Abstract Managing bridges during and after catastrophic events is a challenging task, requiring a balance between the safety of users and minimizing functional disruptions. Scour around bridge foundations is the leading cause of collapses in watercourse‐spanning bridges. Currently, the most common method for scour monitoring is visual inspection but it is labor‐intensive, inefficient, and unreliable. In response to that, Structural Health Monitoring (SHM) systems have gained interest in recent years. However, for large infrastructure networks, equipping all bridges with sensors is economically unfeasible, limiting sensor use to critical structures. This study implements a probabilistic framework for scour assessment in a bridge network using Bayesian Networks (BNs). This framework employs data from installed scour monitoring systems at key bridge locations to infer scour depths at unmonitored piers. It then enhances decision‐making by integrating BN‐derived scour data with analyses of both direct and indirect costs linked to various management plans. Finally, the proposed risk‐based framework is applied to a case study involving a network of bridges spanning a same river.
Fadaei et al. (Mon,) studied this question.