Buch, Englisch, 266 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 412 g
Buch, Englisch, 266 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 412 g
ISBN: 978-0-367-72040-7
Verlag: CRC Press
Stochastic Structural Optimization presents a comprehensive picture of robust design optimization of structures, focused on nonparametric stochastic-based methodologies. Good practical structural design accounts for uncertainty, for which reliability-based design offers a standard approach, usually incorporating assumptions on probability functions which are often unknown. By comparison, a worst-case approach with bounded support used as a robust design offers simplicity and a lower level of sensitivity. Linking structural optimization with these two approaches by a unified framework of non-parametric stochastic methodologies provides a rigorous theoretical background and high level of practicality. This text shows how to use this theoretical framework in civil and mechanical engineering practice to design a safe structure which accounts for uncertainty.
- Connects theory with practice in the robust design optimization of structures
- Advanced enough to support sound practical designs
This book provides comprehensive coverage for engineers and graduate students in civil and mechanical engineering.
Makoto Yamakawa is a Professor at Tokyo University of Science, and a member of the Advisory Board of the 2020 Asian Congress of Structural and Multidisciplinary Optimization.
Makoto Ohsaki is a Professor at Kyoto University, Japan, treasurer of the International Association for Shell & Spatial Structures and former President of the Asian Society for Structural and Multidisciplinary Optimization.
Zielgruppe
Postgraduate and Professional
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
1. Basic concepts and examples. 2. Stochastic optimization. 3. Random search-based optimization. 4. Order statistics-based robust design optimization. 5. Robust geometry and topology optimization. 6. Multi-objective robust optimization approach. 7. Surrogate-assisted and reliability-based optimization. Appendix.
