Contents
Cover
Half title
Title page
Imprints page
Dedication
Contents
Preface
Acknowledgments
1 Introduction
1.1 Introduction
1.2 Brief History of the Field
1.3 The Nature of Uncertainties and Probability
1.4 Objectives
1.5 Software
1.6 Organization of Chapters
2 Review of Probability Theory
2.1 Introduction
2.2 Elements of Set Theory
2.3 Basic Rules of Probability Theory
2.4 Random Variable
2.5 Reliability and Hazard Functions
2.6 Multiple Random Variables
2.7 Expectation and Moments
2.8 Distribution of Functions of Random Variables
2.9 Second Moments of Functions of Random Variables
2.10 Extreme-Value Distributions
2.11 Probability Distribution Models
Appendix 2A Probability Distribution Models
PROBLEMS
3 Multivariate Distributions
3.1 Introduction
3.2 The Multinormal Distribution
3.3 The Multivariate Lognormal Distribution
3.4 Joint Distribution as Product of Conditionals
3.5 Multivariate Distributions with Prescribed Marginals
3.5.1 Morgenstern Family of Multivariate Distributions
3.5.2 Nataf Family of Multivariate Distributions
Remark
3.5.3 Copula Distributions
Remark
3.6 Transformation to the Standard Normal Space
3.6.1 Single Random Variable
3.6.2 Statistically Independent Random Variables
3.6.3 Multinormal Random Variables
3.6.4 Nataf-Distributed Random Variables
3.6.5 General Non-Normal Random Variables
Appendix 3A Correlation Coefficients for Nataf Distribution
Appendix 3B Brief on Cholesky Decomposition
PROBLEMS
4 Formulation of Structural Reliability
4.1 Introduction
4.2 The R-S Reliability Problem
4.2.1 Solution by Conditioning on S
4.2.2 Solution by Conditioning on R
4.2.3 Formulation in Terms of Safety Margin
4.2.4 Formulation in Terms of Safety Factor
4.3 The Tail-Sensitivity Problem
4.4 The Generalized Structural Reliability Problem
4.5 Concluding Remarks
PROBLEMS
5 Analysis of Structural Reliability Under Incomplete Probability Information
5.1 Introduction
5.2 Second-Moment Reliability Methods
5.2.1 The Mean-Centered, First-Order, Second-Moment Reliability Method
5.2.2 The First-Order, Second-Moment Reliability Method
5.2.3 Algorithm for Finding the Design Point
5.2.4 The Generalized (Second-Moment) Reliability Index
5.3 Reliability Methods with Beyond Second-Moment Information
5.3.1 Knowledge of Third and Fourth Moments
5.3.2 Knowledge of Marginal Distributions
5.3.3 Reliability Index Based on Upper Chebyshev Bound
5.4 Concluding Remarks
PROBLEMS
6 The First-Order Reliability Method
6.1 Introduction
6.2 Properties of the Standard Normal Space
6.3 The First-Order Reliability Method
6.4 Accuracy of the FORM Approximation
6.5 FORM Measures of Importance of Random Variables
6.6 FORM Parameter Sensitivities
6.7 Sensitivities with Respect to Alternative Set of Parameters
6.8 Importance Vectors with Respect to Means and Standard Deviations
6.9 Multiple Design Points
6.10 The Inverse Reliability Problem
6.11 FORM Approximation of the CDF and PDF of a Function of Random Variables
6.12 Concluding Remarks
PROBLEMS
7 The Second-Order Reliability Method
7.1 Introduction
7.2 Classical Formulation of the Second-Order Reliability Method
7.3 Gradient-Based SORM
7.4 Point-Fitting SORM
7.5 Concluding Remarks
PROBLEMS
8 System Reliability
8.1 Introduction
8.2 Representation of Systems
8.3 Definition of System Reliability
8.4 System Reliability by Expectation
8.5 System Reliability by the Inclusion–Exclusion Rule
8.6 Bounds on Series-System Reliability
8.7 Bounds on System Reliability by Linear Programming
8.8 Matrix-Based System Reliability Method
8.9 Formulation of Structural System Reliability
8.10 First-Order Approximations for Series and Parallel Systems
8.11 Bi-Component Bounds for FORM Approximation of Series Systems
8.12 Event-Tree Approach for Modeling Sequential Failures
8.13 Component Importance Measures
Structural Important Measure
Marginal Important Measure
Risk Achievement Worth Measure
Risk Reduction Worth Measure
Fussell–Vesely Measure
Conditional Probability Measure
Upgrade Worth Measure
8.14 System Sensitivity Measures
8.15 Concluding Remarks
PROBLEMS
9 Simulation Methods
9.1 Introduction
9.2 Generation of Pseudorandom Numbers with Uniform Distribution
9.3 Generation of Pseudorandom Numbers with Specified Distribution
9.4 Generation of Pseudorandom Numbers for Dependent Random Variables
9.5 Monte Carlo Simulation
9.6 Use of Antithetic Variates
9.7 Importance Sampling
9.8 Numerical Integration by Importance Sampling
9.9 Directional Sampling
9.10 Orthogonal-Plane Sampling
9.11 Subset Simulation
9.12 Reliability Sensitivities by Simulation
9.13 Concluding Remarks
PROBLEMS
10 Bayesian Parameter Estimation and Reliability Updating
10.1 Introduction
10.2 Sources and Types of Uncertainties
10.3 Bayesian Parameter Estimation
10.3.1 Formulation of the Likelihood Function
10.3.2 Selection of Prior Distribution
10.3.3 Conjugate Priors for the Normal Distribution
10.4 Assessing Mathematical Models of Physical Phenomena
10.4.1 Formulation of Likelihood Function for Model Assessment
10.5 Analysis of Structural Reliability under Statistical and Model Uncertainties
10.6 Updating of Structural Reliability
10.7 Updating the Distribution of Basic Random Variables
10.8 Concluding Remarks
Appendix 10A Conjugate-Pair Distributions
Problems
11 Time- and Space-Variant Reliability Analysis
11.1 Introduction
11.2 Review of Random Processes
11.3 Power-Spectral Density of a Stationary Process
11.4 The Gaussian Process
11.5 Solution Approaches for Reliability Analysis
11.5.1 Upper-Bound Solution
11.5.2 Lower-Bound Solution
11.6 The Poisson Process
11.6.1 Poisson Process with Random Selections
11.6.2 Waiting and Interarrival Times in a Poisson Process
11.6.3 Poisson Approximation for Time- and Space-Variant Reliability Problems
11.7 Stochastic Load Models and Load Combination
11.7.1 Ferry Borges–Castanheta Load Model
11.7.2 The Filtered Poisson Model
11.7.3 The Poisson Square-Wave Process
11.7.4 The Poisson Pulse Process
11.8 Combination of Homogeneous Poisson Pulse Load Processes
11.9 Concluding Remarks
Appendix 11A Derivation of Limit Formula for Mean Down-Crossing Rate
PROBLEMS
12 Finite-Element Reliability Methods
12.1 Introduction
12.2 Brief Review of the Finite-Element Formulation
12.3 Formulation of the Finite-Element Reliability Problem
12.4 The Direct-Differentiation Method
12.5 Discrete Representation of Random Fields
12.6 The Spectral Stochastic Finite-Element Method
12.7 Response-Surface Methods
12.8 Concluding Remarks
13 Reliability Methods for Stochastic Structural Dynamics
13.1 Introduction
13.2 Discrete Representation of Random Processes
13.3 Response of Linear System to Gaussian Excitation
13.4 Response of Linear System to Non-Gaussian Excitation
13.5 Tail-Equivalent Linearization for Nonlinear Stochastic Dynamic Analysis
13.5.1 Properties of the Tail-Equivalent Linear System
13.6 Level Crossings of the Response Process
13.7 The First-Passage Probability
13.8 TELM with Multiple Excitations
13.9 Evolutionary TELM
13.10 Concluding Remarks
14 Reliability-Based Design Optimization
14.1 Introduction
14.2 Problem Formulation
14.3 Solution by the Decoupling Approach
14.3.1 Solution of Problems P1 and P1,sys
14.3.2 Solution of Problems P2 and P2,sys
14.3.3 Solution of Problems P3 and P3,sys
14.4 Sampling-Based RBDO
14.5 RBDO Employing Surrogate Models
14.6 Buffered Failure Probability Approach
14.7 Concluding Remarks
15 Bayesian Network for Reliability Assessment and Updating
15.1 Introduction
15.2 Elements of a Bayesian Network
15.3 D-Separation Rules
15.4 Discretization of Continuous Random Variables
15.5 Inference in Bayesian Network
15.6 BN Modeling of Components
15.7 BN Modeling of Systems
15.8 BN Modeling of Random Fields
15.9 Dynamic Bayesian Network
15.10 Bayesian Network Enhanced by Structural Reliability Methods
15.11 Concluding Remarks
References
Index
Downloads
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