This paper proposes a hybrid approach for the Bayesian modelling of the performance of a real-world road network subjected to seismic hazard, based on preliminary Monte-Carlo simulations. While several computational bottlenecks are limiting the application of Bayesian Networks to simplified infrastructure systems, Monte-Carlo simulations are used here for the identification of the most common and typical damage configurations, in order to design a simplified Bayesian Network formulation, i.e. the thrifty-naïve formulation. The practical appeal of this approach is demonstrated on a complex road network in the French Pyrenees, where the corresponding Bayesian Network has the ability to predict various system performance indicators and to update predictions based on field observations.
Robustness of a hybrid simulation-based/Bayesian approach for the risk assessment of a real-world road network / Gehl, Pierre; Cavalieri, Francesco; Franchin, Paolo; Negulescu, Caterina. - STAMPA. - (2017), pp. 2848-2857. (Intervento presentato al convegno 12th International Conference on Structural Safety & Reliability tenutosi a Vienna, Austria nel 6-10 Agosto 2017).
Robustness of a hybrid simulation-based/Bayesian approach for the risk assessment of a real-world road network
CAVALIERI, FRANCESCO;FRANCHIN, Paolo;
2017
Abstract
This paper proposes a hybrid approach for the Bayesian modelling of the performance of a real-world road network subjected to seismic hazard, based on preliminary Monte-Carlo simulations. While several computational bottlenecks are limiting the application of Bayesian Networks to simplified infrastructure systems, Monte-Carlo simulations are used here for the identification of the most common and typical damage configurations, in order to design a simplified Bayesian Network formulation, i.e. the thrifty-naïve formulation. The practical appeal of this approach is demonstrated on a complex road network in the French Pyrenees, where the corresponding Bayesian Network has the ability to predict various system performance indicators and to update predictions based on field observations.File | Dimensione | Formato | |
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