Infrastructures have become important for the holistic growth of any country. With the number of such projects being constantly on the rise each year, its efficient maintenance has become a primary requirement for any asset owner. Among the many techniques available, FMECA is widely used for maintenance planning. In this method, different critical components are identified based on their likelihood of failure and impact on the infrastructure. Based on the inferences obtained, maintenance plans are developed where interventions are prioritized based on a component’s criticality within the system. FMECA uses risk matrices to assess criticalities semi-quantitatively. This lacks the required quantifiable information of a component in a system. A component by design can either be individual or exist as a subsystem comprising several components. To analyze criticalities accurately, quantitative information that can provide insights on a component’s state in the system is required. Hence, this thesis develops a new approach that could eliminate subjective errors in conventional criticality analysis. The new and conventional approach was applied to two infrastructures to observe and discuss the variations in results obtained. The comparative study showcased that the new approach provided better quantitative reasonings for criticalities obtained for each component. Using the unique implementation of the obtained quantitative data as done in the current research, developing realistic and accurate maintenance strategies is possible.