Sensitivity analysis of road tunnel resilience through data-driven stochastic simulation

Abstract

Abstract A probabilistic resilience model for tunnels exposed to disruptive events is vital to understanding and estimating the functionality loss and its recovery time due to these events. Performing sensitivity analysis will help to identify the critical parameters contributing to tunnel resilience. This paper aims to identify tunnel resilience’s sensitivity for parameters such as traffic volume, fire suppression systems, changes in maintenance, and operation parameters using a simulation model that estimates overall tunnel resilience for a given period. Overall universal compatibility of the simulation model is checked for twenty-two tunnels using information from the US National Tunnel Inventory (NTI), and resilience correlation is established. The results show that resilience loss due to fire and accidents are directly correlated with traffic volume. A significant reduction in the loss due to fire can be found by installing a fire suppression system. Increasing the service life of equipment and frequency of inspection and repair contributes to an increase in a tunnel’s resilience index. A resilience correlation study for the twenty-two tunnels showed that an average resilience index for these tunnels is 96.57%. Linear correlations can be made between tunnel length and traffic loss due to fire and operation. Accidents and fire events are correlated with average traffic in the tunnel. Tunnel speed limit, age, number of lanes, and bores do not show a considerable effect on disruptive events. Overall, the study shows that the proposed simulation model can encompass various disruptive events to estimate the resilience of the tunnel.