Resilience-informed investment planning for earthquake-prone urban transportation systems: balancing pre-disaster reinforcement and post-disaster recovery

In the face of increasing seismic risks, enhancing the resilience of transportation infrastructure systems has become a critical priority for urban policy and planning. This study proposes a resilience-based decision-support framework that integrates pre-disaster reinforcement and post-disaster recovery into a unified optimisation model for transportation systems under seismic uncertainty. A bi-objective stochastic optimisation model is developed to minimise the expected total economic cost, which includes both pre-earthquake reinforcement costs and post-earthquake recovery losses, comprising direct repair costs and indirect economic disruptions. To improve computational efficiency, a surrogate model based on an artificial neural network (ANN) is constructed to approximate functionality metrics derived from traffic-flow simulations. A case study from a Chinese city illustrates the model’s effectiveness in informing cost-efficient reinforcement strategies for building a resilient transportation system. Results indicate that reasonable pre-disaster investments can notably improve system resilience and mitigate post-disaster impacts. This study offers critical insights for policymakers on cost-effective infrastructure reinforcement strategies and underscores the importance of integrated planning across the disaster management cycle.