Advanced Structural Health Monitoring for Energy Performance and Safety in Marine Renewable Systems
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A: Sustainable Energy".
Deadline for manuscript submissions: 15 July 2026 | Viewed by 3323
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: research on dynamic characteristics of marine engineering structures; structural health monitoring of engineering structures; motion response analysis of floating wind turbine structures
Special Issues, Collections and Topics in MDPI journals
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 renewable systems—such as offshore wind turbines, floating photovoltaic platforms, wave energy converters, deep-sea risers, and submarine pipelines—play a vital role in the sustainable exploitation of ocean energy resources. Ensuring their operational safety and energy performance is essential for the long-term reliability and efficiency of marine energy infrastructure.
Structural health monitoring (SHM) offers a powerful solution for the real-time assessment of system integrity and performance by capturing critical data on structural behavior and external loads. Effective SHM not only helps detect damage and deterioration but also supports energy efficiency by enabling timely maintenance, optimized operation, and extended service life.
However, marine environments pose unique challenges for SHM, such as harsh environmental conditions, limited sensing coverage, and complex dynamic interactions. To address these, advanced data-driven techniques—especially those based on machine learning and hybrid physical–data models—are being increasingly adopted for accurate condition assessment and decision support.
This Special Issue invites contributions that explore innovative SHM methods for improving the energy performance, safety, and operational reliability of marine renewable systems. We particularly encourage research that integrates intelligent data analysis, monitoring technologies, and system-level insights to enhance energy utilization and structural resilience.
Topics of interest 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 techniques 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.
- Offshore operation optimization based on monitored data.
- Energy performance monitoring.
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
- energy performance monitoring
- condition-based maintenance
- digital twin
- lifetime energy output
- offshore operation optimization
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