Automated and explainable machine learning for accelerating nonlinear vibration prediction in bioinspired helicoidal laminated composite structures

Key Points

• Vibration prediction accuracy improves significantly, demonstrating the power of machine learning in assessing nonlinear behaviors.
• Key evidence shows a reduced prediction time by approximately 30% compared to traditional methods, enhancing efficiency.
• Assessment using advanced machine learning techniques provides robust insights into complex composite structures' behavior during vibrations.
• Supports the development of more effective designs in engineering applications, with implications for bioinspired structures and materials.