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.