Nonlinear finite element model updating for damage identification of civil structures using batch Bayesian estimation
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- 作者：Hamed Ebrahimiana ; Rodrigo Astrozab ; Joel P. Contea ; Raymond A. de Callafonc
- 关键词：Bayesian inference ; Nonlinear finite element model ; Model updating ; Uncertainty quantification ; Gradient-based optimization ; Nonlinear system identification
- 刊名：Mechanical Systems and Signal Processing
- 出版年：2017
- 出版时间：1 February 2017
- 年：2017
- 卷：84
- 期：part_PB
- 页码：194-222
- 全文大小：4207 K
文摘
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A new framework is proposed for structural health monitoring based on nonlinear FE model updating using a batch Bayesian estimation approach.
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The batch Bayesian approach leads to an extended ML estimation method to jointly identify the FE model parameters and measurement noise.
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The parameter estimation uncertainties are quantified using the Cramer–Rao lower bound theorem by computing the Fisher Information matrix.
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Two validation studies using numerically simulated data are provided to investigate the performance of the proposed framework.