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. INDICE: Preface Table of contents PART I 1 Introduction to risk assessment and the associated treatment of uncertainties. Challenges 1.1 Risk 1.1.1 The concept of risk 1.1.2 Describing/measuring risk 1.1.3 Examples 1.2 Probabilistic Risk Assessment 1.3 Use of risk assessment. The risk management and decision–making context 1.4 Treatment of uncertainties in risk assessments 1.5 Challenges. Discussion 1.5.1 Examples 1.5.2 Alternatives to the probability–based approaches to risk and uncertainty assessment 1.5.3 The way ahead References – Part I PART II 2 Probabilistic approaches for treating uncertainty 2.1 Classical probabilities 2.2 Frequentist probabilities 2.3 Subjective probabilities 2.4 The Bayesian subjective probability framework 2.5 Logical probabilities 3 Imprecise probabilities for treating uncertainty 4 Possibility theory for treating uncertainty 4.1 Basics of possibility theory 4.2 Approaches to construct possibility distributions 4.2.1 Building possibility distributions from nested probability intervals 4.2.2 Justification for using the triangular possibility distribution 4.2.3 Building possibility distributions using Chebyshev’s inequality 5 Evidence theory for treating uncertainty 6 Methods of uncertainty propagation 6.1 Level 1 uncertainty propagation setting 6.1.1 Level 1 purely probabilistic framework 6.1.2 Level 1 Purely possibilistic framework 6.1.3 Level 1 hybrid probabilistic–possibilistic framework 6.2 Level 2 uncertainty propagation setting 6.2.1 Level 2 purely probabilistic framework 6.2.2 Level 2 hybrid probabilistic–evidence theory framework 7 Discussion 7.1 Probabilistic analysis 7.2 Lower and upper probabilities 7.3 Non–probabilistic representations with interpretations other than lower and upper probabilities 7.4 Hybrid representations of uncertainty 7.5 Semi–quantitative approaches References – Part II PART III 8 Uncertainty representation and propagation in structural reliability analysis: Application to fatigue crack propagation 8.1 Structural reliability analysis 8.1.1 A model of crack propagation under cyclic fatigue 8.2 Case study 8.3 Uncertainty representation 8.4 Uncertainty propagation 8.5 Results 8.6 Comparison with a purely probabilistic method 9 Uncertainty representation and propagation in maintenance performance assessment: Application to a check valve of a turbo pump lubricating system 9.1 Maintenance performance assessment 9.2 Case study 9.3 Uncertainty representation 9.4 Uncertainty propagation 9.4.1 Maintenance performance assessment in the case of no epistemic uncertainty on the parameters 9.4.2 Application of the hybrid probabilistic–theory of evidence uncertainty propagation method 157 9.5 Results 10 Uncertainty representation and propagation in event tree analysis: Application to a nuclear accident scenario 10.1 Event tree analysis 10.2 Case study 10.3 Uncertainty representation 10.4 Uncertainty propagation 10.5 Results 10.6 Comparison of the results with those obtained by using other uncertainty representation and propagation methods 10.6.1 Purely Probabilistic representation and propagation of the uncertainty 10.6.2 Purely possibilistic representation and propagation of the uncertainty 10.6.3 Result comparison 11 Uncertainty representation and propagation in the evaluation of the consequences of industrial activity: Application to the estimation of the ground level concentration of dioxin/furans emitted from a waste gasification plant 11.1 Evaluation of the consequences of undesirable events 11.2 Case study 11.3 Uncertainty representation 11.4 Uncertainty propagation 11.5 Results 11.6 Comparison of the results with those obtained using a purely probabilistic approach 12 Uncertainty representation and propagation in an event tree analysis: Probabilistic risk assessment of a process plant using Bayesian analysis 12.1 Introduction 12.2 Case description 12.3 The “textbook” Bayesian approach (Level 2 analysis) 12.4 An alternative approach based on subjective probabilities (Level 1 analysis) References – Part III PART IV 13 Conclusions References – Part IV APPENDICES Appendix A. Operative procedures of the methods for uncertainty propagation Appendix A.1 Level 1 hybrid probabilistic–possibilistic framework Appendix A.2 Level 2 purely probabilistic framework Appendix B. Possibility–probability transformation Index
- ISBN: 978-1-118-48958-1
- Editorial: Wiley–Blackwell
- Encuadernacion: Cartoné
- Páginas: 200
- Fecha Publicación: 04/02/2014
- Nº Volúmenes: 1
- Idioma: Inglés