Probabilistic Risk Analysis

A graduate level textbook on probabilistic risk analysis, aimed at statisticians, operations researchers and engineers.

Probabilistic risk and hazard assessments are applied to a wide range of engineering systems, mainly for regulatory reasons needed for development consent, system certification and occupational health and safety issues. The purpose of this book is to raise awareness of the limitations, uncertainties and other issues inherent in probabilistic risk analysis procedures. Probabilistic Risk Assessment of Engineering Systems describes: the importance of probabilistic risk assessment in decision making, i.e. risk management; types of risk and probabilistic risk analysis procedures; data needed for the conduct of probabilistic risk analysis; and acceptable/tolerable risk and other risk acceptance criteria. In essence, the book provides a multi-disciplinary and integrated explanation of risk assessment procedures that will enable the non-specialist reader to gain valuable insights into the development of risk analysis procedures. Practising engineers and graduate engineering students across a range of disciplines will find this book immensely useful.

This text presents notions and ideas at the foundations of a statistical treatment of risks. The focus is on statistical applications within the field of engineering risk and safety analysis. Coverage includes Bayesian methods. Such knowledge facilitates the understanding of the influence of random phenomena and gives a deeper understanding of the role of probability in risk analysis. The text is written for students who have studied elementary undergraduate courses in engineering mathematics, perhaps including a minor course in statistics. This book differs from typical textbooks in its verbal approach to many explanations and examples.

Based on the author's 20 years of teaching, Risk Analysis in Engineering: Techniques, Tools, and Trends presents an engineering approach to probabilistic risk analysis (PRA). It emphasizes methods for comprehensive PRA studies, including techniques for risk management. The author assumes little or no prior knowledge of risk analysis on the p

This book proposes a new approach to dynamic and online risk assessment of automated and autonomous marine systems, taking into account different environmental and operational conditions. The book presents lessons learnt from dynamic positioning incidents and accidents, and discusses the challenges of risk assessment of complex systems. The book begins by introducing dynamic and online risk assessment, before presenting automated and autonomous marine systems, as well as numerous dynamic positioning incidents. It then discusses human interactions with technology and explores how to quantify human error. Dynamic probabilistic risk assessment and online risk assessment are both considered fully, including case studies with the application of assisting operators in decision making in emergency situations. Finally, areas for future research are suggested. This practical volume offers tools and methodologies to help operators make better decisions and improve the safety of automated and autonomous marine systems. It provides a guideline for researchers and practitioners to perform dynamic probabilistic and online risk assessment, which also should be applicable to other complex systems outside the marine and maritime domain, such as nuclear power plants, chemical processes, autonomous transport systems, and space shuttles.

Bayesian Inference for Probabilistic Risk Assessment provides a Bayesian foundation for framing probabilistic problems and performing inference on these problems. Inference in the book employs a modern computational approach known as Markov chain Monte Carlo (MCMC). The MCMC approach may be implemented using custom-written routines or existing general purpose commercial or open-source software. This book uses an open-source program called OpenBUGS (commonly referred to as WinBUGS) to solve the inference problems that are described. A powerful feature of OpenBUGS is its automatic selection of an appropriate MCMC sampling scheme for a given problem. The authors provide analysis “building blocks” that can be modified, combined, or used as-is to solve a variety of challenging problems. The MCMC approach used is implemented via textual scripts similar to a macro-type programming language. Accompanying most scripts is a graphical Bayesian network illustrating the elements of the script and the overall inference problem being solved. Bayesian Inference for Probabilistic Risk Assessment also covers the important topics of MCMC convergence and Bayesian model checking. Bayesian Inference for Probabilistic Risk Assessment is aimed at scientists and engineers who perform or review risk analyses. It provides an analytical structure for combining data and information from various sources to generate estimates of the parameters of uncertainty distributions used in risk and reliability models.

Electrical Engineering Probabilistic Risk Assessment and Management for Engineers and Scientists Second Edition "State of the art in risk analysis...[this book] projects the technology into the next decade. Congratulations to the authors on a virtuoso performance." -Charles Donaghey, University of Houston "A very useful reference to the academic and government communities, and junior engineering staff within nuclear, chemical, transportation, aerospace, and other industries." -Yovan Lukic, Arizona Public Service Company As the demands of government agencies and insurance companies escalate, societal risk assessment and management become increasingly critical to the development and use of engineered systems in the full range of industrial installations. Packed with real-world examples and practical mathematical and statistical methods for large, complex systems, this definitive text and sourcebook gives you the guidance you need for thorough and conclusive study. You'll find new and updated coverage of all the key topics related to risk analysis: * Probabilistic nature of risk * Qualitative and quantitative risk assessments * System decomposition * Legal and regulatory risks * And much more! The authors also provide end-of-chapter problems and a course outline. Complete with a new, automated, fault tree synthesis method using semantic networks. Probabilistic Risk Assessment and Management for Engineers and Scientists, Second Edition will be of value to anyone working with engineered systems. Also of Interest from IEEE Press... Successful Patents and Patenting for Engineers and Scientists edited by Michael A. Lechter, Esq. 1995 Softcover 432 pp IEEE Order No. PP4478 ISBN 0-7803-1086-1 Metric Units and Conversion Charts A Metrication Handbook for Engineers, Technologists, and Scientists Second Edition Theodore Wildi 1995 Softcover 144 pp IEEE Order No. PP4044 ISBN 0-7803-1050-0 The Probability Tutoring Book An Intuitive Course for Engineers and Scientists (And Everyone Else!) Carol Ash 1993 Softcover 480 pp IEEE Order No. PP2881 ISBN 0-7803-1051-9

Explores methods for the representation and treatment of uncertainty in risk assessment In providing guidance for practical decision-making situations concerning high-consequence technologies (e.g., nuclear, oil and gas, transport, etc.), the theories and methods studied in Uncertainty in Risk Assessment have wide-ranging applications from engineering and medicine to environmental impacts and natural disasters, security, and financial risk management. The main focus, however, is on engineering applications. While requiring some fundamental background in risk assessment, as well as a basic knowledge of probability theory and statistics, Uncertainty in Risk Assessment can be read profitably by a broad audience of professionals in the field, including researchers and graduate students on courses within risk analysis, statistics, engineering, and the physical sciences. Uncertainty in Risk Assessment: Illustrates the need for seeing beyond probability to represent uncertainties in risk assessment contexts. Provides simple explanations (supported by straightforward numerical examples) of the meaning of different types of probabilities, including interval probabilities, and the fundamentals of possibility theory and evidence theory. Offers guidance on when to use probability and when to use an alternative representation of uncertainty. Presents and discusses methods for the representation and characterization of uncertainty in risk assessment. Uses examples to clearly illustrate ideas and concepts.