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Risk Analysis
in Engineering
Techniques, Tools, and Trends
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Risk Analysis
in Engineering
Techniques, Tools, and Trends
Mohammad Modarres
Boca Raton London New York
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Preface
Engineering systems are becoming more complex, interconnected, nonlinear, geographically
widespread, and expensive. Consequences of failures of such systems are exceedingly dam-
aginganddestructivetohumans,environment,andeconomy.Failuresofengineeringsystems
areunavoidable.Theyareduetointernalcausesorhaveexternalorigins.Ofspecialconcerns
are externally caused system failures due to malicious terror acts, which disrupt or fail key
infrastructure systems, leading tolarge and widespread damage.
Forminimizingoccurrencesandcontrollingconsequencesofengineeringsystemfailures,
limitations areoften imposed on their usesinthe form ofsafety, health, environmental,and
security regulations. As the developed nations become more prosperous, the demands for
safety,health,security,andenvironmentalprotectionequallyrise,leadingtostricterrequire-
ments anddemandforincreasedperformance ofengineering systems. Toaddress thistrend,
formalengineeringriskanalysisemergedinthelate1960’sandmadesubstantialprogressinto
a well-established discipline. Two leading industries that made substantial contributions
to this growth are the nuclear and aerospace industries. However, more recent applications
ofsuch techniquescoverabroadspectrumof industries, includinggroundandair transpor-
tation, chemical process and oil, food processing, defense, financial, and energy production
and distribution. The concerns in these industries vary widely and include physical security,
information security, asset protection, human safety and health, environmental protection,
and componentand structural health management.
Ideally,theriskwhichcontemplatespossiblesystemfailuresandconsequencesintheform
ofadverseeventsleadingtocapitallosses,fatalities,andenvironmentalcontaminationcanbe
estimateddirectlyfromhistoricaloccurrencesofsuchadverseevents.Thisispossibleonlyin
engineering systems for which such data and information are readily available. Relying on
historical adverse events is, for example, the principal risk assessment approach in the
insurance industries. Other examples include assessment of land transportation risks, in
which performance and accident data are available. However, there are many engineering
systems for which adequate amounts of historical failure data and events needed to assess
their risks may not be available. Therefore, it is often necessary to estimate the risks of
complex engineering systems from models of such systems — especially for those systems
involving low-frequency failure events, leading to adverse consequences, such as nuclear
power plants. Risk models used in engineering systems are often probabilistic in nature,
because one needsto estimatethe associatedlow frequency offailuresand adverse events.
In the mid-1970’s the nuclear industry led by Professor Norman C. Rasmussen of MIT
introduced the Probabilistic Risk Assessment (PRA) methodology to assess risks associated
with the operation of the nuclear power plants in the United States. This model-based
methodology has gained considerable attention and has gone through substantial improve-
ments since its inception. The PRA methodology, whileoriginally focused on estimating the
frequencyofsomeadverseoutcomes(consequences),haschangedtobecomealife-cycletool
for supporting design, manufacturing, construction, operation, maintenance, security, and
decommissioning of complex engineering systems. Recently, PRA methodology is used for
devising risk-based and risk-informed regulatory and policy requirements that are commen-
suratewiththe risks of theengineering systems.
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Threeprimarycomponentsofriskanalysisnamelyriskassessment,riskmanagement,and
riskcommunication,involveseparate,butimportantinterrelatedactivities.Whiletheprimary
purposeof PRA isto assess therisks ofengineering systems, it also provides systematic and
transparentriskmodelsandassessmenttoolsforbetterriskmanagementandriskcommuni-
cation.
This book has been developed as a guide for practitioners of risk analysis and as a
textbook for graduate-level and senior-level undergraduate courses in risk analysis and
presents the engineering approach to probabilistic risk analysis. Particularly, the book
emphasizes methods for comprehensive PRA studies, including formal decision tech-
niques needed for risk management. A critical part of any PRA is the treatment of
uncertainties in the models and parameters used in the analysis. Since the PRA is a best-
estimate approach, the uncertainties associated with the results and risk management
strategies must be characterized. For this reason, the book has a special emphasis on
uncertainty characterization.
The book assumes no prior knowledge of risk analysis and provides the necessary
mathematicalandengineeringfoundations.However,somebasicknowledgeofundergradu-
ate senior-level probability and statistics and basic engineering is helpful. While the book is
notwrittenwithaparticularapplicationinmind,becauseofitpioneeringrolesindeveloping
thePRAtechniques,thebookreliesheavilyonexamplesfromthenuclearindustry.Tomake
it a complete guide, this book uses related materials from my previous book, Reliability
Engineering and Risk Analysis:APractical Guide(Marcel Dekker, 1999).
Chapter 1 describes the basic definitions and the notions of risk, safety, and perform-
ance. Chapter 2 presents the elements of risk analysis and their applications in engineering.
Chapter 3 focuses on methods for performing PRAs. This is a key chapter because it
describes the structure of a PRA and how to build the necessary logic models, and how
to solve such models to find the risk values. Chapter 4 describes how to assess and measure
performance ofthebuildingblocks ofthePRAs, suchasreliabilityofhardwaresubsystems,
structures, components, human actions, and software. It also shows how performance and
risk interrelate. Since the performance measurements and the PRA models involve uncer-
tainties, Chapter 5 describes methods of characterizing such uncertainties, and methods for
propagating them through the PRA model to estimate uncertainties of the results. Chapters
4 and 5 cover critical topics of PRAs. Chapter 6 reviews a topic that makes the transition
from risk assessment to risk management. This chapter describes ways to identify and rank
important and sensitive contributors to the estimated risk using the PRA and performance
assessment models. Contributors to risk are the most important results of any risk assess-
ment. They focus the analyst’s attention to those elements of the engineering systems that
highly affect risks and uncertainties, and prioritize candidates for risk management actions.
Chapters 7 and 8 are primarily related to probabilistic risk management techniques, with
Chapter7describingriskacceptancecriteriausedasreferencelevelstoaccept,compare,and
reduce estimated risks. Chapter 8 summarizing the formal methods for making decisions
related to risk management options and strategies. The book ends with a brief reviewof the
main aspects, issues, and methods of risk communication presented in Chapter 9.
Finally, Appendix A provides a comprehensive review of the necessary probability and
statistics techniques for model-based risk analyses, and Appendix B provides necessary
statistical tables.
Thisbookcamefromnotes,homeworkproblems,andexaminationsofagraduatecourse
entitled ‘‘Risk Assessment for Engineers’’ offered by me at the University of Maryland,
College Park for the past 20 years. I would like to thank all the students who participated
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in this course, many of whom made valuable inputs to the contents of the book. I would
like to thank Drs. Mark Kaminskiy and Vasily Krivtsov who were my coauthors with this
book’s predecessor, Reliability Engineering and Risk Analysis: A Practical Guide. Many
graduate students and research staff working with me helped in the development of the
materials and examples of this book. Particularly, I am indebted to Jose L. Hurtado, Reza
Azarkhail,SamChamberlain,M.PourGoulMohamad,KristineFretz,CarlosBaroetta,and
Genebelin Valbuena for their valuable contributions and examples. Finally, the technical
graphicsandformattingofthebookwouldhavenotbeenpossiblewithouttirelesseffortsof
Miss Willie M. Webb.
Mohammad Modarres
College Park, MD, USA
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Author
Mohammad Modarres is professor of nuclear engineering and reliability engineering and
director of the Center for Technology Risk Studies at the University of Maryland, College
Park.HepioneeredanddevelopedtheinternationallyleadinggraduateprograminReliability
Engineering at the University of Maryland. His research areas are probabilistic risk assess-
ment,uncertainty analysis,andphysicsoffailure modeling.Inthepast23yearshehasbeen
with the University of Maryland, he has served as a consultant to several governmental
agencies, private organizations, and national laboratories in areas related to risk analysis,
especially applications to complex systems and processes such as the nuclear power plants.
ProfessorModarreshasover200papersinarchivaljournalsandproceedingsofconferences
and three books in various areas of risk and reliability engineering. He is a University of
MarylandDistinguishedScholar-Teacher.HereceivedhisPh.D.inNuclearEngineeringfrom
MIT and his M.S inMechanical Engineering also from MIT.
