Parcourir par sujet "Hierarchical Bayesian modeling"
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Accounting for amplitude of excitation in model updating through a hierarchical Bayesian approach: Application to a two-story reinforced concrete building
(2019)Calibrated linear equivalent models of civil structures are often used for response prediction and performance assessment. However, these models are only valid for a narrow range of excitation level for which these models ... -
Accounting for modeling errors and inherent structural variability through a hierarchical bayesian model updating approach: An overview
(2020)Mechanics-based dynamic models are commonly used in the design and performance assessment of structural systems, and their accuracy can be improved by integrating models with measured data. This paper provides an overview ... -
A Bayesian framework for calibration of multiaxial fatigue curves
(2022)A Bayesian framework is proposed to re-formulate a multiaxial fatigue model and produce probabilistic stress-life fatigue curves from experimental data. The proposed framework identifies the experimentally-driven parameters ... -
Computationally efficient hierarchical Bayesian modeling framework for learning embedded model uncertainties
(2020)A hierarchical Bayesian modeling (HBM) framework has recently been developed for estimating the uncertainties in the parameters of physics-based models of systems, as well as propagating these uncertainties to estimate the ... -
Hierarchical Bayesian Model Updating for Nonlinear Structures Using Response Time Histories
(2022)This paper presents a novel hierarchical Bayesian modeling (HBM) framework for the model updating and response predictions of dynamic systems with material nonlinearity using multiple data sets consisting of measured ... -
Hierarchical bayesian model updating for probabilistic damage identification
(2015)This paper presents the newly developed Hierarchical Bayesian model updating method for identification of civil structures. The proposed updating method is suitable for uncertainty quantification of model updating parameters, ... -
Hierarchical Bayesian modeling framework for model updating and robust predictions in structural dynamics using modal features
(2022)The hierarchical Bayesian modeling (HBM) framework has recently been developed to tackle the uncertainty quantification and propagation in structural dynamics inverse problems. This new framework characterizes the ensemble ... -
Hierarchical Bayesian Uncertainty Quantification for a Model of the Red Blood Cell
(2021)Simulations of blood flows in microfluidic devices and physiological systems are gaining importance in complementing experimental and clinical studies. The predictive capabilities of these simulations hinge on the parameters ... -
Hierarchical Bayesian uncertainty quantification of Finite Element models using modal statistical information
(2022)This paper develops a Hierarchical Bayesian Modeling (HBM) framework for uncertainty quantification of Finite Element (FE) models based on modal information. This framework uses an existing Fast Fourier Transform (FFT) ... -
Nonlinear model updating through a hierarchical Bayesian modeling framework
(2022)A new time-domain probabilistic technique based on hierarchical Bayesian modeling (HBM) framework is proposed for calibration and uncertainty quantification of hysteretic type nonlinearities of dynamical systems. Specifically, ... -
Two-Stage Hierarchical Bayesian Framework for Finite Element Model Updating
(2020)A hierarchical Bayesian modeling (HBM) framework is presented for updating finite element (FE) models. A two stage approach is proposed for which in the first stage the modal data properties (modal frequencies, damping ...