Advanced Structural Health Monitoring for Energy Performance and Safety in Marine Renewable Systems

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

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