Πλοήγηση ανά Θέμα "Bayesian networks"
Αποτελέσματα 1-20 από 70
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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 ... -
Adaptive Bayesian Inference Framework for Joint Model and Noise Identification
(2022)Model updating, the process of inferring a model from data, is prone to the adverse effects of modeling error, which is caused by simplification and idealization assumptions in the mathematical models. In this study, an ... -
Allometric biomass models for european beech and silver fir: Testing approaches to minimize the demand for site‐specific biomass observations
(2020)In this paper, site‐specific allometric biomass models were developed for European beech (Fagus sylvatica L.) and silver fir (Abies alba Mill.) to estimate the aboveground biomass in Șinca virgin forest, Romania. Several ... -
Approximate Bayesian computation for granular and molecular dynamics simulations
(2016)The effective integration of models with data through Bayesian uncertainty quantification hinges on the formulation of a suitable likelihood function. In many cases such a likelihood may not be readily available or it may ... -
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 ... -
A bayesian framework for optimal experimental design in structural dynamics
(2016)A Bayesian framework for optimal experimental design in structural dynamics is presented. The optimal design is based on an expected utility function that measures the value of the information arising from alternative ... -
Bayesian identification of the tendon fascicle's structural composition using finite element models for helical geometries
(2017)Despite extensive experimental and computational investigations, the accurate determination of the structural composition of biological tendons remains elusive. Here we infer the structural compositions of tendons by ... -
Bayesian inference for damage identification based on analytical probabilistic model of scattering coefficient estimators and ultrafast wave scattering simulation scheme
(2020)Ultrasonic Guided Waves (GW) actuated by piezoelectric transducers installed on structures have proven to be sensitive to small structural defects, with acquired scattering signatures being dependent on the damage type. ... -
A Bayesian methodology for crack identification in structures using strain measurements
(2010)A Bayesian system identification methodology is presented for estimating the crack location, size and orientation in a structure using strain measurements. The Bayesian statistical approach combines information from measured ... -
Bayesian networks based policy making in the renewable energy sector
(2020)Extensive research on energy policy nowadays combines theory with advanced statistical tools such as Bayesian networks for analysis and prediction. The majority of these studies are related to observe energy scenarios in ... -
Bayesian optimal estimation for output-only nonlinear system and damage identification of civil structures
(2018)This paper presents a new framework for output-only nonlinear system and damage identification of civil structures. This framework is based on nonlinear finite element (FE) model updating in the time-domain, using only the ... -
Bayesian optimal experimental design using asymptotic approximations
(2017)Bayesian optimal experimental design (OED) tools for model parameter estimation and response predictions in structural dynamics include sampling (Huan and Marzouk, J. Comput. Phys., 232:288–317, 2013) and asymptotic ... -
Bayesian optimal sensor placement for crack identification in structures using strain measurements
(2018)A Bayesian framework is presented for finding the optimal locations of strain sensors in a plate with a crack with the goal of identifying the crack properties, such as crack location, size, and orientation. Sensor grids ... -
Bayesian uncertainty quantification and propagation in molecular dynamics simulations
(2012)A comprehensive Bayesian probabilistic framework is developed for quantifying and calibrating the uncertainties in the parameters of the models (e.g. force-field potentials) involved in molecular dynamics (MD) simulations ... -
Bayesian uncertainty quantification and propagation in nonlinear structural dynamics
(2013)A Bayesian uncertainty quantification and propagation (UQ&P) framework is presented for identifying nonlinear models of dynamic systems using vibration measurements of their components. The measurements are taken to be ... -
Bayesian uncertainty quantification and propagation in nonlinear structural dynamics
(2009)Nonlinear modelling and parametric identification of an experimental vehicle model, are employed in this paper. The composite structure of the vehicle model is split into a frame substructure and to four support substructures. ... -
Bayesian uncertainty quantification and propagation using adjoint techniques
(2014)This paper presents the Bayesian inference framework enhanced by analytical approximations for uncertainty quantification and propagation and parameter estimation. A Gaussian distribution is used to approximate the posterior ... -
Bayesian uncertainty quantification of turbulence models based on high-order adjoint
(2015)The uncertainties in the parameters of turbulence models employed in computational fluid dynamics simulations are quantified using the Bayesian inference framework and analytical approximations. The posterior distribution ... -
Camera-driven probabilistic algorithm for multi-elevator systems
(2020)A fast and reliable vertical transportation system is an important component of modern office buildings. Optimization of elevator control strategies can be easily done using the state-of-the-art artificial intelligence ...