Bayesian damage identification based on autoregressive model and MH-PSO hybrid MCMC sampling method
Bayesian damage identification method, due to its ability to consider the uncertainties, has
attracted much attention from researchers. However, there are two key issues to ensure the …
attracted much attention from researchers. However, there are two key issues to ensure the …
A novel method for damage identification based on tuning-free strategy and simple population Metropolis–Hastings algorithm
The most commonly used method for sampling damage parameters from the posterior
distribution is the Markov chain Monte Carlo (MCMC) method. The population MCMC …
distribution is the Markov chain Monte Carlo (MCMC) method. The population MCMC …
Bayesian damage identification of an unsymmetrical frame structure with an improved PSO algorithm
With the development of artificial intelligence technology, intelligent algorithms are widely
utilized in the field of probabilistic model updating to solve the optimization problems. In this …
utilized in the field of probabilistic model updating to solve the optimization problems. In this …
EEG-based identification of evidence accumulation stages in decision-making
Dating back to the 19th century, the discovery of processing stages has been of great
interest to researchers in cognitive science. The goal of this paper is to demonstrate the …
interest to researchers in cognitive science. The goal of this paper is to demonstrate the …
[HTML][HTML] The Bayesian pattern search, a deterministic acceleration of Bayesian model updating in structural health monitoring
Finite element (FE) model updating is a popular tool for damage localisation and
quantification in structural health monitoring (SHM) of buildings, infrastructure and wind …
quantification in structural health monitoring (SHM) of buildings, infrastructure and wind …
Bayesian model updating utilizing scaled likelihood ratio and BCT-PCA with frequency response function
Z Deng, M Zhan, X Yuan, H Luo, B Zhang - Mechanical Systems and Signal …, 2023 - Elsevier
Bayesian inference has now been widely practiced for model updating. In this study, the
discussion is conducted about two issues facing the implementation of Bayesian model …
discussion is conducted about two issues facing the implementation of Bayesian model …
Finite element model updating using a shuffled complex evolution Markov chain algorithm
In this paper, a probabilistic-based evolution Markov chain algorithm is used for updating
finite element models. The Bayesian approaches are well-known algorithms used for …
finite element models. The Bayesian approaches are well-known algorithms used for …
Markov chain Monte Carlo methods applied to the stochastic inversion of 1D viscoelastic parameters
The characterization of the subsurface profile properties that are susceptible to liquefaction
during earthquakes is of great importance in accident prevention. Field data can be used in …
during earthquakes is of great importance in accident prevention. Field data can be used in …
Bayesian Inference for One-Shot Devices with Weibull Dependent Failure Modes Using Copulas
One-shot devices, like airbags, bombs, and fire extinguishers, are reliable products that are
extensively destroyed after their use and cannot be used more than once. Assessing the …
extensively destroyed after their use and cannot be used more than once. Assessing the …
[HTML][HTML] Bayesian inference for parameter identification in mechanistic models, exemplified using a cow lifetime performance model
Mechanistic models are valuable tools for studying the underlying mechanisms of complex
biological phenomena. For example, cow lifespan models can be used to identify …
biological phenomena. For example, cow lifespan models can be used to identify …