Risk Analysis in Engineering: Techniques, Tools, and Trends

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ISBN: 9781574447941 | 1574447947
Cover: Hardcover
Copyright: 1/13/2006

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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 part of the student and provides the necessary mathematical and engineering foundations. The text relies heavily on, but is not limited to, examples from the nuclear industry, because that is where PRA techniques were first developed. Since PRA provides a best-estimate approach, the author pays special attention to explaining uncertainty characterization.The book begins with a description of the basic definitions and principles of risk, safety, and performance and presents the elements of risk analysis and their applications in engineering. After highlighting the methods for performing PRAs, the author describes how to assess and measure performance of the building blocks of PRAs, such as reliability of hardware subsystems, structures, components, human actions, and software. He covers methods of characterizing uncertainties and methods for propagating them through the PRA model to estimate uncertainties of the results. The book explores how to identify and rank important and sensitive contributors to the estimated risk using the PRA and performance assessment models. It also includes a description of risk acceptance criteria and the formal methods for making decisions related to risk management options and strategies. The book concludes with a brief review of the main aspects, issues, and methods of risk communication.Drawing on notes, homework problems, and exams from courses he has taught as well as feedback from his students, Professor Modarres provides a from-the-trenches method for teaching risk assessment for engineers. This is a textbook that is easy to use for students and professors alike.

Introduction and Basic Definitions	p. 1
Introduction	p. 1
Importance of Risk Analysis	p. 1
Complexity and Characteristics of Engineering Systems and their Models	p. 1
Demand for Risk Analysis	p. 3
Emergence of Formal Risk Analysis for Managing and Regulating Risks	p. 3
Categories of Risk	p. 3
Trends in the Use of Risk Analysis Methods in Engineering	p. 4
Elements and Types of Risk Analysis	p. 5
Quantitative Risk Analysis	p. 6
Qualitative Risk Analysis	p. 6
Mixed Qualitative-Quantitative Analysis	p. 6
Risk Assessment	p. 7
Identification of Hazards	p. 8
Identification of System Barriers	p. 8
Identification of Challenges to Barriers	p. 8
Estimation of Frequency or Probability of a Hazard Exposure	p. 9
Consequences Evaluation	p. 9
Risk Management	p. 9
Risk Communication	p. 11
The Nature of the Risk	p. 11
The Nature of the Benefits	p. 11
Uncertainties in Risk Assessment	p. 12
Risk Management Options	p. 12
References	p. 12
Elements of Risk Assessment	p. 13
Types of Risk Assessment	p. 13
Risk, Hazard, Performance, and Engineering Risk Assessment	p. 14
Risk and Hazard	p. 14
Engineering Risk Assessment	p. 14
Performance and Performance Assessment	p. 18
Elements of a Formal Risk Assessment	p. 20
Qualitative Risk Assessment	p. 24
Quantitative Risk Assessment	p. 28
References	p. 32
Probabilistic Risk Assessment	p. 33
Introduction	p. 33
Steps in Conducting a PRA	p. 33
Objectives and Methodology Definition	p. 33
Familiarization and Information Assembly	p. 34
Identification of Initiating Events	p. 35
Sequence or Scenario Development	p. 38
Logic Modeling	p. 39
Failure Data Collection, Analysis, and Performance Assessment	p. 39
Quantification and Integration	p. 40
Uncertainty Analysis	p. 42
Sensitivity Analysis	p. 43
Risk Ranking and Importance Analysis	p. 43
Interpretation of Results	p. 45
Strength of PRA	p. 45
Scenario and Logic Modeling, Development, and Quantification	p. 46
Fault Tree Method	p. 46
Evaluation of Logic Trees	p. 53
Event Trees Method	p. 63
Master Logic Diagram	p. 69
Modeling of Dependent Failures in Risk Assessment	p. 75
Multiple Greek Letter Model	p. 81
[alpha]-Factor Model	p. 82
Binomial Failure Rate Model	p. 83
Assessment of Parameters of CCF Models	p. 83
A Simple Example of PRA	p. 85
References	p. 111
Performance Assessment: Data and Modeling	p. 113
Introduction	p. 113
Active Hardware Performance Assessment	p. 115
Basic Random Processes Used in PRA	p. 115
Homogeneous Poisson Process	p. 116
Renewal Process	p. 117
Analysis of Less than Perfect Repair: Generalized Renewal Process	p. 120
Statistical Data Analysis for Hardware Items	p. 122
Parameter Estimation for the HPP Using Complete Data	p. 123
Parameter Estimation for NHPP Using Complete Data	p. 123
Availability as a Measure of Performance for Repairable Hardware Items	p. 128
Instantaneous (Point) Availability	p. 129
Limiting Point Availability	p. 130
Average Availability	p. 131
Classical Parameter Estimation of Distribution Models for Performance Assessment of Hardware	p. 132
Estimation of Parameters of Exponential Distribution Model (Based on Complete Data)	p. 133
Estimation of Parameters of Lognormal Distribution Model (Based on Complete Data)	p. 138
Estimation of Parameters of Weibull Distribution Model (Based on Complete and Censored Data)	p. 138
Estimation of Parameters of Binomial Distribution Model	p. 140
Estimation of Parameters of Exponential, Lognormal, and Weibull Models when Dealing with Incomplete (Censored) Data	p. 141
Bayesian Parameters Estimation of Distribution Models for Performance Assessment of Hardware	p. 143
Bayesian Estimation of the Parameter of Exponential Distribution Model	p. 145
Bayesian Estimation of the Parameter of Binomial Distribution Model	p. 147
A Comparison of Classical Parameter Estimation and Bayesian Parameter Estimation Methods: Advantages and Disadvantages	p. 153
Performance of Passive and Some Active Hazard Barriers Through Physics and Engineering of Failure Modeling	p. 155
Stress-Strength Models	p. 156
Damage-Endurance Models	p. 159
Software Performance Assessment	p. 165
Software Reliability Models	p. 166
Human Reliability Analysis	p. 170
Human Reliability Analysis Process	p. 171
Human Reliability Analysis Models	p. 174
Human Reliability Data	p. 180
Expert Opinion in Risk Assessment and Performance Assessment	p. 181
Geometric Averaging Technique	p. 182
Bayesian Approach	p. 183
Statistical Evidence on the Accuracy of Expert Estimates	p. 183
References	p. 193
Uncertainty Analysis	p. 197
Introduction	p. 197
Types of Uncertainty	p. 199
Model Uncertainty	p. 200
Parameter Uncertainty	p. 204
Measures of Uncertainty	p. 207
Uncertainty Propagation Methods	p. 208
Method of Moments	p. 208
Probabilistic Methods (Monte Carlo Methods)	p. 213
The Response Surface Method	p. 223
Comparison of Uncertainty Propagation Methods	p. 232
Monte Carlo Method	p. 232
Differential Analysis (Method of Moments)	p. 233
Response surface methodology	p. 233
Graphic Representation of Uncertainty	p. 234
References	p. 238
Identifying, Ranking, and Predicting Contributors to Risk	p. 241
Introduction	p. 241
Importance Ranking in Probabilistic Risk Assessment	p. 242
Birnbaum	p. 243
Fussell-Vesely	p. 244
Risk Reduction Worth (RRW)	p. 244
Risk Achievement Worth (RAW)	p. 245
Differential Importance Measure (DIM)	p. 246
Importance Measures in Success Space	p. 246
A Comprehensive Example of Importance Measure Application	p. 247
Importance Measures in Failure and Success Domains	p. 251
Consideration of Uncertainties in Importance Measures used for Risk-Based Ranking	p. 255
Ranking Order Uncertainty	p. 256
Uncertainty Associated with the Importance Measures	p. 256
Illustrative Example	p. 257
Relative Ranking Based on Uncertain Importance Measures	p. 260
Absolute Ranking Based on Uncertain Importance Measures	p. 262
Uncertainty Importance Measures	p. 263
Precursor Analysis	p. 267
Basic Methodology	p. 267
Categorization and Selection of Precursor Events	p. 268
Properties of Precursor Estimator for the Occurrence Rate of Hazard Exposure Events and their Interpretation	p. 268
Applications of Precursor Analysis	p. 270
Differences between Precursor Analysis and Probabilistic Risk Assessments	p. 271
References	p. 273
Representation of Risk Values and Risk Acceptance Criteria	p. 275
Introduction	p. 275
Representation of Risk Results	p. 278
Health and Safety Risk Acceptance Criteria	p. 282
Individual Risk Acceptance Criteria	p. 284
Societal Risk Acceptance Criteria (Exposure, Consequence, Precursor)	p. 288
Economic Risk and Performance Acceptance Criteria	p. 291
Other Risk Acceptance Criteria in Form of Figures of Merit	p. 292
Acceptable Risk Levels Based on Changes in Figures of Merit	p. 293
Using Figures of Merit in Single Event Risk	p. 294
Instantaneous Absolute Value Limits	p. 295
Risk Acceptance Based on Importance Measures	p. 295
Risk Acceptance Based on Value of Human Life	p. 296
References	p. 301
Decision Making Techniques Using Risk Information	p. 303
Introduction	p. 303
Economic Methods in Risk Analysis	p. 303
Benefit-Cost Analysis	p. 304
Cost-Effectiveness Analysis	p. 308
Risk-Effectiveness Analysis	p. 308
Noneconomic Techniques	p. 311
Probability of Exceedance Method	p. 312
Structured Value Analysis	p. 312
Analytical Hierarchy Process	p. 318
Decision Tree Analysis	p. 323
References	p. 331
Risk Communication	p. 333
Introduction	p. 333
Forms of Communication	p. 334
Some Basic Rules of Risk Communication	p. 335
Elements of Effective Risk Communication	p. 335
Characteristics of an Effective Risk Communication	p. 336
Risk Perception	p. 338
References	p. 342
Basic Reliability Mathematics: Review of Probability and Statistics	p. 343
Introduction	p. 343
Elements of Probability	p. 343
Sets and Boolean Algebra	p. 343
Basic Laws of Probability	p. 346
Bayes' Theorem	p. 351
Probability Distributions	p. 351
Random Variable	p. 352
Some Basic Discrete Distributions	p. 352
Some Basic Continuous Distributions	p. 354
Joint and Marginal Distributions	p. 361
Basic Characteristics of Random Variables	p. 364
Estimation and Hypothesis Testing	p. 367
Point Estimation	p. 367
Interval Estimation and Hypothesis Testing	p. 369
Frequency Tables and Histograms	p. 371
Goodness-of-Fit Tests	p. 372
Chi-Square Test	p. 372
Kolmogorov Test	p. 373
Regression Analysis	p. 373
Simple Linear Regression	p. 374
References	p. 375
Appendix B	p. 376
Index	p. 391
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Risk Analysis in Engineering: Techniques, Tools, and Trends

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