Stochastic Structural Optimization
Seiten
2023
CRC Press (Verlag)
978-0-367-72039-1 (ISBN)
CRC Press (Verlag)
978-0-367-72039-1 (ISBN)
This comprehensive presentation of robust design optimization of structures combines structural optimization with reliability-based design and robust design through a unified framework of non-parametric stochastic methodologies, to design safe civil or mechanical structures which take account of uncertainty.
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.
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.
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.
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.
| Erscheinungsdatum | 10.08.2023 |
|---|---|
| Zusatzinfo | 48 Tables, black and white; 20 Line drawings, color; 61 Line drawings, black and white; 20 Illustrations, color; 61 Illustrations, black and white |
| Verlagsort | London |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Gewicht | 650 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik |
| Technik ► Bauwesen | |
| Technik ► Maschinenbau | |
| Technik ► Umwelttechnik / Biotechnologie | |
| ISBN-10 | 0-367-72039-6 / 0367720396 |
| ISBN-13 | 978-0-367-72039-1 / 9780367720391 |
| Zustand | Neuware |
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