Bayesian Network Enhanced with Structural Reliability Methods: Methodology

TL;DR

This paper introduces an enhanced Bayesian network framework that integrates structural reliability methods to improve probabilistic risk analysis of complex engineering systems, especially for rare events and evolving information.

Contribution

It presents a novel combined methodology, eBN, unifying Bayesian networks and SRMs for efficient reliability assessment of infrastructure and structural systems.

Findings

eBN effectively models complex dependence structures.

The framework enables efficient rare event probability computation.

Strategies for analysis are demonstrated through conceptual examples.

Abstract

We combine Bayesian networks (BNs) and structural reliability methods (SRMs) to create a new computational framework, termed enhanced Bayesian network (eBN), for reliability and risk analysis of engineering structures and infrastructure. BNs are efficient in representing and evaluating complex probabilistic dependence structures, as present in infrastructure and structural systems, and they facilitate Bayesian updating of the model when new information becomes available. On the other hand, SRMs enable accurate assessment of probabilities of rare events represented by computationally demanding, physically-based models. By combining the two methods, the eBN framework provides a unified and powerful tool for efficiently computing probabilities of rare events in complex structural and infrastructure systems in which information evolves in time. Strategies for modeling and efficiently analyzing the eBN are described by way of several conceptual examples. The companion paper applies the eBN methodology to example structural and infrastructure systems.