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E-grāmata: Construction Reliability: Safety, Variability and Sustainability

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  • Formāts: EPUB+DRM
  • Izdošanas datums: 07-Feb-2013
  • Izdevniecība: ISTE Ltd and John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781118601129
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Construction Reliability: Safety, Variability and SustainabilityPalielināt
  • Formāts: EPUB+DRM
  • Izdošanas datums: 07-Feb-2013
  • Izdevniecība: ISTE Ltd and John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781118601129
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Five parts of the book give answers to following questions: how to identify most probable critical failures? How to describe and use data concerning material, which are heterogeneous or time-variant or space-variant? How to quantify the reliability or the lifetime of a system? How to use feed back informations to actualize reliability results? How to optimize an inspection politic or a maintenance strategy? A dozen authors from public research centers or firms propose a synthesis of methods, well known and new, providing numerous examples: dams, geotechnical study, structures from nuclear or civil engineering.
Preface xiii
Julien Baroth
Franck Schoefs
Denys Breysse
Introduction xvii
Julien Baroth
Alaa Chateauneuf
Franck Schoefs
PART 1 QUALITATIVE METHODS FOR EVALUATING THE RELIABILITY OF CIVIL ENGINEERING STRUCTURES
1(52)
Introduction to Part 1
3(2)
Chapter 1 Methods for System Analysis and Failure Analysis
5(16)
Daniel Boissier
Laurent Peyras
Aurelie Talon
1.1 Introduction
5(2)
1.2 Structural analysis
7(3)
1.2.1 The sub-systems
7(1)
1.2.2 Environments
8(1)
1.2.3 Bounding the analysis
8(1)
1.2.4 Scales of a study
9(1)
1.3 Functional analysis
10(4)
1.3.1 Principles of functional analysis
10(1)
1.3.2 External functional analysis
11(1)
1.3.3 Internal functional analysis
12(2)
1.4 Failure Modes and Effects Analysis (FMEA)
14(5)
1.4.1 Principles of FMEA
14(2)
1.4.2 Process FMEA
16(1)
1.4.3 Product FMEA
17(2)
1.5 Bibliography
19(2)
Chapter 2 Methods for Modeling Failure Scenarios
21(16)
Daniel Boissier
Laurent Peyras
Aurelie Talon
2.1 Introduction
21(1)
2.2 Event tree method
22(2)
2.3 Fault tree method
24(2)
2.3.1 Information acquisition
24(1)
2.3.2 Fault tree construction
24(2)
2.4 Bow-tie method
26(3)
2.5 Criticality evaluation methods
29(5)
2.5.1 Criticality formulation
30(4)
2.5.2 Civil engineering considerations
34(1)
2.6 Bibliography
34(3)
Chapter 3 Application to a Hydraulic Civil Engineering Project
37(16)
Daniel Boissier
Laurent Peyras
Aurelie Talon
3.1 Context and approach for an operational reliability study
37(2)
3.2 Functional analysis and failure mode analysis
39(3)
3.2.1 Functional analysis of the system
39(2)
3.2.2 Failure mode analysis, and effects
41(1)
3.3 Construction of failure scenarios
42(2)
3.4 Scenario criticality analysis
44(6)
3.4.1 Hydrological study
44(1)
3.4.2 Hydraulic model and quantitative consequence analysis
45(1)
3.4.3 Evaluation of probability of technological failure
46(3)
3.4.4 Representing the criticality of a scenario
49(1)
3.5 Application summary
50(1)
3.6 Bibliography
51(2)
PART 2 HETEROGENEITY AND VARIABILITY OF MATERIALS: CONSEQUENCES FOR SAFETY AND RELIABILITY
53(66)
Introduction to Part 2
55(2)
Chapter 4 Uncertainties in Geotechnical Data
57(20)
Denys Breysse
Julien Baroth
Gilles Celeux
Aurelie Talon
Daniel Boissier
4.1 Various sources of uncertainty in geotechnical engineering
57(5)
4.1.1 Erratic terrain, light disorder and anthropogenic terrain
58(1)
4.1.2 Sources of uncertainty, errors, variability
58(3)
4.1.3 Correlations between material properties
61(1)
4.2 Erroneous, censored and sparse data
62(2)
4.2.1 Erroneous data
62(1)
4.2.2 Bounded data
63(1)
4.2.3 Sparse data
63(1)
4.3 Statistical representation of data
64(2)
4.3.1 Notation
64(2)
4.3.2 Spatial variability of material properties
66(1)
4.4 Data modeling
66(8)
4.4.1 Probabilistic and possibilistic approaches
67(1)
4.4.2 Useful random variables (Gaussian, Weibull)
68(2)
4.4.3 Maximum likelihood method
70(3)
4.4.4 Example: resistance measurements in concrete samples
73(1)
4.5 Conclusion
74(1)
4.6 Bibliography
74(3)
Chapter 5 Some Estimates on the Variability of Material Properties
77(20)
Denys Breysse
Antoine Marache
5.1 Introduction
77(1)
5.2 Mean value estimation
77(5)
5.2.1 Sampling and estimation
77(4)
5.2.2 Number of data points required for an estimate
81(1)
5.3 Estimation of characteristic values
82(4)
5.3.1 Characteristic value and fractile of a distribution
82(1)
5.3.2 Example: resistance measurements for wood samples
83(1)
5.3.3 Optimization of number of useful tests
84(1)
5.3.4 Estimate of in situ concrete mechanical strength
85(1)
5.4 Principles of a geostatistical study
86(10)
5.4.1 Geostatistical modeling tools
86(4)
5.4.2 Estimation and simulation methods
90(1)
5.4.3 Study of pressuremeter measurements in an urban environment
91(5)
5.5 Bibliography
96(1)
Chapter 6 Reliability of a Shallow Foundation Footing
97(22)
Denys Breysse
6.1 Introduction
97(1)
6.2 Bearing capacity models for strip foundations - modeling errors
98(3)
6.3 Effects of soil variability on variability in bearing capacity and safety of the foundation
101(8)
6.3.1 Methodology
101(3)
6.3.2 Purely frictional soil
104(2)
6.3.3 Soil with friction and cohesion
106(3)
6.4 Taking account of the structure of the spatial correlation and its influence on the safety of the foundation
109(6)
6.4.1 Spatial correlation and reduction in variance
109(3)
6.4.2 Taking account of the spatial correlation, and results
112(3)
6.5 Conclusions
115(2)
6.5.1 Conclusions drawn from case study
115(1)
6.5.2 General conclusions
116(1)
6.6 Bibliography
117(2)
PART 3 METAMODELS FOR STRUCTURAL RELIABILITY
119(50)
Introduction to Part 3
121(2)
Chapter 7 Physical and Polynomial Response Surfaces
123(24)
Frederic Duprat
Franck Schoefs
Bruno Sudret
7.1 Introduction
123(1)
7.2 Background to the response surface method
124(1)
7.3 Concept of a response surface
125(6)
7.3.1 Basic definitions
125(1)
7.3.2 Various formulations
126(1)
7.3.3 Building criteria
127(4)
7.4 Usual reliability methods
131(2)
7.4.1 Reliability issues and Monte Carlo simulation
131(1)
7.4.2 FORM
131(2)
7.5 Polynomial response surfaces
133(10)
7.5.1 Basic formulation
133(2)
7.5.2 Working space
135(1)
7.5.3 Response surface expression
135(1)
7.5.4 Building the numerical experimental design
136(2)
7.5.5 Example of an adaptive RS method
138(5)
7.6 Conclusion
143(1)
7.7 Bibliography
143(4)
Chapter 8 Response Surfaces based on Polynomial Chaos Expansions
147(22)
Bruno Sudret
Geraud Blatman
Marc Berveiller
8.1 Introduction
147(2)
8.1.1 Statement of the reliability problem
147(1)
8.1.2 From Monte Carlo simulation to polynomial chaos expansions
148(1)
8.2 Building of a polynomial chaos basis
149(2)
8.2.1 Orthogonal polynomials
149(1)
8.2.2 Example
150(1)
8.3 Computation of the expansion coefficients
151(7)
8.3.1 Introduction
151(2)
8.3.2 Projection methods
153(1)
8.3.3 Regression methods
154(3)
8.3.4 Post-processing of the coefficients
157(1)
8.4 Applications in structural reliability
158(6)
8.4.1 Elastic engineering truss
158(3)
8.4.2 Frame structure
161(3)
8.5 Conclusion
164(1)
8.6 Bibliography
165(4)
PART 4 METHODS FOR STRUCTURAL RELIABILITY OVER TIME
169(80)
Introduction to Part 4
171(2)
Chapter 9 Data Aggregation and Unification
173(14)
Daniel Boissier
Aurelie Talon
9.1 Introduction
173(1)
9.2 Methods of data aggregation and unification
173(8)
9.2.1 Data unification methods
175(4)
9.2.2 Data aggregation methods
179(2)
9.3 Evaluation of evacuation time for an apartment in case of fire
181(4)
9.4 Conclusion
185(1)
9.5 Bibliography
185(2)
Chapter 10 Time-Variant Reliability Problems
187(20)
Bruno Sudret
10.1 Introduction
187(1)
10.2 Random processes
188(4)
10.2.1 Definition and elementary properties
188(2)
10.2.2 Gaussian random processes
190(1)
10.2.3 Poisson and rectangular wave renewal processes
190(2)
10.3 Time-variant reliability problems
192(5)
10.3.1 Problem statement
192(1)
10.3.2 Right-boundary problems
193(1)
10.3.3 General case
194(3)
10.4 PHI2 method
197(5)
10.4.1 Implementation of the PHI2 method - stationary case
198(2)
10.4.2 Implementation of the PHI2 method - non-stationary case
200(1)
10.4.3 Semi-analytical example
200(2)
10.5 Industrial application: truss structure under time-varying loads
202(2)
10.6 Conclusion
204(1)
10.7 Bibliography
205(2)
Chapter 11 Bayesian Inference and Markov Chain Monte Carlo Methods
207(20)
Gilles Celeux
11.1 Introduction
207(1)
11.2 Bayesian Inference
208(2)
11.2.1 Bayesian estimation of the mean of a Gaussian distribution
209(1)
11.3 MCMC methods for weakly informative data
210(9)
11.3.1 Weakly informative statistical problems
210(1)
11.3.2 From prior information to prior distributions
211(1)
11.3.3 Approximating a posterior distribution
212(1)
11.3.4 A popular MCMC method: Gibbs sampling
213(1)
11.3.5 Metropolis-Hastings algorithm
214(3)
11.3.6 Assessing the convergence of an MCMC algorithm
217(1)
11.3.7 Importance sampling
218(1)
11.4 Estimating a competing risk model from censored and incomplete data
219(6)
11.4.1 Choosing the prior distributions
220(1)
11.4.2 From prior information to prior hyperparameters
221(1)
11.4.3 Gibbs sampling
221(1)
11.4.4 Adaptive Importance Sampling (AIS)
222(3)
11.5 Conclusion
225(1)
11.6 Bibliography
225(2)
Chapter 12 Bayesian Updating Techniques in Structural Reliability
227(22)
Bruno Sudret
12.1 Introduction
227(1)
12.2 Problem statement: link between measurements and model prediction
228(1)
12.3 Computing and updating the failure probability
229(4)
12.3.1 Structural reliability - problem statement
229(3)
12.3.2 Updating failure probability
232(1)
12.4 Updating a confidence interval on response quantities
233(2)
12.4.1 Quantiles as the solution of an inverse reliability problem
233(1)
12.4.2 Updating quantiles of the response quantity
234(1)
12.4.3 Conclusion
234(1)
12.5 Bayesian updating of the model basic variables
235(3)
12.5.1 A reminder of Bayesian statistics
235(1)
12.5.2 Bayesian updating of the model basic variables
235(3)
12.6 Updating the prediction of creep strains in containment vessels of nuclear power plants
238(7)
12.6.1 Industrial problem statement
238(1)
12.6.2 Deterministic models
239(3)
12.6.3 Prior and posterior estimations of the delayed strains
242(3)
12.7 Conclusion
245(1)
12.8 Acknowledgments
246(1)
12.9 Bibliography
246(3)
PART 5 RELIABILITY-BASED MAINTENANCE OPTIMIZATION
249(66)
Introduction to Part 5
251(2)
Chapter 13 Maintenance Policies
253(18)
Alaa Chateauneuf
Franck Schoefs
Bruno Capra
13.1 Maintenance
253(4)
13.1.1 Lifetime distribution
253(1)
13.1.2 Maintenance cycle
254(1)
13.1.3 Maintenance planning
255(2)
13.2 Types of maintenance
257(5)
13.2.1 Choice of the maintenance policy
257(3)
13.2.2 Maintenance program
260(1)
13.2.3 Inspection program
261(1)
13.3 Maintenance models
262(7)
13.3.1 Model of perfect maintenance: AGAN
263(1)
13.3.2 Model of minimal maintenance: ABAO
264(1)
13.3.3 Model of imperfect or bad maintenance: BTO/WTO
265(2)
13.3.4 Complex maintenance policy
267(2)
13.4 Conclusion
269(1)
13.5 Bibliography
269(2)
Chapter 14 Maintenance Cost Models
271(22)
Alaa Chateauneuf
Franck Schoefs
14.1 Preventive maintenance
271(2)
14.2 Maintenance based on time
273(2)
14.2.1 Model I
274(1)
14.2.2 Model II
274(1)
14.2.3 Model III
275(1)
14.3 Maintenance based on age
275(1)
14.4 Inspection models
276(7)
14.4.1 Impact of inspection on costs
276(1)
14.4.2 The case of imperfect inspections
277(6)
14.5 Structures with large lifetimes
283(1)
14.6 Criteria for choosing a maintenance policy
284(1)
14.7 Example of a corroded steel pipeline
285(5)
14.8 Conclusion
290(1)
14.9 Bibliography
290(3)
Chapter 15 Practical Aspects: Industrial Implementation and Limitations in a Multi-criteria Context
293(22)
Franck Schoefs
Bruno Capra
15.1 Introduction
293(3)
15.2 Motorway concession with high performance requirements
296(7)
15.2.1 Background and stakes
296(2)
15.2.2 Methodology
298(2)
15.2.3 Results
300(3)
15.3 Ageing of civil engineering structures: using field data to update predictions
303(4)
15.3.1 Background and stakes
303(1)
15.3.2 Corrosion risk of a cooling tower
303(2)
15.3.3 Bayesian updating
305(2)
15.4 Conclusion
307(1)
15.5 Bibliography
308(7)
Conclusion
311(4)
Julien Baroth
Franck Schoefs
Denys Breysse
List of Symbols 315(8)
List of Authors 323(2)
Index 325
Julien Baroth is a professor at the?IUT Laboratoire of Grenoble University in?France.

Denys Breysse is a professor?in the Department of Civil and Environmental Engineering (GCE) at Bordeaux 1 University's Institute of Mechanics and Engineering (I2M) in France.

D. Franck Schoefs is a professor at the Institute for Research in Civil and Mechanical Engineering (GeM)?of Nantes University in France.
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