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<p>This study comprehensively reviews advanced damage detection techniques for composite structures. Composites, valued for their exceptional mechanical properties, are widely used across engineering disciplines but are prone to defects such as delamination, fiber breakage, and matrix cracking due to their heterogeneous nature. This review explores non-destructive damage assessment methods, metaheuristic optimization techniques, machine learning approaches, and advanced signal processing algorithms applied to SHM of composite structures, covering their principles, recent innovations, and practical implementations. Key insights indicate significant progress in integrating sensing, signal processing, and computational methods, with hybrid models showing improved robustness in noisy environments; however, challenges persist in real-time monitoring, data scarcity, environmental variability, and large-scale deployment. Future SHM development should focus on embedded sensor networks integrated with edge computing and real-time data processing for continuous in-situ monitoring, while adaptive hybrid models combining signal processing and machine learning can improve robustness. Scalable, cloud-based SHM frameworks incorporating real-time localization, severity classification, and predictive maintenance will be essential for autonomous, intelligent SHM systems that ensure early detection, reduce maintenance costs, and enhance structural safety in high-performance composite applications.</p>

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Applications of Structural Health Monitoring Approach for Defect Detection in Composite Structure: A Review

  • 期刊论文

  • 发表日期：

    2025年10月6日

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  • Omer N. Saleh,
  • Alaa Abdulhady Jaber,
  • Rasha Mohammed Hussein

摘要

This study comprehensively reviews advanced damage detection techniques for composite structures. Composites, valued for their exceptional mechanical properties, are widely used across engineering disciplines but are prone to defects such as delamination, fiber breakage, and matrix cracking due to their heterogeneous nature. This review explores non-destructive damage assessment methods, metaheuristic optimization techniques, machine learning approaches, and advanced signal processing algorithms applied to SHM of composite structures, covering their principles, recent innovations, and practical implementations. Key insights indicate significant progress in integrating sensing, signal processing, and computational methods, with hybrid models showing improved robustness in noisy environments; however, challenges persist in real-time monitoring, data scarcity, environmental variability, and large-scale deployment. Future SHM development should focus on embedded sensor networks integrated with edge computing and real-time data processing for continuous in-situ monitoring, while adaptive hybrid models combining signal processing and machine learning can improve robustness. Scalable, cloud-based SHM frameworks incorporating real-time localization, severity classification, and predictive maintenance will be essential for autonomous, intelligent SHM systems that ensure early detection, reduce maintenance costs, and enhance structural safety in high-performance composite applications.