Gaussian Mixture–Based Autoregressive Error Model with a Conditionally Heteroscedastic Hierarchical Framework for Bayesian Updating of Structures-Reference-Cited by-同舟云学术

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Gaussian Mixture–Based Autoregressive Error Model with a Conditionally Heteroscedastic Hierarchical Framework for Bayesian Updating of Structures

Published:2024-09 Issue:3 Volume:10 Page:
ISSN:2376-7642
Container-title:ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
language:en
Short-container-title:ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

Author:

Sengupta Partha¹^ORCID,Chakraborty Subrata²^ORCID,Mishra Sudib Kumar³

Affiliation:

1. Research Assistant, Dept. of Civil Engineering, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, West Bengal 711103, India (corresponding author). ORCID: .

2. Professor, Dept. of Civil Engineering, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, West Bengal 711103, India. ORCID:

3. Professor, Dept. of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh 208016, India.

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/AJRUA6.RUENG-1205

Reference34 articles.

1. Damage/fault diagnosis in an operating wind turbine under uncertainty via a vibration response Gaussian mixture random coefficient model based framework

2. Finite element model updating using the shadow hybrid Monte Carlo technique

3. Finite element model updating using Hamiltonian Monte Carlo techniques;Boulkaibet I.;Inverse Probl. Sci. Eng.,2016

4. Modal identification of output-only systems using frequency domain decomposition

5. General Methods for Monitoring Convergence of Iterative Simulations

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