Special Issue "Emerging Topics in Mechanical Vibrations Analysis: Optical Methods and Machine Learning Approaches"
Dear Colleague,
This Special Issue on "Emerging Topics in Mechanical Vibrations Analysis: Optical Methods and Machine Learning Approaches" aims to present cutting-edge research and developments in the analysis and management of vibrations within structural systems. We invite contributions that address both fundamental and applied aspects of structural dynamics, with a particular focus on (but not limited to) optical methods or machine learning approaches, including:
Experimental Approaches: New experimental methodologies for measuring and analyzing vibrations, including sensor technologies and data acquisition techniques, with a particular focus on optical methods.
Machine Learning Approaches: development of artificial intelligence tools and neural networks with direct application to vibration measurements, data post-processing, and vibration suppression.
Dynamic Modeling and Simulation: Advances in theoretical and computational models for predicting the dynamic behavior of structures, especially through machine learning.
Vibration Control Techniques: Innovative methods for controlling and mitigating vibrations, including passive, active, and hybrid systems.
Response Analysis: Studies on the dynamic response of structures to different types of loads, such as earthquakes, wind, and operational vibrations.
Structural Integrity and Safety: Implications of vibrations on the structural integrity and safety of mechanical and aerospace structures.
Case Studies: Practical applications and real-world case studies demonstrating the implementation of dynamic analysis and vibration control in engineering projects.
We encourage submissions that contribute to advancing both theoretical understanding and practical applications in structural dynamics and vibration analysis, offering new insights, methodologies, or technologies that can benefit a wide range of engineering disciplines by applying optical methods or machine learning approaches.
Dr. Paolo Neri
Dr. Marco Cococcioni
Prof. Dr. Daniele Botto
Dr. Christian Maria Firrone
Guest Editors