Approximate Bayesian Network Formulation for the Rapid Loss Assessment of Real-World Infrastructure Systems
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.