Data‐driven distributionally robust optimization of railway alignments in earthquake‐prone regions considering active fault zone risks

Railway alignment design in earthquake-prone regions faces many challenges, among which an active fault zone threat is a dominant factor. However, slight attention has been devoted in this field to the complex fault zone risks affecting alignment optimization (AO). To this end, the first-known AO model that estimates active fault zone risks is proposed according to the distributionally robust optimization (DRO) theory. In this model, a data-driven minimax DRO function is formulated to compute the uncertain fault zone risks while optimizing railway alignments. In addition, a degree-of-regret (DoR) chance constraint is developed to trade off solution quality and search conservatism during optimization. To solve this DRO model, a particle swarm algorithm is improved in two ways. First, a Monte Carlo simulation is customized based on several alignment refinement analyses to assess possible railway losses due to uncertain fault zone damages. Afterward, a solution selection operator is devised to determine the best alignment alternatives while tackling the DoR constraint. Ultimately, the proposed DRO model and algorithm are applied to a real-world railway example. Their effectiveness is verified through two sensitivity analyses and by being compared with the best solution found by human designers.