Titel_SS06

A.5 References A.

• 1 st Lecture – Engineering Decisions under Uncertainty 1.


• 1.1 Introduction 1.
  
  
  • Objective for Engineering Decision-Making 1.
  
  
  • Societal Performance and Challenges 1.
  
  
  
  


• 1.2 Introduction to Risk-Based Decision-Making 1.
  
  
  • Example 1.1 – Feasibility of hydraulic power plant 1.
  
  
  
  


• 1.3 Definition of Risk 1.


• 1.4 The Risk-Based Decision Process 1.
  
  
  • Define Context 1.
  
  
  • Define System 1.
  
  
  • Identify Hazard Scenario 1.
  
  
  • Analysis of Consequences 1.
  
  
  • Analysis of Probability 1.
  
  
  • Identify Critical Risk Scenarios 1.
  
  
  • Analysis of Sensitivities 1.
  
  
  • Risk Assessment 1.
  
  
  • Risk Treatment 1.
  
  
  • Monitoring and Review 1.
  
  
  
  


• 1.5 Detailing of Risk Analysis 1.


• 1.6 Sources of Risk in Engineering 1.
  
  
  • General Risks for Individuals 1.
  
  
  • Risks Due to Natural Hazards 1.
  
  
  • Risks Due to Malevolence 1.
  
  
  • Risks Due to Structural Failures 1.
  
  
  • The Role of Human Errors 1.
  
  
  • Example 1.2 – Human error in bridge design 1.
  
  
  
  


• 1.7 A Review of Reported Failures 1.
  
  
  • Failures of Building and Bridge Structures 1.
  
  
  • Failure of Dam Structures 1.
  
  
  • Failures of Offshore Structures 1.
  
  
  • Failures of Pipelines 1.
  
  
  • Failures in Nuclear Power Plants 1.
  
  
  • Failures of Chemical Facilities 1.
  
  
  
  


• 2 nd Lecture – Review of Basic Probability Theory and Statistics 2.


• 2.1 Introduction 2.


• TOC-


• 2.2 Definition of Probability 2.
  
  
  • Frequentistic Definition 2.
  
  
  • Classical Definition 2.
  
  
  • Bayesian Definition 2.
  
  
  • Practical Implications of the Different Interpretations of Probability 2.
  
  
  
  


• 2.3 Conditional Probability and Bayes’ Rule 2.
  
  
  • Example 2.1 – Using Bayes’ rule for concrete assessment 2.
  
  
  • Example 2.2 – Using Bayes’ rule for bridge upgrading 2.
  
  
  
  


• 2.4 Introduction to Descriptive Statistics 2.


• 2.5 Numerical Summaries 2.
  
  
  • Central Measures 2.
  
  
  • Example 2.3 – Concrete compressive strength data 2.
  
  
  • Dispersion Measures 2.
  
  
  • Other Measures 2.
  
  
  • Measures of Correlation 2.
  
  
  
  


• 2.6 Graphical Representations 2.
  
  
  • One-Dimensional Scatter Diagrams 2.
  
  
  • Histograms 2.
  
  
  • Quantile Plots 2.
  
  
  • Tukey Box Plots 2.
  
  
  
  


• 2.7 Introduction to Engineering Uncertainty Modelling 2.


• 2.8 Uncertainties in Engineering Problems 2.


• 2.9 Random Variables 2.
  
  
  • Cumulative Distribution and Probability Density Functions 2.
  
  
  • Moments of Random Variables and the Expectation Operator 2.
  
  
  • Probability Density and Distribution Functions 2.
  
  
  • The Normal Distribution 2.
  
  
  • The Lognormal Distribution 2.
  
  
  • Properties of the Expectation Operator 2.
  
  
  • Random Vectors and Joint Moments 2.
  
  
  • Conditional Distributions and Conditional Moments 2.
  
  
  
  


• 2.10 Random Processes and Extremes 2.
  
  
  • The Poisson Counting Process 2.
  
  
  • Continuous Random Processes 2.
  
  
  • Statistical Assessment of Extreme Values 2.
  
  
  • Extreme Value Distributions 2.
  
  
  • Type I Extreme Maximum Value Distribution – Gumbel max 2.
  
  
  • Type I Extreme Minimum Value Distribution – Gumbel min 2.
  
  
  • Type II Extreme Maximum Value Distribution – Frechet max 2.
  
  
  • Type III Extreme Minimum Value Distribution – Weibull min 2.
  
  
  • Return Period for Extreme Events 2.
  
  
  
  


• 2.11 Introduction to Engineering Model Building 2.


• TOC-


• 2.12 Selection of Probability Distributions 2.
  
  
  • Model Selection by Use of Probability Paper 2.
  
  
  
  


• 2.13 Estimation of Distribution Parameters 2.
  
  
  • The Method of Moments 2.
  
  
  • The Method of Maximum Likelihood 2.
  
  
  • Example 2.4 – Parameter estimation 2.
  
  
  
  


• 3 rd Lecture – Bayesian Decision Analysis 3.


• 3.1 Introduction 3.


• 3.2 The Decision / Event Tree 3.


• 3.3 Decisions Based on Expected Values 3.


• 3.5 Decision Making Subject to Uncertainty 3.


• 3.6 Decision Analysis with Given Information - Prior Analysis 3.


• 3.7 Decision Analysis with Additional Information - Posterior Analysis 3.


• 3.8 Decision Analysis with ‘Unknown’ Information - Pre-posterior Analysis 3.


• 3.9 The Risk Treatment Decision Problem 3.


• 4 th Lecture – Risk Assessment in Civil Engineering 4.


• 4.1 Introduction 4.


• 4.2 The JCSS Framework for Risk Assessment in Engineering 4.
  
  
  • Decisions and decision maker 4.
  
  
  • Attributes of decision outcomes 4.
  
  
  • Preferences among attributes - utility 4.
  
  
  • Constraints on decision making 4.
  
  
  • Feasibility and optimality 4.
  
  
  
  


• 4.3 System Modelling 4.
  
  
  • Knowledge and uncertainty 4.
  
  
  • System representation 4.
  
  
  • Exposure and hazards 4.
  
  
  • Vulnerability 4.
  
  
  • Robustness 4.
  
  
  
  


• 4.4 Assessment of risk 4.
  
  
  • Indicators of risk 4.
  
  
  • Risk perception 4.
  
  
  • Comparison of decision alternatives 4.
  
  
  • Criteria for and acceptance of risk 4.
  
  
  • Discounting and sustainability 4.
  
  
  
  


• TOC-
  
  
  • Risk treatment 4.
  
  
  • Risk transfer 4.
  
  
  • Risk communication 4.
  
  
  
  


• 4.5 The Procedure of Risk Assessment 4.


• 4.6 Techniques for System Identification 4.
  
  
  • Preliminary Hazard Analysis - PHA 4.
  
  
  • Failure Modes and Effect Analysis - FMEA 4.
  
  
  • Failure Modes Effect and Criticality Analysis - FMECA 4.
  
  
  • Hazard and Operability Studies – HAZOP 4.
  
  
  • Risk Screening Sessions - HAZID 4.
  
  
  • Incident Databanks 4.
  
  
  • Identification of Exposures and Event Scenarios in Civil Engineering Applications 4.
  
  
  
  


• 4.7 Tools for Risk Analysis 4.
  
  
  • Fault Tree Analysis 4.
  
  
  • Example 4.1 – Power supply system 4.
  
  
  • Event Trees 4.
  
  
  • Example 4.2 – non-destructive testing of concrete structures 4.
  
  
  • Cause Consequence Charts 4.
  
  
  
  


• 5 th Lecture – Elements of Classical Reliability Theory 5.


• 5.1 Introduction 5.


• 5.2 Introduction to the classical reliability theory 5.
  
  
  • Example 5.1 – Pump failure modelling 5.
  
  
  
  


• 5.3 Failure rate data for mechanical systems and components 5.


• 5.4 Reliability analysis of static components 5.


• 6 th Lecture – Methods of Structural Reliability Analysis 6.


• 6.1 Introduction 6.


• 6.2 Failure Events and Basic Random Variables 6.


• 6.3 Linear Limit State Functions and Normal Distributed Variables 6.


• 6.4 The Error Accumulation Law 6.
  
  
  • Example 6.1 – Linear Safety Margin 6.
  
  
  • Example 6.2 – Error Accumulation Law 6.
  
  
  
  


• 6.5 Non-linear Limit State Functions 6.
  
  
  • Example 6.3 – Non-linear Safety Margin 6.
  
  
  
  


• 6.6 Correlated and Dependent Random Variables 6.


• TOC-


• 6.7 Non-Normal and Dependent Random Variables 6.
  
  
  • The Normal-tail Approximation 6.
  
  
  • The Rosenblatt Transformation 6.
  
  
  
  


• 6.8 Software for Reliability Analysis 6.


• 6.9 Assessment of Partial Safety Factors by FORM Analysis 6.
  
  
  • Example 6.4 – Calculation of Partial Safety Factors 6.
  
  
  
  


• 6.10 Simulation Methods 6.
  
  
  • Crude Monte-Carlo Simulation 6.
  
  
  • Importance Sampling Simulation Method 6.
  
  
  
  


• 7 th Lecture – Probabilistic Modelling in Structural Engineering 7.


• 7.1 Introduction 7.


• 7.2 Probabilistic Load Modelling 7.
  
  
  • Loads on Buildings – the JCSS Probabilistic Model Code “Light” 7.
  
  
  • Permanent Loads 7.
  
  
  • Live Floor Loads 7.
  
  
  • Wind Loads 7.
  
  
  • Snow Loads 7.
  
  
  • Combinations of Loads 7.
  
  
  • Turkstra’s Load Combination Rule 7.
  
  
  • The Ferry Borges – Castanheta Load Combination Rule 7.
  
  
  
  


• 7.3 Probabilistic Modelling of Resistances 7.
  
  
  • Geometrical Uncertainties 7.
  
  
  • Material Resistances – the JCSS Probabilistic Model Code “Light” 7.
  
  
  • Concrete Compressive Strength 7.
  
  
  • Reinforcement Steel 7.
  
  
  • Structural Steel 7.
  
  
  
  


• 7.4 Probabilistic Modelling of Model Uncertainties 7.
  
  
  • Model Uncertainties – the JCSS Probabilistic Model Code “Light” 7.
  
  
  
  


• 8 th Lecture – Time Variant Reliability Analysis 8.


• 8.1 Introduction 8.


• 8.2 General Formulation 8.
  
  
  • Poisson Processes 8.
  
  
  • Normal Processes 8.
  
  
  
  


• 8.3 Approximations to the Time Variant Reliability Problem 8.
  
  
  • Non-ergodic Components and Random Sequences 8.
  
  
  • Situations to Differentiate in Practical Cases 8.
  
  
  
  


• TOC-


• 9 th Lecture – Structural Systems Reliability Analysis 9.


• 9.1 Introduction 9.


• 9.2 Probabilistic Characteristics of Systems 9.
  
  
  • Example 9.1– Successive reduction of systems using the simple bounds 9.
  
  
  
  


• 9.3 Mechanical Modelling of Structural Systems 9.
  
  
  • Example 9.2– System reliability analysis 9.
  
  
  • The -unzipping Method 9.
  
  
  • The Fundamental Mechanism Method 9.
  
  
  
  


• 9.4 Risk Based Assessment of Structural Robustness 9.
  
  
  • Example 9.3– Assessment of Structural Robustness 9.
  
  
  
  


• 10 th Lecture – Bayesian Probabilistic Nets in Risk Assessment 10.


• 10.1 Introduction 10.


• 10.2 Causality and Reasoning 10.
  
  
  • Example 10.1– Reasoning on the quality of concrete structures 10.
  
  
  
  


• 10.3 Introduction to Causal and Bayesian Networks 10.


• 10.4 BPN’s with Discrete State Variables 10.


• 10.5 Use of BPN’s in Risk Assessment and Decision Analysis 10.
  
  
  • Nets 10. Example 10.2– Classical fault tree and event tree risk analysis by Bayesian Probabilistic
  
  
  • Example 10.3– Decision analysis by Bayesian Probabilistic Nets 10.
  
  
  
  


• 10.6 Large Scale Natural Hazards Risk Management using BPN’s 10.


• 11 th Lecture – Basis for the Design of Structures 11.


• 11.1 Introduction 11.


• 11.2 Structural Reliability and Safety Formats of Codes 11.


• 11.3 Formulating Code Calibration as a Decision Problem 11.


• 11.4 Target Reliabilities for Design of Structures 11.


• 11.5 The JCSS Code Calibration Procedure 11.
  
  
  • Cal software 11. Example 11.1 - Calibration of partial safety factors using the JCSS Code
  
  
  
  


• of Structures 12. 12 th Lecture – Reliability Based Assessment and Inspection


• TOC-


• 12.1 Introduction 12.


• 12.2 General Philosophy for Reassessment 12.
  
  
  • Reasons for Reassessment 12.
  
  
  • Framework for Structural Reassessment 12.
  
  
  • Value of information 12.
  
  
  • Structural Performance Assessment 12.
  
  
  • Practical Aspects of Reassessment 12.
  
  
  • Inspection strategy based on known deterioration 12.
  
  
  • Inspection strategy based on unexpected deterioration 12.
  
  
  
  


• 12.3 Theoretical Framework for Reassessment 12.


• 12.4 Reliability Updating in Assessment of Structures 12.
  
  
  • Updating of Random Variables 12.
  
  
  • Event Updating 12.
  
  
  
  


• 12.5 Decision Analysis in Structural Reassessment 12.
  
  
  • The Decision Tree 12.
  
  
  • Assessment of utility/benefit 12.
  
  
  • Decision analysis with given information 12.
  
  
  
  


• Example 12.1 - Reassessment decision analysis with given information –prior 12.
  
  
  • Decision analysis with new information 12.
  
  
  • Example 12.2 – Reassessment analysis based on new data - posterior analysis 12.
  
  
  • Decision analysis concerning collection of information 12.
  
  
  • Example 12.3 – Optimal planning of experiments – pre-posterior analysis 12.
  
  
  
  


• 12.6 Typical Problems in Assessment and Maintenance 12.
  
  
  • Example 12.4 – Reliability updating by material strength testing 12.
  
  
  • Example 12.5 – Reliability updating by proof load testing 12.
  
  
  • Example 12.6 – Reliability updating by indirect information 12.
  
  
  • Example 12.7 – Reliability updating by inspection of deterioration 12.
  
  
  
  


• 13 th Lecture – Risk Acceptance and Life Safety in Decision Making 13.


• 13.1 Introduction 13.
  
  
  • Fundamental societal value settings 13.
  
  
  • Preferences in decision making 13.
  
  
  
  


• 13.2 Commonly Applied Formats of Risk Acceptance 13.


• 13.3 Revealed Risks in Society 13.
  
  
  • Experienced life safety risks 13.
  
  
  • Experienced risks in selected commercial activities 13.
  
  
  
  


• 13.4 Life Saving – and the Performance of Society 13.


• 13.5 Modelling Socio-Economical Acceptable Risks 13.
  
  
  • The Life Quality Index 13.
  
  
  • The Societal Willingness To Pay (SWTP) as basis for acceptability criteria 13.
  
  
  • The Societal Value of a Statistical Life 13.
  
  
  
  


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• TOC-
  
  
  • Example 13.1 – Optimization of the design of a steel rod 13.
  
  
  
  


• 13.6 Sustainable Decision Making 13.
  
  
  • Indicators of sustainability 13.
  
  
  • Consequences to economy and society 13.
  
  
  • Consequences to the environment 13.
  
  
  • Intergenerational decision making 13.
  
  
  
  


• References R.


• Index I.


• Annex A – Tutorial for the JCSS Code Calibration Program CodeCal A.


• A.1 Introduction A.


• A.2 Installation of CodeCal A.


• A.3 Start CodeCal A.


• A.4 Examples A.


• A.5 References A.


• Annex B – General Considerations on the Planning of Experiments B.


• B.1 Introduction B.


• B.2 Modelling of Response Characteristics in Structural Engineering B.


• B.3 Hypothesis Testing and Planning of Experiments B.


• B.4 Reporting of Test Results B.