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Reliability-Based Structural Design


As modern structures require more critical and complex designs, the need for accurate approaches to assess uncertainties in loads, geometry, material properties, manufacturing processes and operational environments has increased significantly. Reliability assessment techniques help to develop safe designs and identify where significant contributors of uncertainty occur in structural systems, or, where further research, testing and quality control could increase the safety and efficiency of the structure.

Reliability-based Structural Design provides readers with an understanding of the fundamentals and applications of structural reliability, stochastic finite element method, reliability analysis via stochastic expansion, and optimization under uncertainty. Probability theory, statistic methods, and reliability analysis methods including Monte Carlo Sampling, Latin hypercube sampling, first and second-Order reliability methods, stochastic finite element method, and stochastic optimization are discussed. In addition, the use of stochastic expansions, including polynomial chaos expansion and Karhunen-Loeve expansion, for the reliability analysis of practical engineering problems is also examined. Detailed examples of practical engineering applications including an uninhabited joined-wing aircraft and a supercavitating torpedo are presented to illustrate the effectiveness of these methods.

Reliability-based Structural Design will be a valuable reference for graduate and post graduate students studying structural reliability, probabilistic analysis and optimization under uncertainty; as well as engineers, researchers, and technical managers who are concerned with theoretical fundamentals, computational implementations and applications for probabilistic analysis and design.

Product Details

Table of Contents

Preliminaries.- Probabilistic Analysis.- Methods of Structural Reliability.- Reliability-based Structural Optimization.- Stochastic Expansion for Probabilistic Analysis.- Probabilistic Analysis Examples via Stochastic Expansion.- Summary.

About the Author

Dr Seung-Kyum Choi earned his PhD in mechanical and materials engineering at Wright State University, OH, USA. His research interests include structural reliability and probabilistic mechanics, statistical approaches to design of mechanical systems, and multidisciplinary design optimization. Dr Ramana Grandhi is the distinguished professor of mechanical and materials engineering at Wright State University, OH, USA. His research interests are in multidisciplinary analysis and optimization, probabilistic mechanics, and metal forming. Dr Grandhi has conducted sponsored research for the US Air Force, US Navy, NSF, NASA, DARPA, GE, GM and Caterpillar. He is a fellow of the ASME and an associate fellow of the AIAA. Dr Robert A. Canfield is an associate professor of aerospace engineering in the Department of Aeronautics and Astronautics at the Air Force Institute of Technology (AFIT), OH. USA. His research interests include structural optimization, multidisciplinary analysis and design methods, uncertainty quantification, structural dynamics and control, and aeroelasticity. He retired as a Lieutenant Colonel in the US Air Force, where he was the project engineer for the Automated Structural Optimization System (ASTROS), an AFIT instructor, the program manager for basic research in computational mathematics, the chief of plans and budget, and then the director of policy and integration at the Air Force Office of Scientific Research. He is an Associate Fellow of the AIAA, and he chaired the AIAA Multidisciplinary Design Optimization (MDO) Technical Committee for two years.

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