14th International Probabilistic Workshop (eBook)

Preface 5
Organization 7
Chair of IPW2016 7
Scientific Committee 7
Contents 9
Keynotes 13
1 Optimizing Adaptable Systems for Future Uncertainty 14
Abstract 14
1 Introduction 14
2 Adaptable or Flexible Engineering Systems 15
2.1 A Measure of Flexibility 16
3 Sequential Decision Analysis 17
4 Numerical Illustrations 18
4.1 Case 1: Infrastructure Capacity 18
4.2 Case 2: Disaster Risk Management 21
5 Concluding Remarks 23
References 23
2 Freak Events, Black Swans, and Unknowable Unknowns: Impact on Risk-Based Design 25
Abstract 25
1 Introduction 26
2 Infrastructure: Evolving Expectations 26
3 What We Know, What We Should Know, What We Don’t Know 27
4 Black Swans and Perfect Storms 29
5 The “Very” Extreme 31
6 The Carlsbad Black Swan: El Paso Natural Gas Pipeline Rupture, 19 August, 2000 32
7 The Fukushima Daiichi Perfect Storm, 11 March 2011 35
8 Conclusions: Demystifying the Extraordinary 37
References 39
Structural Reliability Methods and Statistical Approaches 41
Extrapolation, Invariance, Geometry and Subset Sampling 42
1 Introduction 42
2 The Subset Sampling Method 43
3 SuS and Asymptotic Approximations 44
4 Extrapolation 46
5 Invariance 47
6 Changing Topological Structure of Domains 48
7 Several Beta Points 49
8 Bias and Variance of SuS Estimates 50
9 Conclusions 50
References 52
4 Performance of Various Sampling Schemes in Asymptotic Sampling 53
Abstract 53
1 Introduction 53
2 Testing Limit-State Functions 55
2.1 Limit-State Function Sum1D 55
2.2 Limit-State Function Sum2D 55
2.3 Limit-State Function Sin2D 56
3 Asymptotic Sampling (AS) 56
4 Design of Experiment 58
4.1 Monte Carlo (MC) Sampling 58
4.2 Latin Hypercube Sampling (LHS) 58
4.3 LHS Optimized—Periodic Audze-Egl?js (PAE) Criterion 59
4.4 Quasi-Monte Carlo (QMC) Sequences 60
4.5 The Sobol Sequence 61
5 Results 61
5.1 Limit-State Function Sum1D 62
5.2 Limit-State Function Sum2D 64
5.3 Limit-State Function Sin2D 64
6 Concluding Remarks 66
Acknowledgments 68
References 68
Moving Least Squares Metamodels---Hyperparameter, Variable Reduction and Model Selection 70
1 Introduction 71
2 From Least Squares to Moving Least Squares 73
2.1 Linear Regression Model 73
2.2 Least Squares 74
2.3 Weighted Least Squares 74
2.4 Moving Least Squares 75
3 Settings of WLS and MLS 75
3.1 Model Function f (b(x1 , ƒ, xnk)) 75
3.2 Weighting Matrix 76
4 MLS Model Tuning 79
4.1 Tuning of Hyperparameters 79
4.2 Variable Reduction 80
5 Framework of Deterministic Models 82
5.1 Implemented Models 83
5.2 Design of Experiments (DOE) 83
6 Evaluation of LS and MLS Metamodels 84
7 Summary and Outlook 85
References 86
6 Comparing Three Methodologies for System Identification and Prediction 88
Abstract 88
1 Introduction 90
2 Structural Identification Methodologies 90
2.1 Traditional Bayesian Model Updating 91
2.2 Error-Domain Model Falsification 92
2.3 Modified Bayesian Model Updating 93
3 Numerical Example 94
4 Conclusion 100
References 100
7 Global Sensitivity Analysis of Reinforced Concrete Walls Subjected to Standard Fire—A Comparison of Methods 103
Abstract 103
1 Introduction 104
2 Applied Methods 104
2.1 Monte Carlo Simulation of Reinforced Concrete Walls 104
2.2 Sensitivity Analysis 107
2.2.1 Spearman’s Rank Order Correlation Coefficients 107
2.2.2 Conceptual Implementation for the Estimation of First Order Sobol Indices 107
2.2.3 “Matrix Method” for the Estimation of First Order Sobol Indices 108
3 Results 110
4 Conclusions 111
References 112
Probability and Statistics 113
8 Comparison of Computed and Observed Probabilities of Failure and Core Damage Frequencies 114
Abstract 114
1 Introduction 114
2 Comparison of Observed and Computed Probabilities of Failure 115
2.1 History 115
2.2 Concept 115
2.3 Goal Values 116
2.4 Observed Values 117
3 Comparison of Observed and Computed Core Damage Frequencies 120
3.1 History 120
3.2 Concept 121
3.3 Goal Values 121
3.4 Observed Values 121
3.5 Comparison 123
4 Conclusion 125
References 126
9 Probability of a Large Fire in a Road Tunnel Bayesian Inference 128
Abstract 128
1 Introduction 128
1.1 Probability of Fire 129
1.2 Developments 129
1.3 Scope 130
1.4 Approach 130
2 Dutch Data 130
2.1 Fires Until 2012 130
2.2 Fires Between 2012 and 2015 131
2.3 Traffic Intensity Until 2012 131
2.4 Traffic Intensity 2012–2015 132
3 Method to Calculate the Probability of a Large Fire 132
3.1 Statistical Model 132
3.2 Bayesian Estimation Procedure 133
3.3 Likelihood Function 133
3.4 The Data 134
3.4.1 Data Until 2012 134
3.4.2 Data Until 2015 135
3.5 The Prior’s 135
3.6 The Predictive Distribution 137
3.7 The Results 137
4 Conclusions and Recommendations 138
References 138
10 Statistical Extrapolation for Extreme Traffic Load Effect Estimation on Bridges 140
Abstract 140
1 Introduction 141
2 Methodology 142
2.1 Traffic Streams 142
2.2 Load Effect Time Histories 143
2.3 Statistical Extrapolation 143
2.3.1 Block Maxima Method with Fit of GEV Distribution 144
2.3.2 Level Cross Counting Method with Fit of Rice’s Formula 144
3 Numerical Investigation 145
3.1 Traffic Data 145
3.1.1 Real Traffic 145
3.1.2 Synthetic Traffic 146
3.2 Bridge Structure 146
3.3 Statistical Extrapolation 147
3.4 Results for Extrapolation Based on Real Traffic 148
3.5 Results for Extrapolation Based on Synthetic Traffic 152
4 Concluding Remarks 157
Acknowledgments 158
References 158
Uncertainty Quantification 159
Uncertainty Quantification for Force Identification and Response Estimation in Structural Dynamics 160
1 Introduction 160
2 Mathematical Formulation 161
2.1 System Model 162
2.2 Joint Input-State Estimation Algorithm 163
2.3 Uncertainty Quantification Approach 164
3 Demonstration 167
3.1 Measurement Setup 167
3.2 System Model 168
3.3 Quantification of Estimation Uncertainty 169
3.4 Force Identification Results 174
4 Conclusions 175
References 176
12 Uncertainty Quantification of Creep in Concrete by Taylor Series Expansion 178
Abstract 178
1 Introduction 179
2 Creep Prediction Models 182
3 Methods of Uncertainty Analysis 183
3.1 Tayler Series Approximation 183
3.2 Statistical Analysis by Simulation 184
4 Results and Discussion 185
4.1 Uncorrelated Input Parameters 186
4.2 Correlated Input Parameters 187
5 Conclusions 189
References 190
13 Uncertainty Quantification of Extrapolation Techniques for Bonded Anchors 192
Abstract 192
1 Introduction 193
2 Displacement Projection of a Complete Anchor System 193
3 Uncertainty Quantification 195
3.1 Regression Choices 197
3.1.1 Moving Time Window Analysis (MTWA) 197
3.1.2 Random Sampling Analysis (RSA) 197
3.1.3 Stochastic Modification of Point Coordinates (SMPC) 198
4 Summary of Regression Uncertainty 201
5 Conclusions 202
Acknowledgments 203
References 203
14 Uncertainty Quantification Applied to a Fire-Exposed Glued-Laminated Timber Beam 205
Abstract 205
1 Introduction 206
1.1 Fire Design of Structural Timber Elements 206
1.2 Monte Carlo Simulation 207
1.3 Deterministic Sampling 207
2 Uncertainty Quantification of a Timber Beam 209
3 Summary and Conclusions 210
References 215
Uncertainty Modelling 216
Generation of Spatially Embedded Random Networks to Model Complex Transportation Networks 217
1 Introduction 217
2 Methodology 218
2.1 Definition of a Spatially Embedded Random Network 218
2.2 Vertex Creation 219
2.3 Edge Creation 220
2.4 Network Generation 222
3 Example 222
3.1 Background 222
3.2 Description of Data 223
3.3 Generation of Random Networks 224
3.4 Illustration of Usefulness 225
4 Discussion 227
5 Conclusions 228
6 Future Work 228
References 229
16 Effect of Climate Change on Snow Load on Ground: Bayesian Approach for Snow Map Refinement 231
Abstract 231
1 Introduction 232
2 Methodology 232
2.1 Analysis of Gridded Climatic Data 233
2.2 Definition of the Random Field 235
2.2.1 Introduction 235
2.2.2 Representation of the Random Field 236
2.3 Bayesian Updating of the Random Field 239
3 Conclusion and Outlook 243
References 243
17 Imposed Correlation Between Random Field and Discrete Particle Placement 245
Abstract 245
1 Introduction 246
1.1 Ldpm 247
2 Proposed Particle Generation 248
3 Conclusions 251
Acknowledgments 252
References 252
18 A Bayesian Network for the Definition of Probability Models for Masonry Mechanical Parameters 253
Abstract 253
1 Introduction 254
1.1 General Remarks 254
1.2 Assessment of Material Parameters Through Bayesian Updating Techniques 254
1.3 Assessment of Material Parameters Through Visual Inspection 255
1.4 Assessment of Material Parameters Through Artificial Intelligence Techniques 255
2 Bayesian Networks 256
3 Development of a Bayesian Networks for the Definition of Masonry Mechanical Parameters 258
3.1 Proposal Based on Masonry Quality Index Method 258
3.2 Improvement with the Variable ‘Similarity’ 260
3.3 Improvement with the Variables ‘Engineering Judgments on…’ 261
4 Example 262
5 Conclusions 266
Acknowledgments 267
References 267
19 A Bayesian Network for the Definition of Probability Models for Compressive Strength of Concrete Homogeneous Population 269
Abstract 269
1 Introduction 269
1.1 Motivation of the Research 269
1.2 Discovering Homogeneous Population of Concrete in Databases of Test Results 270
1.3 Applying Artificial Intelligence Techniques for Estimating Concrete Strength 272
2 Mixture Models and Bayesian Networks 273
2.1 Mixture Models 273
2.2 Bayesian Networks 274
2.3 Fitting a Mixture Model 275
3 A Bayesian Network for the Definition of Concrete Compressive Strength Probability Models 276
3.1 Fitting the Mixture Model of the Concrete Compressive Strength 276
3.2 Improvement with the Concrete Strength Estimated Through NDT 279
3.3 Validation of the Methodology 281
4 Conclusion 282
References 283
20 Probabilistic Tsunami Hazard Assessment Through Large Scale Simulations 284
Abstract 284
1 Introduction 284
2 Method 286
2.1 PTHA Method 286
2.2 2004 Earthquake and Tsunami 286
2.3 Observation Point 287
2.4 COMCOT Tsunami Model 287
2.5 SPH Model 288
3 Result 289
3.1 Tsunami Return Period 289
3.2 Tsunami Inundation Simulation 290
4 Conclusion 292
Acknowledgments 293
References 293
Applied Structural Reliability Analysis 295
21 Probabilistic Slope Stability Analysis Using Approximative FORM 296
Abstract 296
1 Introduction 297
2 Slope Stability Analysis of River Embankments 297
2.1 Introduction 297
2.2 Slope Stability Modelling 297
2.3 Shear Strength Modelling 298
2.3.1 Mohr-Coulomb for Drained Analysis 298
2.3.2 Critical State Soil Mechanics Model for Undrained Analysis 298
2.4 Model Uncertainty 298
3 Standard Reliability Calculation Methods 299
3.1 Limit State Definition 299
3.2 Reliability Calculation Methods 299
3.3 Monte Carlo Approach 299
3.4 First Order Reliability Method 300
3.5 Fragility Curves 300
3.6 Review of Monte-Carlo Approach and First Order Reliability Method in the Reliability Calculation 301
4 Approximative FORM 301
4.1 Workflow 301
4.2 Probabilistic Benchmark of Approximative FORM 302
4.3 Validation Approximative FORM Algorithm 304
5 Case Study of a River Embankment 306
5.1 Implementation of Approximative FORM for Slope Reliability Analysis 306
5.2 Case Study of a Realistic Cross Section 308
5.3 Deterministic Sanity Check 308
5.4 Probabilistic Analysis Using Approximative FORM 310
6 Summary and Conclusions 312
References 312
22 Bayesian Updating of Slope Reliability in Undrained Clay with Vane Shear Test Data 314
Abstract 314
1 Introduction 315
2 Bayesian Updating of Slope Reliability in Spatially Variable Soils 316
2.1 Modelling Spatially Variable Soils 316
2.2 Learning the Distribution of Spatially Varying Soil Properties 316
2.2.1 Adaptive BUS Approach 317
2.3 Estimation of the Posterior Probability of Slope Failure 318
3 Application to a Saturated Clay Slope Example 319
3.1 Prior Probabilistic Model 319
3.2 Data and Likelihood Function 321
3.3 Results 322
3.4 Sensitivity Analysis on Borehole Locations 324
4 Conclusion 325
Acknowledgments 326
References 326
23 Structural Reliability in Design and Analysis of Tensile Structures 328
Abstract 328
1 Introduction 329
2 Case Study: Y-Structure 330
2.1 Description 330
2.2 Full-Probabilistic Structural Reliability Analysis Using Latin-Hypercube Sampling 331
2.3 Results 331
2.3.1 Step 1: Design 331
2.4 Step 2: Simulation Sets for Latin Hypercube Sampling 332
2.4.1 Step 3: Easy Simulation 335
2.4.2 Step 4: Reliability Index and Probability of Failure 337
3 Conclusion 340
References 341
Probabilistic Assessment of Wind-Loaded Façade Elements 342
1 Introduction 343
2 Assessment Procedure 344
2.1 Approach 344
2.2 Probabilistic Description of the Wind Load S(?i) 346
3 Case Study 350
3.1 Design Situation 350
3.2 Data Used for Analysis 351
3.3 Results 353
4 Discussion 358
5 Conclusion 359
References 360
25 Shear Resistance of Prestressed Girders: Probabilistic Design 362
Abstract 362
1 Introduction 363
2 Deterministic Computational Model 365
3 Stochastic Computational Model 367
4 Design Based on Probabilistic Assessment 369
4.1 Concept 369
4.2 Ultimate Limit State 369
4.3 Serviceability Limit State of Crack Initiation 370
5 Conclusions 371
Acknowledgments 371
References 371
26 Reliability Assessment of Buried Pipelines for Through-Wall Bending Stress 373
Abstract 373
1 Introduction 374
2 Pipe Failure Condition 375
2.1 Ovality and Bending Stress 375
3 Corrosion of Buried Pipes 376
4 Methodology 377
5 Numerical Example 379
6 Results and Discussion 380
7 Conclusions 382
References 382
Sensitivity Studies Within a Reliability Analysis of Cross Sections with Carbon Concrete 384
1 Introduction 384
2 Engineering Model and Limit State 385
2.1 Material Models 386
2.2 Limit States 386
2.3 Modes of Failure 388
3 Reliability 389
3.1 Probability of Failure 389
3.2 The Reliability Index 390
3.3 Scattering Properties 391
3.4 Probability of Failure of a Carbon RC-Beam 393
4 Sensitivity Analysis 394
4.1 Sensitivity and Total Effect Indices 395
4.2 Response Surface Method 396
5 Results and Discussion 396
6 Conclusion 398
References 399
Risk Analysis and Optimization 401
28 Risk Analysis of Bridge Falsework Structures 402
Abstract 402
1 Introduction 403
2 Risk Informed Structural Design Methodology 404
2.1 Structural Robustness 404
2.2 Structural Fragility 405
2.3 Vulnerability and Risk Measures 406
3 Illustrative Examples 407
3.1 Choice of Stochastic Variables 407
3.2 Case Study Analyses 409
3.2.1 Introduction 409
3.2.2 Predictive Models and Uncertainties 410
3.2.3 Stochastic Analyses 411
3.2.4 Additional Stochastic Analyses 412
3.3 Risk Evaluation 415
3.4 Risk Control and Risk Informed Decision-Making 415
4 Conclusions 418
Acknowledgments 418
References 419
29 Reliability-Based Methodology for the Optimal Design of Viscous Dampers 420
Abstract 420
1 Introduction 421
2 Reliability-Based Design Optimization 422
2.1 General Problem Formulation 422
2.2 RBDO Methodology for Damper Design 423
2.3 RBDO Problem Solution 424
3 Case Study 425
3.1 Structural System 425
3.2 Ground Motion Model 426
3.3 Results 427
4 Conclusions 430
References 431
30 Optimization of a Landing Gear System Including Uncertainties 433
Abstract 433
1 Introduction 434
2 Case Study and Bifurcation Analysis 435
2.1 Landing Gear Model 435
2.2 Bifurcation Analysis 436
3 Optimization Process 437
4 Application and Results 441
5 Conclusions 448
References 448
Probabilistic Assessment of New and Existing Structures 450
31 Probabilistic Analysis of Combination Rules in Eurocodes 451
Abstract 451
1 Introduction 451
2 Fundamental Load Combinations 452
3 Probabilistic Analysis 453
4 Results of Analysis 455
5 Conclusions 458
Acknowledgments 458
References 458
32 Floor Live Loads of Building Structures 460
Abstract 460
1 Introduction 461
2 Stochastic Live Load Model 461
2.1 General Remarks 461
2.2 Spatial Variation 461
2.3 Temporal Variation 463
2.4 Parameters 464
2.4.1 Sustained Load 464
2.4.2 Extraordinary Load 465
3 Stochastic Simulation of Live Loads 465
3.1 Selected Building Structure 465
3.2 Parameter ? for Selected Load Effects 465
3.3 Correlation Between Equivalent Uniform Loads 466
3.4 Generation of Random Processes 468
3.5 Transformation to Load Effect History 469
4 Results 470
4.1 Characteristic Values 470
4.2 Live Load Reduction 471
5 Conclusions 472
References 472
33 Methodology for Evaluating the Safety Level of Current Accepted Design Solutions for Limiting Fire Spread Between Buildings 474
Abstract 474
1 Introduction 474
2 Defining “Failure”: The Limit Criterion 475
3 Calculation Methodology 476
4 Evaluating the Safety Level of Currently Accepted Design Solutions: Case Study UK Guidance BR 187 479
4.1 Case Study Introduction and Standard Application of BR 187 479
4.2 Calculation of the Conditional Probabilities Pf,Fi,AB and Pf,Fi,BA 481
4.3 Annual Failure Probabilities Associated with the BR 187 Design 482
4.4 Parameter Study: Influence of the Separation Distance 483
5 Conclusions 485
References 485
34 Robustness Assessment—A New Perspective to Achieve a Performance Indicator 487
Abstract 487
1 Introduction 487
2 Robustness 488
3 Robustness 489
4 Case Study 490
4.1 Damage Scenarios 491
4.2 Obtained Results 491
5 Conclusions 494
Acknowledgments 494
References 494
35 Probabilistic Concepts of Upcoming European Document on Assessment of Existing Structures 496
Abstract 496
1 Introduction 496
2 Principles and General Framework of Assessment 498
3 Investigation 499
4 Basic Variables 500
5 Data Updating 501
6 Structural Analysis 503
7 Verification 504
7.1 Partial factor method 505
7.2 Design value method 505
7.3 Probabilistic method 505
7.4 Risk assessment approach 506
8 Concluding Remarks 507
Acknowledgments 507
References 507
36 Present and Future Probabilistic Challenges for Maintenance of Reinforced Concrete Structures 508
Abstract 508
1 Introduction 508
2 Probabilistic Models 510
2.1 Modelling of Structural Behaviour 510
2.2 Service Life Modelling 513
3 Probabilistic Model Updating 515
3.1 Reliability of Measurement Methods 516
3.1.1 Quantitative Measurement Methods 517
3.1.2 Qualitative Measurement Methods 518
4 Spatial Variability of Structure Condition 519
5 Conclusions 521
References 522
Author Index 525