shear force on connector pins in multi-module floating platforms. Stress sensors are strategically placed in adjacent areas. Extensive Finite Element simulation scenarios lead to the identification of optimal features sensitive to both force magnitude and direction. Subsequently, an Artificial Neural Network (ANN) is developed to distill the simulation data into characteristic sensor responses. Fine-tuning with physical measurements further enhances shear force inversion accuracy. Using simulated and experimental data, the method demonstrates a shear force inversion error below 3.2 % and an angular inversion error under 1.4 % across test conditions. This methodology provides essential load data for connector safety assessments and crucial guidelines for the assembly of multi-module floating platforms.
期刊介绍:
This journal aims to provide a medium for presentation and discussion of the latest developments in research, design, fabrication and in-service experience relating to marine structures, i.e., all structures of steel, concrete, light alloy or composite construction having an interface with the sea, including ships, fixed and mobile offshore platforms, submarine and submersibles, pipelines, subsea systems for shallow and deep ocean operations and coastal structures such as piers.