A Bayesian belief network approach to bridge infrastructure resilience assessment against seismic hazard

Like other infrastructure, bridges are seriously affected by natural hazards like earthquakes, floods, and hurricanes, significantly affecting communities, transportation networks, and economic development. Hence, it is essential to assess the resilience of the bridge infrastructure. This study introduces the Bayesian Belief Network (BBN) model as a strategy for assessing the seismic resilience of bridges. The BBN model is developed based on the existing literature, multiple expert opinions, and the Bayesian network approach. This method minimizes the need for a large amount of historical data. The BBN model is credible in effectively addressing complex relationships among the parameters and uncertainties associated with seismic resilience through conditional probability tables (CPTs). Enhancing the study’s analytical integrity involves conducting sensitivity, scenario, and extreme condition tests, as well as applying the model to two bridge examples. The outcome of the model analysis provides a more accurate evaluation of the bridge and improves the evaluation of bridge seismic resilience. This resilience assessment of bridge infrastructure aids policymakers, engineers, and stakeholders in constructing enduring transportation networks.