A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Bayesian Model Updating and Parameter Uncertainty Analysis of a Damaged Fortress Through Dynamic Experimental Data
https://orcid.org/http://orcid.org/0000-0002-2808-4707
https://orcid.org/http://orcid.org/0000-0002-4919-1421
https://orcid.org/http://orcid.org/0000-0003-2541-7104
A probabilistic analysis for the uncertainty evaluation of model parameters is of great relevance when dealing with structural damage assessment. Indeed, the identification of the damage severity associated to its uncertainty can support the decision-maker to close a bridge or a building for safety reasons. In this paper the results of the model updating of an historical masonry fortress damaged by the seismic event that hits the town of San Felice sul Panaro and the surrounding localities in the Po Valley in the 2012 are presented. A standard and a Bayesian updating procedures are first applied to the calibration of the complex Finite Element (FE) model of the fortress with respect to experimental modal data. The uncertainty of the identified parameters of structural system is then obtained by using the Bayesian probabilistic approach. The most probable parameter vector is obtained by maximizing the posterior probability density function. The robustness and the efficiency of the procedure are evaluated through the comparison with the results obtained from the estimation of the Pareto-optimal solutions.Ponsi, FedericoBassoli, ElisaVincenzi, Loris
Bayesian Model Updating and Parameter Uncertainty Analysis of a Damaged Fortress Through Dynamic Experimental Data
https://orcid.org/http://orcid.org/0000-0002-2808-4707
https://orcid.org/http://orcid.org/0000-0002-4919-1421
https://orcid.org/http://orcid.org/0000-0003-2541-7104
A probabilistic analysis for the uncertainty evaluation of model parameters is of great relevance when dealing with structural damage assessment. Indeed, the identification of the damage severity associated to its uncertainty can support the decision-maker to close a bridge or a building for safety reasons. In this paper the results of the model updating of an historical masonry fortress damaged by the seismic event that hits the town of San Felice sul Panaro and the surrounding localities in the Po Valley in the 2012 are presented. A standard and a Bayesian updating procedures are first applied to the calibration of the complex Finite Element (FE) model of the fortress with respect to experimental modal data. The uncertainty of the identified parameters of structural system is then obtained by using the Bayesian probabilistic approach. The most probable parameter vector is obtained by maximizing the posterior probability density function. The robustness and the efficiency of the procedure are evaluated through the comparison with the results obtained from the estimation of the Pareto-optimal solutions.Ponsi, FedericoBassoli, ElisaVincenzi, Loris
Bayesian Model Updating and Parameter Uncertainty Analysis of a Damaged Fortress Through Dynamic Experimental Data
https://orcid.org/http://orcid.org/0000-0002-2808-4707
https://orcid.org/http://orcid.org/0000-0002-4919-1421
https://orcid.org/http://orcid.org/0000-0003-2541-7104
A probabilistic analysis for the uncertainty evaluation of model parameters is of great relevance when dealing with structural damage assessment. Indeed, the identification of the damage severity associated to its uncertainty can support the decision-maker to close a bridge or a building for safety reasons. In this paper the results of the model updating of an historical masonry fortress damaged by the seismic event that hits the town of San Felice sul Panaro and the surrounding localities in the Po Valley in the 2012 are presented. A standard and a Bayesian updating procedures are first applied to the calibration of the complex Finite Element (FE) model of the fortress with respect to experimental modal data. The uncertainty of the identified parameters of structural system is then obtained by using the Bayesian probabilistic approach. The most probable parameter vector is obtained by maximizing the posterior probability density function. The robustness and the efficiency of the procedure are evaluated through the comparison with the results obtained from the estimation of the Pareto-optimal solutions.Ponsi, FedericoBassoli, ElisaVincenzi, Loris
Bayesian Model Updating and Parameter Uncertainty Analysis of a Damaged Fortress Through Dynamic Experimental Data
Lecture Notes in Civil Engineering
Rainieri, Carlo (editor) / Fabbrocino, Giovanni (editor) / Caterino, Nicola (editor) / Ceroni, Francesca (editor) / Notarangelo, Matilde A. (editor) / Ponsi, Federico (author) / Bassoli, Elisa (author) / Vincenzi, Loris (author)
International Workshop on Civil Structural Health Monitoring ; 2021 ; Naples, Italy
2021-08-25
19 pages
Article/Chapter (Book)
Electronic Resource
English
Model uncertainty and Bayesian updating in reliability-based inspection
| Online Contents | 2000
Model uncertainty and Bayesian updating in reliability-based inspection
| British Library Online Contents | 2000