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Structural and System Reliability [Kõva köide]

(University of California, Berkeley)
  • Formaat: Hardback, 610 pages, kõrgus x laius x paksus: 253x193x33 mm, kaal: 1430 g, Worked examples or Exercises
  • Ilmumisaeg: 13-Jan-2022
  • Kirjastus: Cambridge University Press
  • ISBN-10: 1108834140
  • ISBN-13: 9781108834148
Teised raamatud teemal:
Structural and System ReliabilitySuurem pilt
  • Formaat: Hardback, 610 pages, kõrgus x laius x paksus: 253x193x33 mm, kaal: 1430 g, Worked examples or Exercises
  • Ilmumisaeg: 13-Jan-2022
  • Kirjastus: Cambridge University Press
  • ISBN-10: 1108834140
  • ISBN-13: 9781108834148
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781108834148.html
Märksõnad:
Based on material taught at the University of California, Berkeley, this textbook offers a modern, rigorous and comprehensive treatment of the methods of structural and system reliability analysis. It covers the first- and second-order reliability methods for components and systems, simulation methods, time- and space-variant reliability, and Bayesian parameter estimation and reliability updating. It also presents more advanced, state-of-the-art topics such as finite-element reliability methods, stochastic structural dynamics, reliability-based optimal design, and Bayesian networks. A wealth of well-designed examples connect theory with practice, with simple examples demonstrating mathematical concepts and larger examples demonstrating their applications. End-of-chapter homework problems are included throughout. Including all necessary background material from probability theory, and accompanied online by a solutions manual and PowerPoint slides for instructors, this is the ideal text for senior undergraduate and graduate students taking courses on structural and system reliability in departments of civil, environmental and mechanical engineering.

Offers a rigorous and comprehensive treatment of reliability analysis of structures and systems using numerous examples and end-of-chapter problems. Accompanied online by a solutions manual and PowerPoint slides, it is the ideal textbook for senior undergraduate and graduate courses in departments of civil, environmental and mechanical engineering.

Arvustused

'This textbook is invaluable to all concerned with structural and system reliability analysis. Providing a modern, rigorous, and comprehensive treatment of the theory and practice, it is a must-read for students, scholars, and practitioners learning, researching, or practicing in this field.' Dan M. Frangopol, Lehigh University 'This book is a welcome addition to the literature on structural reliability, which in recent decades has become essential to structural engineering practice. Authored by a leading international contributor to the mathematical foundations of structural reliability, much of the coverage has been informed by his own research and teaching. Readers wanting a comprehensive overview of the field, with its diverse problems, applications, and challenges, will find this volume indispensable for their technical libraries.' Bruce R. Ellingwood, Colorado State University 'This monograph is a manifest of the impressive contributions of the author and his students to the philosophy and the computationally practicable methods of first- and second-order structural reliability evaluation, necessarily resting on a Bayesian interpretation of probability applied to any type of uncertainty. The history of the field is fairly described, along with the step-by-step unfolding of the theory. The large number of examples and problems makes the book well suited for self-study.' Ove Ditlevsen, Technical University of Denmark 'The long-awaited textbook from one of the great teachers of structural reliability and stochastic mechanics! Professor Der Kiureghian wrote a book that is comprehensive and thorough, but also easily accessible to those new to the field. It is already a classic and an invaluable source for all students, researchers, and practitioners of structural reliability methods.' Daniel Straub, Technical University of Munich 'Professor Der Kiureghian has been one of the fathers of structural and system reliability analysis since the early 1980s and has devoted his entire career to research and education on this topic. No need to say that this book has been expected for a long time by the community! In this textbook, the reader will find elements of probability theory and the basics on structural reliability analysis (FORM/SORM, simulation methods, reliability updating, systems analysis), as well as state-of-the-art methods in stochastic structural dynamics, reliability-based design optimization and more, making it a must-have both for graduate students in engineering, researchers, and practitioners in the field. The crystal-clear writing style and the numerous exercises provided in each chapter make it the perfect reference for teaching.' Bruno Sudret, ETH Zurich 'Because of its focus on the fundamentals, this book will remain relevant and current for many years to come, even as the state of the art of structural reliability and its integration with behavioral sciences in decision-making continues to evolve. Readers wanting a comprehensive overview of the field, with its diverse problems, applications and challenges will find the volume to be an indispensable addition to their technical libraries.' Bruce R. Ellingwood, Structural Safety

Muu info

Offers a modern, rigorous and comprehensive treatment of the subject using numerous well-designed examples and end-of-chapter problems.
Preface xiii
Acknowledgments xv
1 Introduction
1(10)
1.1 Introduction
1(1)
1.2 Brief History of the Field
2(2)
1.3 The Nature of Uncertainties and Probability
4(1)
1.4 Objectives
5(1)
1.5 Software
6(2)
1.6 Organization of
Chapters
8(3)
2 Review of Probability Theory
11(47)
2.1 Introduction
11(1)
2.2 Elements of Set Theory
11(3)
2.3 Basic Rules of Probability Theory
14(6)
2.4 Random Variable
20(4)
2.5 Reliability and Hazard Functions
24(2)
2.6 Multiple Random Variables
26(5)
2.7 Expectation and Moments
31(3)
2.8 Distribution of Functions of Random Variables
34(4)
2.9 Second Moments of Functions of Random Variables
38(4)
2.10 Extreme-Value Distributions
42(7)
2.11 Probability Distribution Models
49(9)
Appendix 2A Probability Distribution Models
50(4)
Problems
54(4)
3 Multivariate Distributions
58(35)
3.1 Introduction
58(1)
3.2 The Multinomial Distribution
58(4)
3.3 The Multivariate Lognormal Distribution
62(3)
3.4 Joint Distribution as Product of Conditionals
65(3)
3.5 Multivariate Distributions with Prescribed Marginals
68(11)
3.5.1 Morgenstern Family of Multivariate Distributions
68(4)
3.5.2 Nataf Family of Multivariate Distributions
72(4)
3.5.3 Copula Distributions
76(3)
3.6 Transformation to the Standard Normal Space
79(14)
3.6.1 Single Random Variable
80(1)
3.6.2 Statistically Independent Random Variables
81(1)
3.6.3 Multinomial Random Variables
82(1)
3.6.4 Nataf-Distributed Random Variables
83(1)
3.6.5 General Non-Normal Random Variables
84(3)
Appendix 3A Correlation Coefficients for Nataf Distribution
87(3)
Appendix 3B Brief on Cholesky Decomposition
90(1)
Problems
91(2)
4 Formulation of Structural Reliability
93(15)
4.1 Introduction
93(1)
4.2 The R-S Reliability Problem
93(8)
4.2.1 Solution by Conditioning on S
94(1)
4.2.2 Solution by Conditioning on R
95(1)
4.2.3 Formulation in Terms of Safety Margin
96(2)
4.2.4 Formulation in Terms of Safety Factor
98(3)
4.3 The Tail-Sensitivity Problem
101(1)
4.4 The Generalized Structural Reliability Problem
102(4)
4.5 Concluding Remarks
106(2)
Problems
107(1)
5 Analysis of Structural Reliability Under Incomplete Probability Information
108(27)
5.1 Introduction
108(1)
5.2 Second-Moment Reliability Methods
109(14)
5.2.1 The Mean-Centered, First-Order, Second-Moment Reliability Method
109(2)
5.2.2 The First-Order, Second-Moment Reliability Method
111(3)
5.2.3 Algorithm for Finding the Design Point
114(7)
5.2.4 The Generalized (Second-Moment) Reliability Index
121(2)
5.3 Reliability Methods with Beyond Second-Moment Information
123(8)
5.3.1 Knowledge of Third and Fourth Moments
124(3)
5.3.2 Knowledge of Marginal Distributions
127(3)
5.3.3 Reliability Index Based on Upper Chebyshev Bound
130(1)
5.4 Concluding Remarks
131(4)
Problems
133(2)
6 The First-Order Reliability Method
135(50)
6.1 Introduction
135(1)
6.2 Properties of the Standard Normal Space
136(3)
6.3 The First-Order Reliability Method
139(10)
6.4 Accuracy of the FORM Approximation
149(4)
6.5 FORM Measures of Importance of Random Variables
153(5)
6.6 FORM Parameter Sensitivities
158(5)
6.7 Sensitivities with Respect to Alternative Set of Parameters
163(2)
6.8 Importance Vectors with Respect to Means and Standard Deviations
165(2)
6.9 Multiple Design Points
167(5)
6.10 The Inverse Reliability Problem
172(3)
6.11 FORM Approximation of the CDF and PDF of a Function of Random Variables
175(2)
6.12 Concluding Remarks
177(8)
Problems
179(6)
7 The Second-Order Reliability Method
185(15)
7.1 Introduction
185(1)
7.2 Classical Formulation of the Second-Order Reliability Method
185(4)
7.3 Gradient-Based SORM
189(4)
7.4 Point-Fitting SORM
193(4)
7.5 Concluding Remarks
197(3)
Problems
198(2)
8 System Reliability
200(62)
8.1 Introduction
200(1)
8.2 Representation of Systems
201(5)
8.3 Definition of System Reliability
206(1)
8.4 System Reliability by Expectation
206(3)
8.5 System Reliability by the Inclusion-Exclusion Rule
209(3)
8.6 Bounds on Series-System Reliability
212(3)
8.7 Bounds on System Reliability by Linear Programming
215(6)
8.8 Matrix-Based System Reliability Method
221(3)
8.9 Formulation of Structural System Reliability
224(1)
8.10 First-Order Approximations for Series and Parallel Systems
225(7)
8.11 Bi-Component Bounds for FORM Approximation of Series Systems
232(6)
8.12 Event-Tree Approach for Modeling Sequential Failures
238(7)
8.13 Component Importance Measures
245(7)
8.14 System Sensitivity Measures
252(4)
8.15 Concluding Remarks
256(6)
Problems
258(4)
9 Simulation Methods
262(39)
9.1 Introduction
262(1)
9.2 Generation of Pseudorandom Numbers with Uniform Distribution
263(1)
9.3 Generation of Pseudorandom Numbers with Specified Distribution
264(1)
9.4 Generation of Pseudorandom Numbers for Dependent Random Variables
265(1)
9.5 Monte Carlo Simulation
266(4)
9.6 Use of Antithetic Variates
270(1)
9.7 Importance Sampling
271(8)
9.8 Numerical Integration by Importance Sampling
279(4)
9.9 Directional Sampling
283(5)
9.10 Orthogonal-Plane Sampling
288(2)
9.11 Subset Simulation
290(4)
9.12 Reliability Sensitivities by Simulation
294(4)
9.13 Concluding Remarks
298(3)
Problems
300(1)
10 Bayesian Parameter Estimation and Reliability Updating
301(56)
10.1 Introduction
301(1)
10.2 Sources and Types of Uncertainties
302(2)
10.3 Bayesian Parameter Estimation
304(15)
10.3.1 Formulation of the Likelihood Function
306(3)
10.3.2 Selection of Prior Distribution
309(4)
10.3.3 Conjugate Priors for the Normal Distribution
313(6)
10.4 Assessing Mathematical Models of Physical Phenomena
319(8)
10.4.1 Formulation of Likelihood Function for Model Assessment
320(7)
10.5 Analysis of Structural Reliability under Statistical and Model Uncertainties
327(13)
10.6 Updating of Structural Reliability
340(6)
10.7 Updating the Distribution of Basic Random Variables
346(2)
10.8 Concluding Remarks
348(9)
Appendix 10A Conjugate-Pair Distributions
350(4)
Problems
354(3)
11 Time- and Space-Variant Reliability Analysis
357(48)
11.1 Introduction
357(4)
11.2 Review of Random Processes
361(5)
11.3 Power-Spectral Density of a Stationary Process
366(3)
11.4 The Gaussian Process
369(1)
11.5 Solution Approaches for Reliability Analysis
370(10)
11.5.1 Upper-Bound Solution
370(8)
11.5.2 Lower-Bound Solution
378(2)
11.6 The Poisson Process
380(8)
11.6.1 Poisson Process with Random Selections
385(1)
11.6.2 Waiting and Interarrival Times in a Poisson Process
385(2)
11.6.3 Poisson Approximation for Time- and Space-Variant Reliability Problems
387(1)
11.7 Stochastic Load Models and Load Combination
388(7)
11.7.1 Ferry Borges-Castanheta Load Model
389(2)
11.7.2 The Filtered Poisson Model
391(2)
11.7.3 The Poisson Square-Wave Process
393(1)
11.7.4 The Poisson Pulse Process
394(1)
11.8 Combination of Homogeneous Poisson Pulse Load Processes
395(6)
11.9 Concluding Remarks
401(4)
Appendix 11A Derivation of Limit Formula for Mean Down-Crossing Rate
402(1)
Problems
403(2)
12 Finite-Element Reliability Methods
405(41)
12.1 Introduction
405(2)
12.2 Brief Review of the Finite-Element Formulation
407(2)
12.3 Formulation of the Finite-Element Reliability Problem
409(4)
12.4 The Direct-Differentiation Method
413(12)
12.5 Discrete Representation of Random Fields
425(10)
12.6 The Spectral Stochastic Finite-Element Method
435(2)
12.7 Response-Surface Methods
437(7)
12.8 Concluding Remarks
444(2)
13 Reliability Methods for Stochastic Structural Dynamics
446(47)
13.1 Introduction
446(1)
13.2 Discrete Representation of Random Processes
447(5)
13.3 Response of Linear System to Gaussian Excitation
452(3)
13.4 Response of Linear System to Non-Gaussian Excitation
455(4)
13.5 Tail-Equivalent Linearization for Nonlinear Stochastic Dynamic Analysis
459(10)
13.5.1 Properties of the Tail-Equivalent Linear System
464(5)
13.6 Level Crossings of the Response Process
469(4)
13.7 The First-Passage Probability
473(7)
13.8 TELM with Multiple Excitations
480(6)
13.9 Evolutionary TELM
486(4)
13.10 Concluding Remarks
490(3)
14 Reliability-Based Design Optimization
493(30)
14.1 Introduction
493(4)
14.2 Problem Formulation
497(3)
14.3 Solution by the Decoupling Approach
500(14)
14.3.1 Solution of Problems P1 and P1,Sys
500(5)
14.3.2 Solution of Problems P2 and P2,Sys
505(2)
14.3.3 Solution of Problems P3 and P3,sys
507(7)
14.4 Sampling-Based RBDO
514(4)
14.5 RBDO Employing Surrogate Models
518(2)
14.6 Buffered Failure Probability Approach
520(2)
14.7 Concluding Remarks
522(1)
15 Bayesian Network for Reliability Assessment and Updating
523(49)
15.1 Introduction
523(1)
15.2 Elements of a Bayesian Network
524(3)
15.3 D-Separation Rules
527(1)
15.4 Discretization of Continuous Random Variables
528(2)
15.5 Inference in Bayesian Network
530(9)
15.6 BN Modeling of Components
539(2)
15.7 BN Modeling of Systems
541(4)
15.8 BN Modeling of Random Fields
545(11)
15.9 Dynamic Bayesian Network
556(5)
15.10 Bayesian Network Enhanced by Structural Reliability Methods
561(10)
15.11 Concluding Remarks
571(1)
References 572(15)
Index 587
Armen Der Kiureghian is the Taisei Professor of Civil Engineering Emeritus at the University of California, Berkeley, and a co-founder and President Emeritus of the American University of Armenia. He is also a Distinguished Alumnus of the Department of Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign, and an elected member of the US National Academy of Engineering.