Machine Learning Applications in Thin-Walled Structural Engineering

Dr A. Praveen Kumar is an Assistant Professor at the Department of Mechanical Engineering, Easwari Engineering College, India. He completed his Ph.D. degree in the area of crashworthiness of thin-walled structures. His major areas of research interest are 3D printing of composite parts, metal forming simulation, additive manufacturing, composite materials and structures. He is ranked among the top 2% of researchers globally in the fields of "Materials" and "Mechanical Engineering & Transports" Released by Stanford University and Elsevier in 2024. Dr Kumar has published 97 research papers and is currently a Editorial Board Member in reputed journals like Discover Materials (Springer) and International Journal of Protective Structures (Sage). Dr Quanjin Ma is a Postdoctoral Researcher at the Institute of Advanced Materials and Technology, Guangdong University of Technology, China. His research includes: 3D-printed lightweight structures, composite sandwich structures, energy-absorbing characteristics, filament-wound composite structures, impact failure behaviour and mechanism and 3D-printedcelectromagnetic absorbing structures. He is ranked among the top 2% of researchers globally in the fields of "Materials" and "Mechanical Engineering & Transports" Released by Stanford University and Elsevier in 2024. Dr. Afdhal is an Assistant Professor within the Solid Mechanics and Lightweight Structures research group at the Faculty of Mechanical and Aerospace Engineering, Institut Teknologi Nasional Bandung, Indonesia. He earned his PhD with a dissertation focused on the development of a constitutive material model that integrates the effects of anisotropy and viscoplasticity. Subsequently, during his postdoctoral fellowship at the Department of Mechanics and Materials, Czech Technical University in Prague, he conducted research on the dynamic behavior of auxetic structures. Leveraging this expertise, he designed auxetic structures fabricated through additive manufacturing, augmented by machine learning techniques. His current research interests encompass material modeling and simulation, viscoplasticity, auxetic structure design, additive manufacturing, and the application of machine learning to the discovery and design of advanced materials and structures. Dr. Akbar has been extensively engaged in both national and international research initiatives.

• 1. An Introduction to Thin-walled Structures and the Transformative Role of Machine Learning in Structural Engineering2. Advanced Machine Learning Techniques for Structural Optimization of Thin-walled Components: Strategies for Enhanced Performance3. Machine Learning Algorithms for Predicting Failure Modes in Thin-walled Structures: Techniques and Applications4. Innovative Algorithms for Efficient Design Space Exploration and Case Studies in Thin-walled Structures5. Advancements in Machine Learning for Material Design and Structural Optimization for Crashworthiness6. Artificial Intelligence in the Design Process of Thin-walled Structures: Automating Design Choices through Machine Learning Models7. Exploring Future Trends in Machine Learning for Thin-walled Structures8. Comparative Study of Supervised and Unsupervised Learning Methods for Thin-walled Structure Applications: Benefits and Limitations9. Hybrid Modeling Approaches: Combining Machine Learning with Traditional Analysis Methods for Thin-walled Structures10. Case Studies of Machine Learning Applications in the Analysis and Design of Thin-walled Structures11. Artificial Intelligence for Lightweight Structures for Crashworthiness Applications: Overview, Case studies, and Future Potentials12. Integrating Sustainability into Design and Data Management of Thin-walled Structures through Machine Learning Approaches13. Using Deep Learning for Image Recognition in Structural Inspections of Thin-walled Components: Innovations in Visual Analysis14. Data Preparation and Preprocessing for Machine Learning in Structural Engineering