Ricerc@Sapienza

This paper presents an approach for the rapid seismic loss assessment of infrastructure systems, where all probabilistic variables are modeled through a Bayesian Network (BN). While BN-based approaches have been introduced as promising tools for the risk assessment of systems, they suffer from computational issues (i.e., combinatorial explosion) that prevent their application to large real-world networks that require accurate and

This paper proposes to learn an approximate Bayesian Network (BN) model from Monte-Carlo simulations of an infrastructure system exposed to seismic hazard. Exploiting preliminary physical simulations has the twofold benefit of building a drastically simplified BN and of predicting complex system performance metrics. While the approximate BN cannot yield exact probabilities for predictive analyses, its use in backward analyses based on evidenced variables yields promising results as a decision support tool for post-earthquake rapid response.