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Uncertainty in Risk Assessment


Uncertainty in Risk Assessment

The Representation and Treatment of Uncertainties by Probabilistic and Non-Probabilistic Methods
1. Aufl.

von: Terje Aven, Piero Baraldi, Roger Flage, Enrico Zio

72,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 17.12.2013
ISBN/EAN: 9781118763056
Sprache: englisch
Anzahl Seiten: 200

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Beschreibungen

<p><b>Explores methods for the representation and treatment of uncertainty in risk assessment</b></p> <p>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 <i>Uncertainty in Risk Assessment</i> 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.</p> <p>While requiring some fundamental background in risk assessment, as well as a basic knowledge of probability theory and statistics, <i>Uncertainty in Risk Assessment</i> 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.</p> <p><i>Uncertainty in Risk Assessment</i>:</p> <ul> <li>Illustrates the need for seeing beyond probability to represent uncertainties in risk assessment contexts.</li> <li>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.</li> <li>Offers guidance on when to use probability and when to use an alternative representation of uncertainty.</li> <li>Presents and discusses methods for the representation and characterization of uncertainty in risk assessment.</li> <li>Uses examples to clearly illustrate ideas and concepts.</li> </ul>
<p>Preface ix</p> <p><b>PART I INTRODUCTION 1</b></p> <p><b>1 Introduction 3</b></p> <p>1.1 Risk 4</p> <p>1.1.1 The concept of risk 4</p> <p>1.1.2 Describing/measuring risk 6</p> <p>1.1.3 Examples 6</p> <p>1.2 Probabilistic risk assessment 8</p> <p>1.3 Use of risk assessment: The risk management and decision-making context 11</p> <p>1.4 Treatment of uncertainties in risk assessments 13</p> <p>1.5 Challenges: Discussion 15</p> <p>1.5.1 Examples 16</p> <p>1.5.2 Alternatives to the probability-based approaches to risk and uncertainty assessment 17</p> <p>1.5.3 The way ahead 19</p> <p>References – Part I 21</p> <p><b>PART II METHODS 27</b></p> <p><b>2 Probabilistic approaches for treating uncertainty 29</b></p> <p>2.1 Classical probabilities 30</p> <p>2.2 Frequentist probabilities 31</p> <p>2.3 Subjective probabilities 35</p> <p>2.3.1 Betting interpretation 36</p> <p>2.3.2 Reference to a standard for uncertainty 36</p> <p>2.4 The Bayesian subjective probability framework 37</p> <p>2.5 Logical probabilities 39</p> <p><b>3 Imprecise probabilities for treating uncertainty 41</b></p> <p><b>4 Possibility theory for treating uncertainty 45</b></p> <p>4.1 Basics of possibility theory 45</p> <p>4.2 Approaches for constructing possibility distributions 49</p> <p>4.2.1 Building possibility distributions from nested probability intervals 49</p> <p>4.2.2 Justification for using the triangular possibility distribution 51</p> <p>4.2.3 Building possibility distributions using Chebyshev’s inequality 52</p> <p><b>5 Evidence theory for treating uncertainty 53</b></p> <p><b>6 Methods of uncertainty propagation 59</b></p> <p>6.1 Level 1 uncertainty propagation setting 61</p> <p>6.1.1 Level 1 purely probabilistic framework 62</p> <p>6.1.2 Level 1 purely possibilistic framework 64</p> <p>6.1.3 Level 1 hybrid probabilistic–possibilistic framework 67</p> <p>6.2 Level 2 uncertainty propagation setting 71</p> <p>6.2.1 Level 2 purely probabilistic framework 73</p> <p>6.2.2 Level 2 hybrid probabilistic–evidence theory framework 75</p> <p><b>7 Discussion 79</b></p> <p>7.1 Probabilistic analysis 80</p> <p>7.2 Lower and upper probabilities 82</p> <p>7.3 Non-probabilistic representations with interpretations other than lower and upper probabilities 84</p> <p>7.4 Hybrid representations of uncertainty 85</p> <p>7.5 Semi-quantitative approaches 87</p> <p>References – Part II 93</p> <p><b>PART III PRACTICAL APPLICATIONS 99</b></p> <p><b>8 Uncertainty representation and propagation in structural reliability analysis 101</b></p> <p>8.1 Structural reliability analysis 101</p> <p>8.1.1 A model of crack propagation under cyclic fatigue 101</p> <p>8.2 Case study 102</p> <p>8.3 Uncertainty representation 104</p> <p>8.4 Uncertainty propagation 105</p> <p>8.5 Results 107</p> <p>8.6 Comparison to a purely probabilistic method 107</p> <p><b>9 Uncertainty representation and propagation in maintenance performance assessment 111</b></p> <p>9.1 Maintenance performance assessment 111</p> <p>9.2 Case study 113</p> <p>9.3 Uncertainty representation 116</p> <p>9.4 Uncertainty propagation 118</p> <p>9.4.1 Maintenance performance assessment in the case of no epistemic uncertainty on the parameters 118</p> <p>9.4.2 Application of the hybrid probabilistic–theory of evidence uncertainty propagation method 122</p> <p>9.5 Results 123</p> <p><b>10 Uncertainty representation and propagation in event tree analysis 127</b></p> <p>10.1 Event tree analysis 127</p> <p>10.2 Case study 128</p> <p>10.3 Uncertainty representation 134</p> <p>10.4 Uncertainty propagation 135</p> <p>10.5 Results 137</p> <p>10.6 Comparison of the results to those obtained by using other uncertainty representation and propagation methods 138</p> <p>10.6.1 Purely probabilistic representation and propagation of the uncertainty 138</p> <p>10.6.2 Purely possibilistic representation and propagation of the uncertainty 138</p> <p>10.7 Result comparison 141</p> <p>10.7.1 Comparison of results 141</p> <p>10.7.2 Comparison of the results for the probability of occurrence of a severe consequence accident 145</p> <p><b>11 Uncertainty representation and propagation in the evaluation of the consequences of industrial activity 147</b></p> <p>11.1 Evaluation of the consequences of undesirable events 147</p> <p>11.2 Case study 148</p> <p>11.3 Uncertainty representation 150</p> <p>11.4 Uncertainty propagation 152</p> <p>11.5 Results 152</p> <p>11.6 Comparison of the results to those obtained using a purely probabilistic approach 153</p> <p><b>12 Uncertainty representation and propagation in the risk assessment of a process plant 155</b></p> <p>12.1 Introduction 155</p> <p>12.2 Case description 155</p> <p>12.3 The “textbook” Bayesian approach (level 2 analysis) 156</p> <p>12.4 An alternative approach based on subjective probabilities (level 1 analysis) 159</p> <p>References – Part III 163</p> <p><b>PART IV CONCLUSIONS 167</b></p> <p><b>13 Conclusions 169</b></p> <p>References – Part IV 173</p> <p><b>Appendix A Operative procedures for the methods of uncertainty propagation 175</b></p> <p>A.1 Level 1 hybrid probabilistic–possibilistic framework 175</p> <p>A.2 Level 2 purely probabilistic framework 176</p> <p><b>Appendix B Possibility–probability transformation 179</b></p> <p>Reference 181</p> <p>Index 183</p>
<p>“Therefore, I would recommend this book to a broad audience, from advanced undergraduates, to specialists, including probability theoreticians.”  (<i>Computing Reviews</i>, 16 July 2014)</p> <p> </p>
<p><b>Terje Aven</b>, <i>University of Stavanger, Norway</i></p> <p><b>Piero Baraldi</b>, <i>Politecnico di Milano, Italy</i></p> <p><b>Roger Flage</b>, <i>University of Stavanger, Norway</i></p> <p><b>Enrico Zio</b>, <i>Politecnico di Milano, Italy</i></p>
<p><b>Explores methods for the representation and treatment of uncertainty in risk assessment</b></p> <p>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 <i>Uncertainty in Risk Assessment</i> 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.</p> <p>While requiring some fundamental background in risk assessment, as well as a basic knowledge of probability theory and statistics, <i>Uncertainty in Risk Assessment</i> 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.</p> <p><i>Uncertainty in Risk Assessment</i>:</p> <ul> <li>Illustrates the need for seeing beyond probability to represent uncertainties in risk assessment contexts.</li> <li>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.</li> <li>Offers guidance on when to use probability and when to use an alternative representation of uncertainty.</li> <li>Presents and discusses methods for the representation and characterization of uncertainty in risk assessment.</li> <li>Uses examples to clearly illustrate ideas and concepts.</li> </ul>

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