Experimental Techniques and Artificial Intelligence for the Structural Health Monitoring of Composite Materials

Dear Colleagues,

Structural health monitoring of composite materials is a demanding task of great importance for the quality assessment and operational safety of manufactured goods. The need for improved time- and cost-effective solutions steers the scientific community towards two main drivers:

• The study of new experimental principles, technologies, testing procedures, and setup integrations for the measurement-based evaluation of bulk materials or complex structures, either to monitor/evaluate their production processes or to follow and check their structural integrity under working conditions;
• The development of new algorithms driven by artificial intelligence (machine learning, pattern recognition, representative learning, deep learning, etc.) for processing complex data to assess structural health.

This Special Issue invites original submissions addressing the structural health monitoring of composite materials through experimental techniques aimed at the autonomous detection and characterization of possible anomalies. Papers integrating different disciplines such as engineering, physics, mathematics, and computer science to produce consistent results of experimental evidence are particularly encouraged. Studies carried out in cooperation with enterprises or displaying the results of national and international projects having the goal of improving the sustainability of composite productions are welcome.

Dr. Roberto Marani
Dr. Davide Palumbo
Dr. Giuseppe Pitarresi
Guest Editors

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