Structural Damage Identification via Bayesian Inference with a New Hierarchical Modeling and Spike-and-Slab Prior

Authors

DOI:

https://doi.org/10.5902/2179460X87212

Keywords:

Bayesian Inference, Adaptive Markov Chain Monte Carlo Method, Spike-and-Slab Prior

Abstract

The present work aims to formulate and solve the inverse problem of structural damage identification using Bayesian Inference. In the solution of the direct problem, the Finite Element Method (FEM) is considered. The modeling of the damage field is performed through the cohesion parameter, which continuously describes the integrity of the structure. The damage identification problem is formulated as an inverse parameter estimation problem, where the posterior probability distribution of the cohesion parameters is sampled using the Adaptive Markov Chain Monte Carlo method and a Spike-Slab prior, adopting a novel hierarchical modeling approach for the inverse problem and an appropriate prior distribution that naturally models the available information about the parameters of interest.

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Author Biographies

Josiele da Silva Teixeira, Universidade do Estado do Rio de Janeiro

Holds a Bachelor's degree in Mathematics with an emphasis in Mathematical Modeling from the Federal University of Espírito Santo (2011). Master's degree in Computational Modeling from the Polytechnic Institute (IPRJ), Regional Campus of Nova Friburgo, RJ (2014). PhD in Computational Modeling from the Polytechnic Institute (IPRJ), Regional Campus of Nova Friburgo, RJ (2018). She has been working in the area of applied mathematics and computational modeling since 2012, where she has developed works in the following research lines: Inverse Problems, Identification of Structural Damage from Vibration Tests and Bayesian Inference.

Helio dos Santos Migon, Universidade do Estado do Rio de Janeiro

Graduated in Statistics from the National School of Statistical Sciences (1970), obtained a Master's degree in Statistics from the University of São Paulo (1974) and a PhD in Statistics from the University of Warwick, UK (1984). Currently, he is a visiting researcher at IPRJ - Research Institute of Rio de Janeiro (UERJ), Faperj) and professor emeritus at the Federal University of Rio de Janeiro. Has experience in the area of Probability and Statistics, with emphasis on Probability and Applied Statistics, working mainly on the following topics: Bayesian Inference, Dynamic Models and Bayesian Predictions, Finite Population Sampling, Econometrics Applied to Finance and Actuary.

Leonardo Tavares Stutz, Universidade do Estado do Rio de Janeiro

Holds a bachelor's degree in Mechanical Engineering from the Federal University of Rio de Janeiro (1997), a master's degree in Mechanical Engineering from the Alberto Luiz Coimbra Institute of Graduate Studies and Engineering Research - COPPE (1999) and a PhD in Mechanical Engineering from the Alberto Luiz Coimbra Institute of Graduate Studies and Engineering Research - COPPE (2005). He is currently an Associate Professor at the State University of Rio de Janeiro, being a member of the Department of Mechanical and Energy Engineering and the Graduate Program in Computational Modeling of the Polytechnic Institute (IPRJ), Regional Campus of Nova Friburgo, RJ. He works in the area of Dynamics and Vibrations. His main interests are: Modeling, parameter estimation and selection of model classes; Identification of structural damage; Viscoelastic cushioning; Reduction of model order; and Bayesian Inference. 

Diego Campos Knupp, Universidade do Estado do Rio de Janeiro

Holds a degree in Mechanical Engineering from the Polytechnic Institute of the State University of Rio de Janeiro (2009). He holds a master's degree (2010) and a PhD (2013) in Mechanical Engineering from COPPE/UFRJ. He currently holds the position of Adjunct Professor at the Polytechnic Institute, IPRJ/UERJ and is a professor of the permanent staff of the Graduate Program in Computational Modeling (Note 6 CAPES - Master's and Doctorate). Elected Affiliate Member of the Brazilian Academy of Sciences (2019-2023). Is the author of more than 100 papers published in peer-reviewed journals and conferences, his main areas of interest include inverse engineering problems, hybrid methods, integral transformation, micro/nanoscale heat transfer, and optimization.

Antônio José da Silva Neto, Universidade do Estado do Rio de Janeiro

Antônio José da Silva Neto is a Mechanical/Nuclear Engineer (UFRJ, 1983), M.Sc. in Nuclear Engineering (COPPE/UFRJ, 1989) and Ph.D. in Mechanical Engineering (North Carolina State University, 1993). Since 1997 he has been a professor at the Polytechnic Institute of the State University of Rio de Janeiro. He works in the area of Mechanical Engineering, with emphasis on Heat Transfer, and in Applied and Computational Mathematics, with emphasis on Numerical Methods. In its Lattes Curriculum, the most frequent terms in the contextualization of scientific and technological production are: Inverse Problems, Thermal Radiation, Heat Conduction, Boltzmann's Equation, Participating Media and Source Term Estimation.

References

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Published

2024-11-07

How to Cite

Teixeira, J. da S., Migon, H. dos S., Stutz, L. T., Knupp, D. C., & Silva Neto, A. J. da. (2024). Structural Damage Identification via Bayesian Inference with a New Hierarchical Modeling and Spike-and-Slab Prior. Ciência E Natura, 46(esp. 1), e87212. https://doi.org/10.5902/2179460X87212

Issue

Vol. 46 No. esp. 1 (2024): ERMAC e ENMC

Section

Special Edition 1

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