Bayesian system identification based on hierarchical sparse Bayesian learning and Gibbs sampling with application to structural damage assessment

DOI10.1016/j.cma.2017.01.030zbMath1439.65136arXiv1701.03550OpenAlexW2577761826MaRDI QIDQ2309095

James L. Beck, Hui Li, Yong Huang

Publication date: 6 April 2020

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1701.03550

zbMATH Keywords

hierarchical modelGibbs samplingsparse Bayesian learningdamage assessmentBayesian system identificationIASE-ASCE Phase II benchmark

Mathematics Subject Classification ID

Bayesian inference (62F15) Numerical methods for inverse problems for boundary value problems involving PDEs (65N21)

Related Items (9)

Nonlinear model updating through a hierarchical Bayesian modeling framework ⋮ Variational Bayesian approximation of inverse problems using sparse precision matrices ⋮ BUAK-AIS: efficient Bayesian updating with active learning kriging-based adaptive importance sampling ⋮ Strategy for sensor number determination and placement optimization with incomplete information based on interval possibility model and clustering avoidance distribution index ⋮ Bayesian structural identification using Gaussian process discrepancy models ⋮ Probabilistic model updating via variational Bayesian inference and adaptive Gaussian process modeling ⋮ Sequential sparse Bayesian learning with applications to system identification for damage assessment and recursive reconstruction of image sequences ⋮ Novel sparseness-inducing dual Kalman filter and its application to tracking time-varying spatially-sparse structural stiffness changes and inputs ⋮ Parameter identification for phase-field modeling of fracture: a Bayesian approach with sampling-free update

Uses Software

• ABC-SubSim
• BayesDA

Cites Work

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• Approximate Bayesian Computation by Subset Simulation
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• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• 10.1162/15324430152748236
• Probability Theory
• Inverse Problem Theory and Methods for Model Parameter Estimation
• ASYMPTOTICALLY INDEPENDENT MARKOV SAMPLING: A NEW MARKOV CHAIN MONTE CARLO SCHEME FOR BAYESIAN INFERENCE

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