{"title":"近海和海洋结构健康监测技术现状","authors":"H. Pezeshki, H. Adeli, D. Pavlou, S. Siriwardane","doi":"10.1680/jmaen.2022.027","DOIUrl":null,"url":null,"abstract":"The present paper deals with state of the art in Structural Health Monitoring (SHM) methods in offshore and marine structures. Most of the SHM methods have been developed for onshore infrastructures. Few works are available to implement SHM technologies in offshore and marine structures. This paper aims to fill this gap and highlight the challenges in implementing SHM methods in offshore and marine structures. The present work categorizes the available techniques for establishing SHM models in oil rigs, offshore wind turbine structures, subsea systems, vessels, pipelines etc. Besides, the capabilities of the proposed ideas in the recent publications are classified into three main categories: a) the Model-Based, b) the Vibration-Based, and c) the Digital Twin methods. Recently developed novel signal processing and machine learning algorithms have been reviewed, and their abilities have been discussed. Developed methods in Vision-Based and Population-Based approaches have also been presented and discussed. The present paper aims to provide a guideline for selecting and establishing SHM in offshore and marine structures.","PeriodicalId":54575,"journal":{"name":"Proceedings of the Institution of Civil Engineers-Maritime Engineering","volume":"85 1","pages":""},"PeriodicalIF":1.0000,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"State of the art in structural health monitoring of offshore and marine structures\",\"authors\":\"H. Pezeshki, H. Adeli, D. Pavlou, S. Siriwardane\",\"doi\":\"10.1680/jmaen.2022.027\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The present paper deals with state of the art in Structural Health Monitoring (SHM) methods in offshore and marine structures. Most of the SHM methods have been developed for onshore infrastructures. Few works are available to implement SHM technologies in offshore and marine structures. This paper aims to fill this gap and highlight the challenges in implementing SHM methods in offshore and marine structures. The present work categorizes the available techniques for establishing SHM models in oil rigs, offshore wind turbine structures, subsea systems, vessels, pipelines etc. Besides, the capabilities of the proposed ideas in the recent publications are classified into three main categories: a) the Model-Based, b) the Vibration-Based, and c) the Digital Twin methods. Recently developed novel signal processing and machine learning algorithms have been reviewed, and their abilities have been discussed. Developed methods in Vision-Based and Population-Based approaches have also been presented and discussed. The present paper aims to provide a guideline for selecting and establishing SHM in offshore and marine structures.\",\"PeriodicalId\":54575,\"journal\":{\"name\":\"Proceedings of the Institution of Civil Engineers-Maritime Engineering\",\"volume\":\"85 1\",\"pages\":\"\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2023-01-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Institution of Civil Engineers-Maritime Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1680/jmaen.2022.027\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Institution of Civil Engineers-Maritime Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1680/jmaen.2022.027","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
State of the art in structural health monitoring of offshore and marine structures
The present paper deals with state of the art in Structural Health Monitoring (SHM) methods in offshore and marine structures. Most of the SHM methods have been developed for onshore infrastructures. Few works are available to implement SHM technologies in offshore and marine structures. This paper aims to fill this gap and highlight the challenges in implementing SHM methods in offshore and marine structures. The present work categorizes the available techniques for establishing SHM models in oil rigs, offshore wind turbine structures, subsea systems, vessels, pipelines etc. Besides, the capabilities of the proposed ideas in the recent publications are classified into three main categories: a) the Model-Based, b) the Vibration-Based, and c) the Digital Twin methods. Recently developed novel signal processing and machine learning algorithms have been reviewed, and their abilities have been discussed. Developed methods in Vision-Based and Population-Based approaches have also been presented and discussed. The present paper aims to provide a guideline for selecting and establishing SHM in offshore and marine structures.
期刊介绍:
Maritime Engineering publishes technical papers relevant to civil engineering in port, estuarine, coastal and offshore environments.
Relevant to consulting, client and contracting engineers as well as researchers and academics, the journal focuses on safe and sustainable engineering in the salt-water environment and comprises papers regarding management, planning, design, analysis, construction, operation, maintenance and applied research. The journal publishes papers and articles from industry and academia that conveys advanced research that those developing, designing or constructing schemes can begin to apply, as well as papers on good practices that others can learn from and utilise.