Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization

Area of Science:

• Engineering
• Computational Mechanics
• Materials Science

Background:

• Accurate mechanical identification of materials and structures is crucial for engineering applications.
• Solving inverse problems for material characterization often involves complex optimization techniques.
• Existing metaheuristic algorithms can be computationally intensive and may lack robustness.

Purpose of the Study:

• To develop a hybrid framework for mechanical identification of materials and structures.
• To enhance metaheuristic algorithms for solving inverse problems more efficiently.
• To improve the robustness and reduce the computational cost of material property identification.

Main Methods:

• Combining experimental optical measurements with non-linear optimization.
• Developing advanced formulations of Simulated Annealing (SA), Harmony Search (HS), and Big Bang-Big Crunch (BBBC).
• Implementing hybrid algorithms (HFSA, HFHS, HFBBBC) with gradient information, line search, and probabilistic search strategies.

Main Results:

• The proposed hybrid algorithms significantly reduce the number of structural analyses required.
• The enhanced metaheuristic search engines demonstrate improved robustness.
• Successful application to inverse problems for composite and hyperelastic materials with up to 17 unknown properties.

Conclusions:

• The hybrid framework provides a valid and efficient approach for mechanical identification.
• The developed algorithms offer a computationally cheaper and more robust solution for material characterization.
• This method advances the field of inverse problem-solving in materials science and structural analysis.