Bayesian Updating of Structural Models and Reliability using Markov Chain Monte Carlo Simulation

By James L. Beck, Siu-Kui Au. 2002

843 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/90bc3e246333c0717afa4434668e1919190bb6cd

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In Civil and Structural Engineering

In Statistics and Probability

Citations in the corpus

Bayesian identification of random field model using indirect test data (2016, background): “…a numerical process that simulates a sequence of random samples of target random variable (e.g., X) as a Markov Chain with the PDF (e.g., PðXjξ̂;MÞ) of the random variables as theMarkov Chain's limiting stationary distribution (e.g., Beck and Au, 2002; Robert and Casella, 2004; Wang and Cao, 2013).”
Bayesian Updating with Structural Reliability Methods (2015, methodology): “Many authors have applied and adopted MCMC to Bayesian updating of mechanical models, including (Beck and Au 2002; Cheung and Beck 2009; Sundar and Manohar 2013).”
A Bayesian Approach to Identifying Structural Nonlinearity using Free-Decay Response: Application to Damage Detection in Composites (2010, methodology): “appear to have pioneered the use of MCMC methods in structural dynamics in Beck and Au [20] and more recently in Cheung and Beck [21].”
Model Selection using Response Measurements: Bayesian Probabilistic Approach (2004, methodology): “~Beck and Katafygioti 1998; Katafygiotis et al. 1998! or by using a Markov chain Mon Carlo simulation technique~Beck and Au 2002! on Eq.”
Interval model updating with irreducible uncertainty using the Kriging predictor (2011, methodology): “[10] and Friswell [11], the Bayesian methods of Beck and his colleagues [12,13], random matrix theory methods of Soize et al.”
An adaptive sparse‐grid high‐order stochastic collocation method for Bayesian inference in groundwater reactive transport modeling (2013, methodology): “…of the most widely utilized approaches for quantifying parametric uncertainty [Kitanidis, 1986; Box and Tiao, 1992; Ezzedine et al., 1999; Beck and Au, 2002; Marshall et al., 2005; Marzouk et al., 2007; Ma and Zabaras, 2009; Marzouk and Xiu, 2009; Allaire and Willcox, 2010; Renard, 2011; Zeng et…”
Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks (2018, methodology): “This methodology has been used extensively in the past for model calibration, prediction and selection [42, 43, 44, 10].”
Probabilistic updating of building models using incomplete modal data (2016, methodology): “In Bayesian model updating, the posterior probability density function (PDF) of the model parameters (θ), given a specified model class, can be obtained based on the Bayes' theorem [8,30]:”
Time-Dependent Reliability of Dynamic Systems Using Subset Simulation With Splitting Over a Series of Correlated Time Intervals (2013, background): “Because it is very challenging to generate samples in the conditional spaces, subset simulation with Markov Chain Monte Carlo (SS/MCMC) [28, 29] and subset simulation with splitting (SS/S) [30-32] methods have been introduced.”