Advances in Simulation-Based Uncertainty Quantification and Reliability Analysis

Abstract

The special collection on Advances in Simulation-Based Uncertainty Quantification and Reliability Analysis is available in the ASCE Library (https://ascelibrary.org/page/ajrua6/simulation _based_uncertainty_quantification_reliability_analysis). Simulations are increasingly being used in lieu of or to supplement physical testing in several major industries from civil structural analysis and design to the automotive, aircraft, and naval industries. Two critical aspects of simulation-based analysis and design are the rigorous quantification of uncertainty and the ability to rapidly and accurately assess reliability. Monte Carlo simulation is the most robust simulation-based approach for such problems and serves as a benchmark against which new methods can be compared. The well-known problem with Monte Carlo methods, especially for reliability assessment, is their large computational expense imposed by the requirement of running a very large number of simulations. In recent years, rapid advances that improve on classical Monte Carlo simulation, coupled with improvements in computational resources, have begun to usher in a new era of simulation-based uncertainty analysis such that modern challenges, e.g., uncertainty quantification (UQ) for very large (e.g., high-dimensional, computationally intensive) and complex (e.g., strongly nonlinear, multicomponent, multiscale, multiphysics) systems and inverse problems, are becoming increasingly tractable with a reasonable number of simulations. Examples of new methods include Markov chain Monte Carlo (MCMC) approaches such as subset simulations, sparse-grid stochastic collocation methods, Bayesian nested sampling, variance reduction techniques (i.e., Latin hyper-cube, importance sampling), and new adaptive Monte Carlo and quasi-Monte Carlo methods. This special collection aims at exploring the latest methodologi-cal developments in simulation-based uncertainty quantification and reliability analysis. The genesis of this special collection was a series of minisymposia on the topic organized by the coeditors and colleagues held at the 2016 Probabilistic Mechanics and Reliability Conference, the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, and the 12th International Conference on Structural Safety and Reliability. These minisymposia saw a total of 39 speakers addressing some of the most pressing issues in computational uncertainty quantification and reliability analysis. Among these presenters, several authors were invited to contribute to this special collection as recognized leaders in the field of computational UQ and reliability analysis for civil engineering systems. These invitations resulted in eight papers that cover four important research areas in simulation-based UQ and reliability analysis: 1. Simulation of natural hazards under uncertainty; 2. Development of surrogate modeling techniques for UQ; 3. Adaptive and informative sampling for uncertainty in simulations ; and 4. Data-driven modeling in UQ. More specifically, Christou et al. (2018), Vlachos et al. (2018), and Suksuwan and Spence (2018) address issues related to seismic and wind hazards. Zhang and Taflanidis (2018), Moustapha et al. (2018), and Sundar and Shields (2019) discuss and compare approaches for surrogate model development-most notably Kriging-and support-vector-regression-based techniques. Naess and Bo (2018), Zhang and Taflanidis (2018), and Sundar and Shields (2019) further discuss issues related to sampling for Monte Carlo simulations or surrogate model development, while Christou et al. (2018) employs an optimal set of random field samples for hazard modeling. Lastly, Cai and Mahadevan (2018) discuss approaches to leverage big data for uncertainty quantification purposes. With regard to seismic hazard modeling, Christou et al. (2018) propose a method called hazard quantization that models regional seismic intensity measures as two-dimensional, non-Gaussian, heterogeneous random fields, and present an optimal representation of these intensity measure maps through a process called functional quantization. Vlachos et al. (2018), meanwhile, present a stochastic method for generating nonstationary seismic acceleration time histories from a set of ground motion descriptors-the moment magnitude, rupture distance, and shear wave velocity-for a given site. The model is calibrated from existing ground motion data, validated against established ground motion prediction models, and employed for nonlinear time history analysis. For the modeling of wind hazards, Suksuwan and Spence (2018) present an approach for reliability-based optimization of large structure systems subjected to wind excitations. The approach resolves the computational challenges of reliability-based optimization by decoupling the reliability problem and the optimization problem. Convergence properties of the proposed method are discussed and its efficiency demonstrated for large high-rise structural forms. A dominant area of research in computational UQ over the past decade has been in the development of surrogate models that can be used to provide computationally inexpensive approximates of an expensive computer model. A surrogate modeling technique of particular interest is the Kriging (or Gaussian process) model that is studied extensively in this special collection. The paper by Moustapha et al. (2018) compares the Kriging model with support vector regression to assess their relative efficiency. In this comparison, the authors specifically uncover the importance of introducing anisotropy in the model hyperparameters through a carefully automated efficient global search algorithm. Along a similar line, Sundar and Shields (2019) explore the importance of the Kriging model form-in particular the form of the regressor used for the trend and the kernel used for the covariance. In this work, © ASCE 02019003-1 ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.