Non-parametric stochastic subset optimization for reliability-based importance ranking of bridges in transportation networks

Abstract

• Proposes a non-parametric stochastic subset optimization algorithm for reliability-based importance ranking of bridges. • Proposes iterative approach with adaptive stochastic sampling to improve efficiency of algorithm. • Number of iterations only grows logarithmically with respect to the total number of bridges in the transportation network. • Algorithm is highly efficient and especially useful for large-scale networks with many bridges. Establishing reliability-based importance ranking of bridges in a transportation network usually entails significant computational efforts, especially for large-scale networks. This paper proposes a non-parametric stochastic subset optimization (NP-SSO) algorithm to efficiently identify the reliability-based importance ranking of bridges. It first generates failure samples from an augmented failure distribution and then directly establishes the importance ranking by comparing the number of failure samples for each bridge. To improve the efficiency, an iterative NP-SSO algorithm is established by restricting the search space to the subset of bridges identified in the previous iteration. For each iteration, the modified accept-reject algorithm with adaptive kernel sampling density (AKSD) is adopted to efficiently generate failure samples. The proposed NP-SSO algorithm is highly efficient for identification of important bridges, and the number of iterations only grows logarithmically with respect to the number of bridges, and NP-SSO is especially useful for large-scale networks with many bridges. The effectiveness and efficiency of NP-SSO are demonstrated through identifying the importance ranking of bridges in the transportation network of Los Angeles and Orange counties.