Bayesian network-based vulnerability assessment of a large-scale bridge network using improved ORDER-II-Dijkstra algorithm

Abstract

Vulnerability analysis has become a recent concern for bridge administration authorities. This paper presents a Bayesian network-based vulnerability evaluation methodology for a large-scale national highway (NH) bridge network, using the improved ORDER-II-Dijkstra algorithm. The NH bridge network consists of 1772 bridges, and includes their spatial locations, types, completion years, and assessment states. The network vulnerability was defined by combining edge failure and its influence on network connectivity. Using the equivalent bridge concept for simplicity, the edge failure probability was determined by a series system composed of all bridges in the same edge, where the failure probability for one bridge could be calculated by incorporating its assessment state and design load. The large-scale bridge network connectivity probability was approximately evaluated using the Bayesian network. To avoid the NP-hard problem, the ORDER-II algorithm evaluated the most probable state combinations of equivalent bridges, by adding or deleting ordered state combinations to the minimum heap. The improved Dijkstra’s algorithm was chosen to determine the network connectivity under each state combination by seeking the shortest paths between node pairs. The application of vulnerability to the bridge network illustrates the effectiveness and accuracy of this method, and can provide guidance for decision-making.

Keywords:

• Assessment
• Bayesian networks
• bridges
• network analysis
• probabilistic method
• vulnerability
• bridge failure

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

Financial support for this study was provided by National Key R&D Program of China [2018YFB1600200], NSFC [51922034, 51678204, 51638007], Heilongjiang Natural Science Foundation for Excellent Young Scholars [YQ2019E025] and Guangxi Science Base and Talent Program [710281886032].