State of the art in structural health monitoring of offshore and marine structures

被引:53
|
作者
Pezeshki, Hadi [1 ]
Adeli, Hojjat [2 ]
Pavlou, Dimitrios [1 ]
Siriwardane, Sudath C. C. [1 ]
机构
[1] Univ Stavanger, Stavanger, Norway
[2] Ohio State Univ, Columbus, OH USA
关键词
Structural Health Monitoring; offshore structures; marine structures; Machine Learning; EMPIRICAL WAVELET TRANSFORM; FATIGUE DAMAGE; WIND TURBINE; NEURAL-NETWORK; GREENS-FUNCTION; SCOUR; IDENTIFICATION; INTEGRITY; MODEL; METHODOLOGY;
D O I
10.1680/jmaen.2022.027
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
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.
引用
收藏
页码:89 / 108
页数:20
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