Advanced Techniques in Health Monitoring of Composite Structures

Dear Colleagues,

Composite structures are susceptible to complex damage and failure modes during the manufacturing and service processes. Some typical defects of the composite structure include matrix cracking, fiber breakage, delamination, etc., which can deteriorate the integrity of the structure and cause catastrophic failures. The continuous monitoring of composite structure health conditions aids in identifying such damages early, and taking appropriate measures to prolong their service life. Advanced artificial intelligence techniques have been extensively integrated into health monitoring systems to enhance the performance of composite structures. A basic health monitoring process for composite structures coves data acquisition via sensing technologies, data-processing and analysis, and decision-making. This Special Issue aims to present recent advanced models, methods, and technologies related to the health monitoring of composite structures for structural safety and integrity. The topics of interest for the Special Issue include, but are not limited to, the following:

• Sensor selection and optimal placement in composite structures;
• Data acquisition from composite structures;
• Pre-processing of data collected from composite structures;
• Feature extraction for composite structures;
• Decision-making on the damage detection of composite structures;
• Health monitoring of composite structures;
• Machine learning/deep learning/transfer learning in composite damage detection;
• Physics-informed machine learning models for composite damage detection.

Dr. Soo-Ho Jo
Dr. Haichao An
Guest Editors

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• structural health monitoring
• composite structures
• artificial intelligence
• machine learning
• damage detection
• optimal sensor placement
• data processing