A Bayesian neural network approach for probabilistic model updating using incomplete modal data
Author:
Affiliation:
1. Key Laboratory of C&PC Structures of Ministry of Education Southeast University Nanjing China
2. Department of Civil Engineering Monash University Clayton Victoria Australia
Funder
Jiangsu Provincial Key Research and Development Program
Publisher
Hindawi Limited
Subject
Mechanics of Materials,Building and Construction,Civil and Structural Engineering
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1002/stc.3030
Reference50 articles.
1. A residual-based Gaussian process model framework for finite element model updating
2. Substructure based approach to finite element model updating
3. Progressive finite element model calibration of a long-span suspension bridge based on ambient vibration and static measurements
4. Structural model updating using adaptive multi-response Gaussian process meta-modeling
5. Vectorization and distributed parallelization of Bayesian model updating based on a multivariate complex-valued probabilistic model of frequency response functions
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