Engineering Risk C - by Siu-Kui Au & Yu Wang (Hardcover)

About the Book

"A unique book giving a comprehensive coverage of Subset Simulation - a robust tool for general applicationsThe book starts with the basic theory in uncertainty propagation using Monte Carlo methods and the generation of random variables and stochastic processes for some common distributions encountered in engineering applications. It then introduces a class of powerful simulation method called Markov Chain Monte Carlo method (MCMC), an important machinery behind Subset Simulation that allows one to generate samples for investigating rare scenarios in a probabilistically consistent manner. The theory of Subset Simulation is then presented, addressing related practical issues encountered in the actual implementation. A number of variants of Subset Simulation that can lead to improved performance for specific classes of problems will also be covered. The second half the book introduces the reader to probabilistic failure analysis and reliability-based design, which are laid out in a context that can be efficiently tackled within the context of Subset Simulation or Monte Carlo simulation in general. The result is a general framework that allows the practitioner to investigate reliability sensitivity to uncertain parameters and to explore possible design scenarios systematically for selection of the final design in a convenient but computationally efficient manner via simulation.A unique feature of this book is that it is complemented with a VBA (Visual Basic for Applications) that implements Subset Simulation in the Excel spreadsheet environment. This allows the reader to experiment with the examples in the book and get hands-on experience with simulation. A chapter is devoted to the software framework that allows a practical solution by resolving the risk assessment problem into three uncoupled procedures, namely, deterministic modeling, uncertainty modeling and uncertainty propagation. Presents a powerful simulation method called Subset Simulation for efficient engineering risk assessment and reliability-based design Illustrates application examples with MS Excel spreadsheets allowing readers to gain hands-on experience with simulation techniques Covers theoretical fundamentals as well as advanced implementation issues in practical engineering problems A companion website is available to include the developments of the software ideas "--

Book Synopsis

This book starts with the basic ideas in uncertainty propagation using Monte Carlo methods and the generation of random variables and stochastic processes for some common distributions encountered in engineering applications. It then introduces a class of powerful simulation techniques called Markov Chain Monte Carlo method (MCMC), an important machinery behind Subset Simulation that allows one to generate samples for investigating rare scenarios in a probabilistically consistent manner. The theory of Subset Simulation is then presented, addressing related practical issues encountered in the actual implementation. The book also introduces the reader to probabilistic failure analysis and reliability-based sensitivity analysis, which are laid out in a context that can be efficiently tackled with Subset Simulation or Monte Carlo simulation in general. The book is supplemented with an Excel VBA code that provides a user-friendly tool for the reader to gain hands-on experience with Monte Carlo simulation.

• Presents a powerful simulation method called Subset Simulation for efficient engineering risk assessment and failure and sensitivity analysis
• Illustrates examples with MS Excel spreadsheets, allowing readers to gain hands-on experience with Monte Carlo simulation
• Covers theoretical fundamentals as well as advanced implementation issues
• A companion website is available to include the developments of the software ideas

This book is essential reading for graduate students, researchers and engineers interested in applying Monte Carlo methods for risk assessment and reliability based design in various fields such as civil engineering, mechanical engineering, aerospace engineering, electrical engineering and nuclear engineering. Project managers, risk managers and financial engineers dealing with uncertainty effects may also find it useful.

From the Back Cover

Engineering Risk Assessment with Subset Simulation

This book starts with the basic ideas in uncertainty propagation using Monte Carlo methods and the generation of random variables and stochastic processes for some common distributions encountered in engineering applications. It then introduces a class of powerful simulation techniques called Markov Chain Monte Carlo method (MCMC), an important machinery behind Subset Simulation that allows one to generate samples for investigating rare scenarios in a probabilistically consistent manner. The theory of Subset Simulation is then presented, addressing related practical issues encountered in the actual implementation. The book also introduces the reader to probabilistic failure analysis and reliability-based sensitivity analysis, which are laid out in a context that can be ef ciently tackled with Subset Simulation or Monte Carlo simulation in general. The book is supplemented with an Excel VBA code that provides a user-friendly tool for the reader to gain hands-on experience with Monte Carlo simulation.

• Presents a powerful simulation method called Subset Simulation for ef cient engineering risk assessment and failure and sensitivity analysis
• Illustrates examples with MS Excel spreadsheets, allowing readers to gain hands-on experience with Monte Carlo simulation
• Covers theoretical fundamentals as well as advanced implementation issues
• Developments of the software ideas in the book available from the author

This book is essential reading for graduate students, researchers and engineers interested in applying Monte Carlo methods for risk assessment and reliability based design in various fields such as civil engineering, mechanical engineering, aerospace engineering, electrical engineering and nuclear engineering. Project managers, risk managers and financial engineers dealing with uncertainty effects may also find it useful.

About the Author

Siu-Kui Au, University of Liverpool, UK

Yu Wang, City University of Hong Kong, China