Over the decades, railway maintenance activities have primarily relied on historical experiences and periodic inspections. But assessing the prior information using simulation-based predictions on types and extent of damages from different traffic types and under their growing conditions (increased traffic, axle loads, etc.), can play a significant role in predictive maintenance for railways. Given this, the present study contributes to the predictive maintenance of railway infrastructure by evaluating rail surface damages caused by different types of rail vehicles on the Swedish iron ore line. Wear rates and rolling contact fatigue (RCF) on rails in tight curves have been assessed using multibody simulations. The simulation results indicate that other rail vehicles contribute to higher rail wear rate and a greater risk of RCF compared to the predominant iron ore vehicles, in the considered line. Even considering their share of the total traffic volume, their damage contribution can be significant. Adopting the same wheel profile used by iron ore wagons for other vehicles could potentially reduce rail wear and RCF risk.