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Preface; Organization; Chair of IPW2016; Scientific Committee; Contents; Keynotes; 1 Optimizing Adaptable Systems for Future Uncertainty; Abstract; 1 Introduction; 2 Adaptable or Flexible Engineering Systems; 2.1 A Measure of Flexibility; 3 Sequential Decision Analysis; 4 Numerical Illustrations; 4.1 Case 1: Infrastructure Capacity; 4.2 Case 2: Disaster Risk Management; 5 Concluding Remarks; References; 2 Freak Events, Black Swans, and Unknowable Unknowns: Impact on Risk-Based Design; Abstract; 1 Introduction; 2 Infrastructure: Evolving Expectations.
3 What We Know, What We Should Know, What We Don't Know4 Black Swans and Perfect Storms; 5 The "Very" Extreme; 6 The Carlsbad Black Swan: El Paso Natural Gas Pipeline Rupture, 19 August, 2000; 7 The Fukushima Daiichi Perfect Storm, 11 March 2011; 8 Conclusions: Demystifying the Extraordinary; References; Structural Reliability Methods and Statistical Approaches; Extrapolation, Invariance, Geometry and Subset Sampling; 1 Introduction; 2 The Subset Sampling Method; 3 SuS and Asymptotic Approximations; 4 Extrapolation; 5 Invariance; 6 Changing Topological Structure of Domains.
7 Several Beta Points8 Bias and Variance of SuS Estimates; 9 Conclusions; References; 4 Performance of Various Sampling Schemes in Asymptotic Sampling; Abstract; 1 Introduction; 2 Testing Limit-State Functions; 2.1 Limit-State Function Sum1D; 2.2 Limit-State Function Sum2D; 2.3 Limit-State Function Sin2D; 3 Asymptotic Sampling (AS); 4 Design of Experiment; 4.1 Monte Carlo (MC) Sampling; 4.2 Latin Hypercube Sampling (LHS); 4.3 LHS Optimized-Periodic Audze-Eglājs (PAE) Criterion; 4.4 Quasi-Monte Carlo (QMC) Sequences; 4.5 The Sobol Sequence; 5 Results; 5.1 Limit-State Function Sum1D.
5.2 Limit-State Function Sum2D5.3 Limit-State Function Sin2D; 6 Concluding Remarks; Acknowledgments; References; Moving Least Squares Metamodels
Hyperparameter, Variable Reduction and Model Selection; 1 Introduction; 2 From Least Squares to Moving Least Squares; 2.1 Linear Regression Model; 2.2 Least Squares; 2.3 Weighted Least Squares; 2.4 Moving Least Squares; 3 Settings of WLS and MLS; 3.1 Model Function f (b(x1, #x83;, xnk)); 3.2 Weighting Matrix; 4 MLS Model Tuning; 4.1 Tuning of Hyperparameters; 4.2 Variable Reduction; 5 Framework of Deterministic Models; 5.1 Implemented Models.
5.2 Design of Experiments (DOE)6 Evaluation of LS and MLS Metamodels; 7 Summary and Outlook; References; 6 Comparing Three Methodologies for System Identification and Prediction; Abstract; 1 Introduction; 2 Structural Identification Methodologies; 2.1 Traditional Bayesian Model Updating; 2.2 Error-Domain Model Falsification; 2.3 Modified Bayesian Model Updating; 3 Numerical Example; 4 Conclusion; References; 7 Global Sensitivity Analysis of Reinforced Concrete Walls Subjected to Standard Fire-A Comparison of Methods; Abstract; 1 Introduction; 2 Applied Methods.
3 What We Know, What We Should Know, What We Don't Know4 Black Swans and Perfect Storms; 5 The "Very" Extreme; 6 The Carlsbad Black Swan: El Paso Natural Gas Pipeline Rupture, 19 August, 2000; 7 The Fukushima Daiichi Perfect Storm, 11 March 2011; 8 Conclusions: Demystifying the Extraordinary; References; Structural Reliability Methods and Statistical Approaches; Extrapolation, Invariance, Geometry and Subset Sampling; 1 Introduction; 2 The Subset Sampling Method; 3 SuS and Asymptotic Approximations; 4 Extrapolation; 5 Invariance; 6 Changing Topological Structure of Domains.
7 Several Beta Points8 Bias and Variance of SuS Estimates; 9 Conclusions; References; 4 Performance of Various Sampling Schemes in Asymptotic Sampling; Abstract; 1 Introduction; 2 Testing Limit-State Functions; 2.1 Limit-State Function Sum1D; 2.2 Limit-State Function Sum2D; 2.3 Limit-State Function Sin2D; 3 Asymptotic Sampling (AS); 4 Design of Experiment; 4.1 Monte Carlo (MC) Sampling; 4.2 Latin Hypercube Sampling (LHS); 4.3 LHS Optimized-Periodic Audze-Eglājs (PAE) Criterion; 4.4 Quasi-Monte Carlo (QMC) Sequences; 4.5 The Sobol Sequence; 5 Results; 5.1 Limit-State Function Sum1D.
5.2 Limit-State Function Sum2D5.3 Limit-State Function Sin2D; 6 Concluding Remarks; Acknowledgments; References; Moving Least Squares Metamodels
Hyperparameter, Variable Reduction and Model Selection; 1 Introduction; 2 From Least Squares to Moving Least Squares; 2.1 Linear Regression Model; 2.2 Least Squares; 2.3 Weighted Least Squares; 2.4 Moving Least Squares; 3 Settings of WLS and MLS; 3.1 Model Function f (b(x1, #x83;, xnk)); 3.2 Weighting Matrix; 4 MLS Model Tuning; 4.1 Tuning of Hyperparameters; 4.2 Variable Reduction; 5 Framework of Deterministic Models; 5.1 Implemented Models.
5.2 Design of Experiments (DOE)6 Evaluation of LS and MLS Metamodels; 7 Summary and Outlook; References; 6 Comparing Three Methodologies for System Identification and Prediction; Abstract; 1 Introduction; 2 Structural Identification Methodologies; 2.1 Traditional Bayesian Model Updating; 2.2 Error-Domain Model Falsification; 2.3 Modified Bayesian Model Updating; 3 Numerical Example; 4 Conclusion; References; 7 Global Sensitivity Analysis of Reinforced Concrete Walls Subjected to Standard Fire-A Comparison of Methods; Abstract; 1 Introduction; 2 Applied Methods.