Advanced Structural Health Monitoring Technology in Marine Engineering and Renewable Energy Systems
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A: Sustainable Energy".
Deadline for manuscript submissions: 25 August 2025 | Viewed by 280
Special Issue Editors
Interests: marine engineering; structural health monitoring; structural safety assessment; dynamic analysis; damage detection; system identification; signal processing; model updating; offshore platform; offshore wind turbine; marine risers; machine learning; deep learning
Interests: dynamic analysis of floating offshore wind turbine; structural health monitoring; modal analysis; structural damage identification
Interests: structural health monitoring and digital twin; virtual hybrid model experiment in ocean engineering; fluid–structure interaction of marine strctures; renewable energy systems; machine learning
Special Issue Information
Dear Colleagues,
Marine engineering structures and renewable energy systems—such as oil platforms, wind turbines with fixed or floating foundations, floating photovoltaic panels, wave energy converters, deep-sea risers, and submarine pipelines—are critical facilities for marine resource and energy exploitation. However, these structures are inevitably subjected to damage, structural aging, and functional deterioration due to their operation in harsh ocean environments and due to their exposure to various external loads throughout their service life.
Structural health monitoring (SHM) plays a crucial role in assessing the condition and performance of these structures by continuously monitoring their static and dynamic behavior using various sensors. By providing accurate and timely information on structural integrity, SHM helps ensure that these facilities meet the performance criteria for which they were originally designed.
Despite its advantages, SHM in marine engineering structures and renewable energy systems faces significant challenges, including time-varying environmental and operational conditions, strong measurement noise, and limited measurement data. These factors increase the complexity of data analysis and interpretation, necessitating the continuous development of new SHM methodologies. Advanced data acquisition and analysis techniques—such as machine learning-based approaches—have shown promising potential in addressing these challenges.
This Special Issue focuses on cutting-edge research in advanced SHM techniques for marine engineering structures and renewable energy systems. We particularly welcome novel applications of advanced data acquisition and analysis methods to address practical challenges. Additionally, original research on SHM-related equipment for monitoring the condition of marine structures will also be considered.
Topics of interest for publication include, but are not limited to, the following:
- Experimental and field tests of marine engineering structures and renewable energy systems;
- Quick and accurate structural response simulation and analysis;
- Machine and deep learning-aided condition assessment;
- Data-driven and physics-informed SHM methods;
- Surrogate models and digital twins;
- System and load identification techniques;
- Damage identification technique robust to various uncertainties;
- Uncertainty quantification in SHM;
- Value quantification and new function of SHM;
- New trends and challenges in machine learning-aided SHM;
- SHM-related data acquisition equipment.
Prof. Dr. Mingqiang Xu
Prof. Dr. Hongchao Lu
Dr. Haojie Ren
Guest Editors
Manuscript Submission Information
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Keywords
- structural health monitoring
- marine structure
- renewable energy system
- dynamic response analysis
- damage detection
- safety assessment
- system identification
- machine learning
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