Railway corridors are often found in regions with modest grades and grade changes, such as riverbanks and coastal areas, making them prone to storm-induced interruptions and damages. Such incidents include inundation of track structure that causes operational restrictions, damages to rail tracks due to scour in the embankment or ballast, and damages incurred in rail bridges due to erosion of abutments, scour in piers, or uplift of ballast decks. Due to delays in passenger and freight transport and repair costs, these incidents cause significant direct and indirect socioeconomic losses. One of the coastal regions that have been historically impacted by storm events is the Gulf coast. Between 1980 and 2018, more than 50 weather related hazard events passed within the region. The Gulf coast has one of the largest rail systems in the country where six of seven major class I railroads operate. Historically, this railway system has suffered many incidents of damage and failure due to storm hazards. For example, CSX Transportation was significantly affected by Hurricane Katrina (2005), where two thirds of the railroad between Mobile and New Orleans were damaged. Considering the importance of the rail system in the freight and passenger transport and the direct and indirect costs that failure in rail system incurs, there is a major need to devise mitigation solutions to improve the performance of coastal rail system. In this project, we will build upon Michigan Tech’s previous project with FRA to perform a life cycle cost analysis to estimate the viability of various mitigation strategies such as elevating rail tracks and relocation of railroads away from the shoreline. In the previous work, we developed a framework that can account for uncertainties in the frequency and severity of tropical cyclones and hurricanes as well as rail components’ performance. We particularly used Synthetic storms that are generated for 10,000 years to develop a probabilistic wind model for the northern Gulf region. The synthetic storms were obtained from Bloemendaal et al (2020). The wind model was adjusted based on the historical data obtained from Hurdat2 dataset (NOAA, 2024) to match the return period and CDF of storms in terms of maximum wind speed. The synthetic storms were then used within ADCIRC-SWAN hydrodynamic modeling platform to estimate surge and wave. Subsequently, the simulated storms surge and wave for CN tracks will be merged with fragility models for rail tracks and bridges to estimate failures (Tsubaki et al., 2016; Ataei and Padgett, 2013). We plan to obtain recovery and direct and indirect costs from CN through a research agreement. These models will be merged with our physical failure estimates to determine life cycle costs. In this study, the life cycle cost analysis will be handled via a Sequential Monte Carlo simulation. In this analysis, we will use the return period of storms within an exponential distribution, to sample time for the next storms. For each storm, we will use the CDF of wind speeds to sample storms within the synthetic dataset. So basically, the failures will be estimated by sampling a large number of storms for an extended period and then estimating the costs for each scenario event. This analysis will investigate the effectiveness of various mitigation strategies, such as elevating tracks and bridges, and shifting them away from the shoreline and could be potentially used in future in an optimization framework to maximize benefits vs costs.